DE102015011213A1 - Method for analyzing the interaction of high frequency electromagnetic emissions with vegetative regulatory mechanisms of a test subject - Google Patents

Abstract

A method for analyzing the interaction of radio frequency electromagnetic emissions (HFI) with vegetative regulatory mechanisms of a test subject, comprising the steps of: determining the course of power density of HFI over a defined period of time, determining the variance of the time interval of electrocardiogram (ECG) sensing , sequential heartbeats and the course of determined therefrom vegetative function parameters (HRV-FP), synchronization and calibration of the HFI and HRV-FP curves to a reference time interval (T1-n), comparison with characteristic, HFI-induced secondary significances, which have been determined from the current experimental setup or from at least one of these preceding experimental setups with the current test subject or third test subjects and in the form of digital or analog data, preferably in the form of graphical histories, alphanumeric or algorithmic data be kept on a storage device; upon detection of a defined, at least partially present agreement of the currently ascertained HRV-FP course with characteristic, HFI-induced secondary significances or upon correlation with primary significances in the HFI course, a positive assessment of the presence of a human biologically relevant influencing of the vegetative regulatory mechanisms of the test subject takes place ,

Description

The invention relates to a method for analyzing the interaction of high-frequency electromagnetic emissions (HFI) with vegetative regulatory mechanisms of a test subject according to claims 1 and 2 and to corresponding arrangements for carrying out the method according to claims 29 and 30.

Wireless information technologies using technically generated electromagnetic fields have been widely used in recent years. Electromagnetic fields (EMF) emitted by radio frequency sources serve as carrier waves onto which respective information signals are modulated. Common transmission standards are about GSM, UMTS, LTE, WLAN, Bluetooth / IEE 802, DECT u. a.

If an electromagnetic wave strikes an absorbing body, then the energy contained in it is converted into another state or into heat. This thermal mode of action is also based on the limits set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP 1998) for the protection of the general population from EMF exposure. Their recommendations were made by the EU (Council Recommendation 1999) and the ÖVE / ÖNORM E 8850 accepted.

In addition to the aforementioned thermal effects, athermal effects of radiofrequency electromagnetic fields, which correlate with adverse health effects, have also been demonstrated in a large number of scientific studies ( Santini 2002 . Navarro et al. 2003 . Hutter et al. 2006 . Abdel-Rassoul et al 2007 . Blettner et al. 2008 ). For example, a study published in 2009 at the Institute for Environmental Hygiene at the Medical University of Vienna indicates that radiation radiation densities of 0.5 mW / m2 and above (see the according to ÖNORM permissible, thermal limit values of 4,500-10,000 mW / m2) adverse human biological effects such. B. an increased immunosuppression are causally detectable ( Hutter, Kundi, 2009 ). As a precautionary measure, the Salzburg Medical Council even recommends an electromagnetic radiation flux density of 0.001 mW / m2.

Despite empirical evidence of athermal EMF effects, their mode of action with respect to the biological organism has not yet been conclusively clarified. As a plausible mechanism of action on intra- and intercellular level, for example, the formation of free radicals or oxidative and nitrosative stress is discussed ( Friedmann et al. 2007 . Simko 2007 . Pall 2007 . Bedard and Krause 2007 . Pacher et al. 2007 . Desai et al. 2009 ).

Since the development of high-frequency technology can only look back on a relatively recent history, there are no long-term studies in this area per se. Until appropriate findings are obtained from such field studies, many specialist committees and environmental physicians recommend a possible reduction of EMF exposure for precautionary reasons. For example, following an in-depth review of all currently available pertinent studies, the WHO International Cancer Research Agency IARC noted in June 2011 the need to reduce high-frequency EMF and classified it as "potentially carcinogenic to humans" (Group 2B).

The precautionary idea has already been taken into account. For example, by the Swiss Federal Council with a "Non-ionizing Radiation Protection Ordinance (NISEV)" for places of sensitive use ("OMEN") such as living spaces, schools, hospitals and retirement homes, with greatly reduced EMF plant limit values compared to regular operation. EMF-related complaints are recognized as physical restriction in some countries, such as the UK and Sweden, under the name Electrohypersensitivity (EHS), along with the statutory rights of EHS-affected workers to reduce exposure.

In the course of occupational health care, an increasing number of companies decide to voluntarily reduce electromagnetic pollution. For example, the Bavarian automotive group BMW has restricted the permissible high-frequency electrosmog exposure at the workplaces of its approximately 105,000 employees worldwide to an in-house maximum of 100 μW / m2, commonly known as the "BMW limit value". In order to comply with this limit, physical measures have been taken in office and laboratory buildings to reduce exposure, eg. B. DECT and WLAN base stations shielded with metal-coated glass and provided wireless router with attenuators. Companies that carry out such preventive care programs document a lower number of sick leave and greater job satisfaction among their employees.

The identification of EMF-related complaints is in practice difficult and provides environmental physicians before extensive differential diagnostic requirements, such as in the "Guideline for the clarification and treatment of EMF-related complaints and diseases" of the Austrian Medical Association of 3 March 2012 are set out. While individual people show no direct symptoms due to their individual ability to regulate, others react significantly even with relatively low EMF exposure. In any case, if EMF sensitivity is present, exposure reduction is indicated.

Although previous studies on the influence of high-frequency EMF on EEG or ECG or heart rate values of a subject have revealed correlations of such exposures with organic reactions, however, these relationships proved to be unclear or not reproducible. It has already been demonstrated in individual subjects that the range of variation in heart rate variability (HRV) decreases or the frequency band of HRV under EMF exposure is narrower, using as reference value an HRV control measurement before EMF exposure for comparison. Such a decrease in the range of HRV variation also occurs in the presence of any other on the subjects acting stressors and is the focus of numerous sports science and health prophylactic investigations (a high HRV is generally regarded as indication for a healthy physiological regulatory capacity, however, indicates a low HRV on health Limitations of the cardiovascular system as well as the general organic constitution). Also, changes in the number of harmonics of HRV baseline under EMF influence have been observed, which, in combination with a measurement of capillary bed microcirculation by laser Doppler technique and the electrical potential distribution on the skin surface of the subject, indicate EMF sensitivity (e.g. B. Tüngler, A., Von Klitzing, L., 2013: Hypothesis on how to measure electromagnetic hypersensitivity, Electromagnetic Biology and Medicine 32: 281-290 .).

However, increasing or decreasing brain or cardiac function over a test period, in terms of absolute or mean values, does not yet provide any means of demonstrating an obvious link between high-frequency EMF and such organic functional deviations. So far no reliable criteria could be found, which indicate such an effect context. The mentioned methods for EEG or ECG / HRV-based analysis of vegetative regulatory dynamics under EMF influence are subject to many subjective influencing factors on the part of the test subject and therefore do not provide reliable information on the presence or absence of EMF sensitivity.

The object of the present invention is to individually determine the indication of an EMF exposure reduction, in that human biologically relevant interactions of high-frequency EMF with the human organism can be detected in a rapid and meaningful manner. The connection between specific high-frequency EMF immission profiles and vegetative regulatory disorders should be evidently detected. Costly differential diagnostic measures and laboratory analyzes to determine EMF sensitivity are to be reduced. A corresponding test and measurement arrangement should be able to be carried out in particular by untrained persons in a location-independent and uncomplicated manner.

Since in the present method a reference to a test subject or its location, that is to say immission-side viewing of high-frequency electromagnetic fields always takes place, these are referred to below as high-frequency immissions (HFI). However, since HFI or high-frequency EMF always emanate from a technical emission source, one could also speak of high-frequency emissions (which, however, decrease with increasing distance to the emission source).

The above objects are achieved by a method having the characterizing features of claims 1 and 2.

If, in the following, a course of the high-frequency immissions (HFI) or of the heart rate variability functional parameters (HRV-FP) is mentioned, then the respective height, or intensity, of one of the HFI, HRV-FP is considered over a defined time course Value meant. These values are measurement and / or calculation quantities which are based on measurement and calculation methods, which are determined by standardized measuring devices or processor devices for the determination of HFI as well as further ECG-based measuring devices or processor devices for the determination of the HRV-FP according to are available in the state of the art (see, for example, DE 603 06 856 T2 . DE 10 2006 039 957 B4 ).

The heart rate variability determination used in the course of the analysis method according to the invention is a recognized medical-technical method for evaluating the physiological condition of a test subject. In the field of HRV already exists an extensive state of the art or a variety of patent applications for specific applications and modified procedures. The DE 102009002134 A1 discloses, for example, a method for determining the effects of a magnetic field on a test subject, wherein different HRV (total) values can be detected before and after a change in the magnetic field has been made.

As a rule, the HFI value (in each case related to a particular point in time) denotes the power flux density or an equivalent measured variable for representing the intensity of electromagnetic high-frequency immissions acting on the test subject, which is determined by means of a dosimeter or spectrum analyzer which is substantiated in greater detail below can be detected together with suitable receiving antennas. Here, the unit [power / area] is common, z. B. mW / m ² or μW / m ² . However, high-frequency immission levels can also be specified in alternative units, which in most cases can be converted into one another by simple mathematical operations. For example, in rare cases, data can be found in V / m, A / m or in logarithmic (dB) units.

The HRV-FP values, which are also substantiated in greater detail in the following description, in each case designate power values of vegetatively controlled organ functions of the test subject, such as, for example, Eg heart and respiratory rhythms, ratio of activation sympathetic / parasympathetic, respiratory sinus arththmia, SDNN _RR . Regardless of their organ or rule-specific statement about the organism of the test subject and their specific, on specific body rhythms related unit, the parameters mentioned are based on an analysis of the variance of the time interval of determined by electrocardiogram measurement (ECG), successive heartbeats. In principle, all parameters known as "time domain measures" or "frequency domain measures" can be used as HRV-FP, which are calculated in a standardized manner from RR interval sequence data provided by an ECG measuring device.

The measurement of the heartbeats and ECG determination takes place in a manner known per se by means of pulse probes applied to the test subject. An objective analysis of the HRV is carried out today according to standardized mathematical methods, wherein measurement data of the heart rate (fundamental frequency) are transmitted, for example by means of Fourier transformation of the time domain in the frequency domain and can be displayed as a power spectrum. The representation of this power spectrum is referred to as a spectrogram or AutoChrone image. Based on the data contained therein, according to the prior art, various other frequencies present in the heart rate can be visualized that have been empirically assigned to other body rhythms (eg, breath, blood pressure, blood flow, sympathetic, parasympathetic).

The HFI and HRV-FP curves can be represented as graphs in a two-dimensional coordinate system, but they can also be described purely by mathematical functions, algorithms and processor-usable data sets. H. be resolved alphanumerically. Regardless of their representation or graphic visualization, the HFI and HRV-FP values are each a function of time, i. H. Each time, which can preferably be represented on an abscissa of a diagram, is assigned a value that can preferably be represented on an ordinate of the diagram. Of course, the assignment of abscissa and ordinate can also be done in the reverse manner, d. H. the time course can also be shown in the vertical direction or in the form of a third spatial axis of a three-dimensional diagram. The time grid for recording and / or displaying the HFI, HRV-FP values can be chosen as desired in accordance with respective analysis requirements and processor capacities; it can, for. From a few milliseconds [ms] to several seconds [s].

The two-dimensional diagram form for the representation of the designated courses can also be supplemented by a third dimension in order to provide additional information about respective measurement or calculation quantities, which are shown in the graphical representation, for example, in FIG. B. by an appropriate color, shading or hatching to express. In any case, preference is given to a classic diagram representation with horizontal X-axis as time axis and vertical Y-axis for mapping the HFI, HRV-FP values. The HFI, HRV-FP profiles resulting from the HFI, HRV-FP values determined in a reference period are thus preferably subjected to a further evaluation as a function graph.

It should be noted that the indicated courses for carrying out the method according to the invention do not necessarily have to be represented in diagram form or in the form of a graph, but also in a purely computational manner analyzable and comparable. The result of an assessment of the presence of human biological relevant influence of the test subject could thus also in purely alphanumeric form or by another, for. B. visualization by colors and / or symbols.

• – Ermitteln des Verlaufs einer vorzugsweise als Leistungsflussdichte gemessenen Intensität von Hochfrequenz-Immissionen (HFI) in Bezug auf das Testsubjekt über einen definierten Zeitraum
• – Ermitteln der Varianz des zeitlichen Abstands von mittels Elektrokardiogramm-Messverfahren (EKG) erfassten, aufeinanderfolgenden Herzschlägen sowie des Verlaufs daraus ermittelter, mit der Herzratenvariabilität (HRV) assoziierter vegetativer Funktionsparameter (HRV-FP) des Testsubjekts über einen definierten Zeitraum (Aus dem Stand der Technik sind mehrere, jeweiligen Organbezirken zugeordnete vegetative Funktionsparameter bzw. Verlaufsdiagramme bekannt, welche aus einer gegenständlichen Erfassung der Herzschlagintervalle ableitbar sind; gemäß standardisierter Auswertesoftware der HRV-Technik ist es möglich, aus einer Frequenzanalyse der zeitlichen Varianz aufeinanderfolgender Herzschläge weitere in der Herzfrequenz vorhandene organische Frequenzen zu ermitteln. Diese mittels des mathematischen Verfahrens der Fouriertransformation aus der Herzfrequenz errechneten Frequenzen wurden auf empirische Weise spezifischen Körperrhythmen bzw. vegetativen Funktionen wie Sympathikus, Parasympathikus, Blutdruck, Atem etc. zugeordnet. Die ermittelten HRV-FP-Verläufe können wahlweise in Form eines grafischen Verlaufs, alphanumerischer oder algorithmischer Daten, Funktionen oder Funktionsgraphen dargestellt bzw. verarbeitet werden und bei Bedarf, z. B. zwecks grafischer Visualisierung des Analyseergebnisses, in eine jeweils alternative Darstellungsart umgewandelt werden.) Aus der HRV ermittelte Frequenzbereiche bzw. HRV-assoziierte Funktionsparameter entsprechen jeweils spezifischen vegetativen Funktion wie z. B. Blutdruck, Durchblutung, Atemtätigkeit (-insb. -tiefe und -frequenz), Sympathikus, Parasympathikus, Thermoregulation, sonstige Organtätigkeiten; die Zuordnung von aus der HRV entnehmbaren Frequenzen zu jeweiligen vegetativen Funktionen des menschlichen Organismus wurde auf empirische Weise ermittelt und ist in Abhandlungen über Herzraten bzw. HRV-Ermittlung gemäß dem Stand der Technik in detaillierter Weise dokumentiert.
• – Die HFI- und die HRV-FP-Verläufe werden zumindest abschnittsweise synchronisiert und auf mindestens ein gemeinsames Referenz-Zeitintervall kalibriert (es können auch mehrere, hinsichtlich der Chronologie jeweils deckungsgleiche Referenz-Zeitintervalle bzw. Sub-Zeitintervalle miteinander verglichen werden; die gegenständliche Synchronisation und Kalibrierung kann entweder im Nachhinein bzw. nach erfolgtem Datenexport jeweils eingesetzter HRV- und HFI-Analyseeinrichtungen oder auch in Echtzeit bzw. mittels einer datentechnischen Verbindung der HRV- und der HFI-Analyseeinrichtungen erfolgen.)
• – Durchführen einer Analyse des HFI-Verlaufs, in welcher primäre Signifikanzen, i. e. temporäre, d. h. innerhalb des Referenz-Zeitintervalls zeitlich begrenzte Abweichungen des HFI-Verlaufs von definierten statischen oder dynamischen Mittelwerten oder HFI-Referenzdaten, insbesondere signifikante Anstiege und/oder Absenkungen des HFI-Verlaufs gegenüber zeitlich vorangehenden und/oder nachfolgenden Abschnitten/Phasen des HFI-Verlaufs detektiert werden (Als signifikant wird hierbei jede über die messtechnisch gegebene, reguläre Oszillationsbreite an HFI-Messwerten hinausgehende Abweichung angesehen. Als HFI-Referenzdaten bzw. Regelverläufe können entweder Mittelwerte aus am Standort des Testsubjekts messtechnisch ermittelten Hochfrequenz-Intensitäten bzw. Leistungsflussdichten herangezogen werden oder in umweltmedizinischen Empfehlungen wie etwa den Vorsorgerichtwerten der Salzburger Ärztekammer oder den „SBM-Standards der baubiologischen Messtechnik” definierte Hochfrequenz-Intensitäten bzw. Leistungsflussdichten.)
• – Durchführen einer Analyse des HRV-FP-Verlaufs, in welcher HFI-induzierte sekundäre Signifikanzen, i. e. temporäre, d. h. innerhalb des Referenz-Zeitintervalls zeitlich begrenzte Abweichungen des HRV-FP-Verlaufs von HRV-FP-Referenzdaten bzw. Regelverläufen, welche aus empirischem Datenbestand geschöpft und/oder dynamisch generiert, d. h. aus aktuellen Mess-/Berechnungsdaten bzw. HRV-FP-Verläufen abgeleitet bzw. extrapoliert sind, detektiert werden, insbesondere signifikante Anstiege und/oder Absenkungen des HRV-FP-Verlaufs gegenüber zeitlich vorangehenden und/oder nachfolgenden Abschnitten/Phasen des HRV-FP–Verlaufs. Als signifikant wird hierbei wiederum jede über die messtechnisch gegebene, reguläre Oszillationsbreite an HRV-FP-Mess- bzw. Berechungswerten hinausgehende Abweichung angesehen. [Anmerkung: die zu detektierenden Abweichungen bzw. Signifikanzen innerhalb des Referenz-Zeitintervalls dauern jedenfalls kürzer an als das gesamte betrachtete Referenz-Zeitintervall d. h. sind als begrenztes zeitliches Ereignis bzw. als anschwelllendes und/oder wieder abschwellendes Phänomen detektierbar. Im Gegensatz zu bekannten Biofeedback-Verfahren wird somit die Ereignishistorie der jeweiligen HRV-FP-Verläufe analysiert. Im Spezialfall eines unten noch näher beschriebenen Abschirmversuchs, könnte es sich als ausreichend erweisen, lediglich ein Anschwellen oder ein Abschwellen einer sekundären Signifikanz in einem bestimmten Zeitintervall oder zu einem bestimmten Zeitpunkt als hinreichendes Kriterium für eine Analysebewertung zu detektieren; selbiges gilt auch für die Detektionsfähigkeit der primären Signifikanzen bzw. temporären Abweichungen des HFI-Verlaufs]

According to claim 1, the following method steps are provided:

• Determining the course of an intensity of high-frequency immissions (HFI), measured as power flux density, with respect to the test subject over a defined period of time
• Determination of the variance of the time interval of successive heartbeats recorded by means of electrocardiogram measurement (ECG) and the course of their determined, associated with the heart rate variability (HRV) vegetative function parameters (HRV-FP) of the test subject over a defined period (from the prior Technique are several, each organ districts associated vegetative function parameters or history diagrams known, which are derivable from a physical detection of heartbeat intervals, according to standardized evaluation software of the HRV technique, it is possible from a frequency analysis of the temporal variance of successive heartbeats other existing in the heart rate organic frequencies These frequencies, which were calculated from the heart rate by means of the Fourier transformation mathematical method, were empirically determined by specific body rhythms or vegetative functions, such as the sympathetic, Parasympathetic, blood pressure, respiratory, etc. assigned. The determined HRV-FP profiles can be displayed or processed optionally in the form of a graphic history, alphanumeric or algorithmic data, functions or function graphs and, if necessary, eg. B. for the purpose of graphical visualization of the analysis result, be converted into a respective alternative representation.) From the HRV determined frequency ranges or HRV-associated function parameters each correspond to specific autonomic function such. Eg blood pressure, blood circulation, respiratory activity (-insb. Depth and frequency), sympathetic, parasympathetic, thermoregulation, other organ activities; the assignment of frequencies which can be taken from the HRV to respective vegetative functions of the human organism has been determined empirically and is documented in detail in treatises on heart rates or HRV determination according to the prior art.
• The HFI and the HRV-FP profiles are synchronized at least in sections and calibrated to at least one common reference time interval (it is also possible to compare several reference time intervals or sub-time intervals which coincide with respect to the chronology, the objective synchronization and calibration can be carried out either in retrospect or after the data has been exported in each case inserted HRV and HFI analysis facilities or in real time or by means of a data connection of the HRV and HFI analysis facilities.)
• Performing an analysis of the HFI curve, in which primary significances, ie temporary, ie within the reference time interval, time-limited deviations of the HFI curve from defined static or dynamic mean values or HFI reference data, in particular significant increases and / or decreases in the HFI It is considered to be significant to deviate from the metrologically given regular oscillation range of HFI measured values as HFI reference data or control curves either average values High-frequency intensities or power flux densities determined by measurement at the site of the test subject are used or are defined in environmental medical recommendations such as the precautionary values of the Salzburg Medical Association or the "SBM Standards of Building Biology Measurement Techniques" entities or power flux densities.)
• Performing an analysis of the HRV-FP curve, in which HFI-induced secondary significances, ie temporal, ie within the reference time interval, time-limited deviations of the HRV-FP curve from HRV-FP reference data or control curves derived from empirical Scanned data and / or generated dynamically, ie derived from current measurement / calculation data or HRV-FP processes or extrapolated are detected, in particular significant increases and / or decreases in the HRV-FP history over time and / or following sections / phases of the HRV-FP course. In this case, any deviation exceeding the metrologically given, regular oscillation width at HRV-FP measurement or calculation values is regarded as significant. [Note: the deviations or significances to be detected within the reference time interval in any case last shorter than the entire reference time interval considered, ie are detectable as a limited temporal event or as an increasing and / or decreasing phenomenon. In contrast to known biofeedback methods, the event history of the respective HRV-FP profiles is thus analyzed. In the special case of a shielding attempt described in more detail below, it could be prove to be sufficient to detect only a swelling or waning of a secondary significance in a given time interval or at a certain time as a sufficient criterion for an analysis evaluation; the same also applies to the detection capability of the primary significances or temporary deviations of the HFI curve]

A high detection accuracy of the analysis method according to the invention can in particular in the case of generation of HRV-FP reference data or gradients based on a combination of empirical data or in the past from measurement results on the same test subject or on third test subjects obtained rules using the current test guaranteed by the test subject HRV-FP measurement / calculation data.

HRV-FP and / or HFI reference data can either be drawn from empirical data or generated dynamically, i. H. derived or extrapolated from current measurement / calculation data or HRV-FP and / or HFI gradients. The reference to HRV-FP and HFI control curves can be made by defining a defined fluctuation range or a defined permissible frequency of irregularities of the courses, which are not yet to be evaluated as significances. By depositing such analysis algorithms, misinterpretations can be largely avoided and the analysis result can be optimized.

Upon detection of a defined number of coincident, i. H. In the case of HFV-induced secondary significance in the HRV-FP course, which occurs essentially simultaneously occurring primary significances, there is a positive evaluation of the presence of a human biological relevant influencing of the vegetative regulatory mechanisms of the test subject.

Thus, a clear temporal assignment of (HFI-induced) secondary significances detected in the HRV-FP curve to primary significances detected in the HFI curve takes place. The incidence of HFI-induced secondary significance in the temporal HRV-FP course is causally related to the occurrence of primary significance in the temporal HFI course.

As a consequence, the exposure of the test subject to those HFI sources which have been classified as relevant by the method according to the invention can be reduced. In the simplest case, such an exposure reduction can be done by a change of the sleeping place or by changing from wireless to corded home and communication technology. Exposure reduction is also possible by technical shielding measures on the building substance, eg. B. using grounded plaster / wire mesh structures or carbon fiber coatings, such as those offered by the building materials producer Sto or Rigips especially for HFI reduction.

The detection of the coincident Signifikanzen is preferably automated, but it can also be in a manual manner z. B. on the basis of a comparison of graphical visualizations temporally mutually corresponding HFI and HRV-FP courses done. By means of a separate graphical representation of the correlation of HFI and HRV-FP curves, the relationships can be clearly displayed. The HFI and HRV-FP profiles can hereby be arranged in diagram form one above the other, one above the other or one above the other. In a preferred variant of the method, the graphic output of the HFI and HRV-FP profiles already takes place in such a representation form calibrated with respect to the analysis period or reference time interval. For rapid assignment of significances in the HFI curve to corresponding significances in the HRV-FP curve, the graphic representations of the two curves are arranged exactly one above the other, whereby the graphical representation of the HFI curve can have vertical grid lines which each mark a specific point in time of the analysis period , These grid lines are either aligned with corresponding grid lines which indicate the same point in time in the graphical representation of the HRV-FP curve or run continuously with the latter grid lines. The grid lines are according to a suitable time interval of z. B. 10-30 minutes, preferably equidistant from each other. An optional representation would be if the HFI curve and HRV-FP curves are mapped in different colors and overlapping each other.

• – Ermitteln der Varianz des zeitlichen Abstands von mittels Elektrokardiogramm-Messverfahren (EKG) erfassten, aufeinanderfolgenden Herzschlägen sowie des Verlaufs daraus ermittelter, mit der Herzratenvariabilität (HRV) assoziierter vegetativer Funktionsparameter (HRV-FP) des Testsubjekts über einen definierten Zeitraum,
• – Vergleich des aktuell ermittelten HRV-FP-Verlaufs mit charakteristischen, HFI-induzierten sekundären Signifikanzen oder mit HRV-FP-Referenzdaten bzw. Regelverläufen, welche jeweils aus aktueller Versuchsanordnung oder aus mindestens einer dieser vorausgehenden Versuchsanordnung mit dem aktuellen Testsubjekt oder dritten Testsubjekten ermittelt wurden und nun in Form von digitalen oder analogen Daten, vorzugsweise in Form von grafischen Verläufen, alphanumerischen oder algorithmischen Daten, Funktionen oder Funktionsgraphen auf einer Speichereinrichtung vorgehalten werden; Anm.: Die genannten Darstellungs- bzw. Definitionsarten können wahlweise ineinander umgewandelt werden, z. B. zwecks grafischer Visualisierung in Diagrammform ausgegeben werden. Auch der aktuell am Testsubjekt ermittelte HRV-FP-Verlauf kann wahlweise in Form eines grafischen Verlaufs, alphanumerischer oder algorithmischer Daten, Funktionen oder Funktionsgraphen herangezogen bzw. in eine jeweils alternative Darstellungsart umgewandelt werden. Es erfolgt somit eine Definition charakteristischer, im HRV-FP-Verlauf auftretender (HFI-induzierter) sekundärer Signifikanzen, z. B. in Form charakteristischer Verläufe oder Abweichungen, welche durch primäre Signifikanzen im HFI-Verlauf einer entsprechenden Versuchsanordnung induziert sind. Vorzugsweise werden hierbei sekundäre Signifikanzen bzw. charakteristische Verläufe oder Abweichungen des HRV-FP-Verlaufs herangezogen, welche als im Wesentlichen koinzidente Folge von bei einer entsprechenden Versuchsanordnung im HFI-Verlauf auftretenden primären Signifikanzen erkannt werden. Es können jedoch auch (HFI-induzierte) sekundäre Signifikanzen bzw. charakteristische Verläufe oder Abweichungen herangezogen werden, welche im HRV-FP-Verlauf als zeitverzögerte Folge von im HFI-Verlauf erkannten primären Signifikanzen auftreten, jedoch kausal zuordenbar sind.
• – Bei Detektion einer definierten, zumindest abschnittsweise vorliegenden Übereinstimmung des aktuell ermittelten HRV-FP-Verlaufs mit charakteristischen, HFI-induzierten sekundären Signifikanzen oder bei Abweichung des aktuell ermittelten HRV-FP-Verlaufs von den HRV-FP-Referenzdaten erfolgt eine positive Bewertung des Vorliegens einer humanbiologisch relevanten Beeinflussung der vegetativen Regulationsmechanismen des Testsubjekts.

Alternatively or also in combination for determining an immediate correlation of a trigger from the HFI with HRV-associated autonomic functional parameters (HRV-FP), according to claim 2, the determination of such a correlation can also take place indirectly:

• Determining the variance of the time interval of successive heartbeats recorded by means of electrocardiogram measurement (ECG) and the course of their determined, associated with the heart rate variability (HRV) vegetative function parameters (HRV-FP) of the test subject over a defined period,
• Comparison of the currently ascertained HRV-FP course with characteristic, HFI-induced secondary significances or with HRV-FP reference data or control courses, which were determined in each case from a current experimental setup or from at least one of these preceding test arrangements with the current test subject or third test subjects and now in the form of digital or analog data, preferably in the form of graphical progressions, alphanumeric or algorithmic data, functions or function graphs are kept on a storage device; Note: The mentioned representation or definition types can optionally be converted into each other, eg. B. for the purpose of graphical visualization in diagram form. The HRV-FP profile currently determined on the test subject can also be used in the form of a graphical progression, alphanumeric or algorithmic data, functions or function graphs, or converted into an alternative representation in each case. There is thus a definition of characteristic occurring in the HRV-FP course (HFI-induced) secondary significances, z. B. in the form of characteristic courses or deviations, which are induced by primary significances in the HFI course of a corresponding experimental design. Secondary significances or characteristic courses or deviations of the HRV-FP course are preferably used here, which are recognized as a substantially coincident sequence of primary significances occurring in the HFI course during a corresponding test arrangement. However, it is also possible to use (HFI-induced) secondary significances or characteristic courses or deviations which occur in the HRV-FP course as a time-delayed sequence of primary significances identified in the HFI course, but can be causally assigned.
• - Upon detection of a defined, at least partially present agreement of the currently determined HRV-FP curve with characteristic, HFI-induced secondary significances or deviation of the currently determined HRV-FP curve from the HRV-FP reference data, a positive evaluation of the presence takes place a human biological relevant influence on the vegetative regulatory mechanisms of the test subject.

Note: In this case, each correlation between a significance detected in the event history of the HRV-FP profile and an HFI-typical significance stored on the memory device is detected as a match. The detection criterion of correspondence can also be defined negatively by defining HRV-FP reference data or control profiles and all significant deviations of the currently determined HRV-FP profile (both increases and decreases) from these HRV-FP reference data or control characteristics as anomaly or (HFI-induced) secondary significance. Optionally defined HFI-characteristic correlations may also occur with a time delay to causative HFI loads or primary significances and be recognized as such.

The advantage of this embodiment variant according to the invention lies in the fact that in the course of testing a test subject, only one HRV-FP profile has to be determined from this and no HFI profile, thus no dosimeter, is required. The costs for a corresponding analysis can thereby be reduced, since measuring arrangements or analysis processes with regard to high-frequency immission (HFI) are only required for generating basic analysis algorithms with regard to characteristic correlations with HRV-FP profiles, but not with all the following Analysis or arrangements provided to customers or test subjects, in which only an HRV analysis device including associated storage unit with analysis algorithms deposited thereon is to be provided. Possible fuzziness in the result, in particular of the analysis method simplified in terms of the measuring device arrangement according to claim 2, can be compensated for by the measures mentioned below for increasing the accuracy of the analysis method.

It should further be noted that it is possible to modify the below-mentioned, associated with the heart rate variability (HRV) vegetative function parameters (HRV-FP) by mathematical methods or specific frequency analysis method or transform into other measures and units, without the proposed inventive analysis concept departing.

A determination of characteristic significances, courses or deviations in the HRV-FP course can be carried out on the basis of third reference test subjects in a standardized analysis arrangement and then serves for comparison with HRV-FP courses of subsequently test subjects to be analyzed. The characteristic HRV-FP significances, gradients or deviations can also be determined directly from the analysis Be determined test subject, z. During a trial exposure to one or more HFI sources, described in more detail below.

The characteristic HRV-FP significances, courses or deviations can be stored on a storage device both as a static data record and as a dynamic, continuously expandable data set or data record which can be optimized on the basis of new analysis results. The characteristic HRV-FP significances, courses or deviations or their detection prevalence used for a comparison according to the invention can also be modified as a function of individual parameters of the current test subject. Individual parameters may include factors related to the physical constitution of the test subject (eg individual ability to regulate, medical diagnoses, results of examinations, certificates of the presence of electrosensitivity, environmental medical reports, etc.) as well as the environment of the test subject or with the exposure to high-frequency immissions detected in its living or working environment or at the location of the current measurement arrangement (eg type, number, distance of adjacent HFI sources, frequency ranges, clocking, transmission powers, power flux densities, etc.) By using such individual parameters related to the test subject, an improved analysis result can be achieved, the probability of overlooking a human biologically relevant influence by unrecognized HFI sources or due to individual compensation mechanisms of the test subject decreases.

The characteristic significances, courses or deviations in the HRV-FP course can be stored in the form of digital or analog data, in particular in the form of graphic progressions, alphanumeric or algorithmic data, functions or function graphs on a memory device in data connection with the comparison device.

In other words, an analysis of HRV-FP progressions in each case takes place in the case of the above-described process variants with regard to the presence of section-wise or temporary congruences with defined (HRV-FP and / or HFI) reference characteristics or progressions. The congruence or secondary significance here does not necessarily have to be determined graphically, but can also be detected in a purely computational manner. Congruences to be detected can each be defined by defining permissible fluctuation tolerances and similarity criteria. Optionally, a congruence or secondary significance may also occur with a time delay to a causative, HFI-related event and be detected as such.

In the present embodiments according to 1 - 29 the most significant HRV-based autonomic function parameters (HRV-FP) were used for the analysis according to the invention. It is possible to derive further possibly refined vegetative functional parameters from the HRV basic evaluation or the HRV-FP, which in turn reflect the described significances, without deviating from the idea according to the invention.

According to a first preferred embodiment variant of the analysis method according to the invention, a heart rate variability (HRV) frequency information data record is analyzed as a vegetative function parameter (HRV-FP) (in the case of a graphical visualization, this is also referred to as "spectrogram" or "auto-chrone image"). which in each case represents / reproduces the activation reaching over a defined frequency bandwidth of essentially 0 to 0.5 Hz in each case to a specific frequency range of associated autonomic organ functions of the test subject, in particular:
0.04 to 0.15 Hz: Low Frequency (LF) with correspondence: predominantly sympathetic activity, to a lesser extent also vagus activity, attribution in particular of the blood pressure and circulatory rhythm (Note: The vagus is the largest nerve of the parasympathetic nervous system and in the regulation of the activity of almost all internal Organs, the terms parasympathetic and vagus are used interchangeably below),
0.15 to 0.40 Hz: High Frequency (HF) with correspondence: vagus activity; Particular allocation of respiratory functions, preferably respiratory sinus arrhythmia (RSA), which reflects the modulation of the cardiac rhythm by respiration;
and activation of respective frequency ranges or vegetative organ functions can be taken from the HRV frequency information data set in the form of amplitude strengths preferably visualized by color coding, whereby secondary significance is detected as (HFI-induced) if spontaneous activation of previously not or in negligible intensity of activated frequency ranges or vegetative organ functions via a (each understood as delta) bandwidth of at least 0.05 Hz, preferably of at least 0.1 Hz, more preferably over a bandwidth of more than 0.2 Hz. Activation is understood in the present context in particular when the amplitude strength respective frequency ranges or vegetative organ functions increased by more than 20%, preferably by more than 50%.

The HRV frequency information record or the spectrogram is a clear representation of complex rhythm information contained in the heart rate or heart rate variability. In this case, the information i. d. R. in three dimensions: abscissa = time, ordinate = frequency, color = amplitude / strength of activation of respective vegetative functions; however, the subject information may also be evaluated as processor-usable alphanumeric code).

Within the subject frequency bandwidth shown frequencies caused by the modulation of the heartbeat, d. H. by changing the time intervals between directly consecutive heart beats (= RR intervals). In addition to the heart rate (= heart rate) calculated as the average of the beats per minute, various other rhythms in the heartbeat can be recorded, which serve to control other organ systems. In the case of the coupling between cardiac and respiratory rhythm (QPA) during sleep, described below, the respiratory rhythm is transmitted to the heart rate and thus becomes visible in the spectrogram (RSA). The information content given here is of great importance for the assessment of the sleep architecture or its chaotisation by HFI loads.

In the method variant described above, according to a preferred evaluation method, it is detected as (HFI-induced) secondary significance if a short-term, preferably not longer than ten minutes, particularly preferably not longer than one minute, activation of previously not or in negligible intensity In the case of a graphic visualization of the HRV frequency information data set, this represents an approximately needle-shaped elevation running orthogonally to the time axis. In a preferred variant of the method, as a sufficient criterion for detection as (HFI-induced) secondary significance, a simultaneous activation of more than 50%, preferably more than 70% of the total, substantially 0 to 0.5 Hz frequency bandwidth of the HRV must be activated. Frequency information data set or spectrogram are present.

In the assessment of the HRV frequency information data set or spectrogram, it can also be assessed as activation or as (HFI-induced) secondary significance if the amplitude strength of respective frequency ranges or vegetative organ functions is more than 20%, preferably more than 50% and the analysis method can also include the possibility of weighting detected secondary significances on the basis of respectively determined amplitude amplitudes (in the case of graphical visualization) and / or the size of the respective activated frequency bandwidth, whereby events or secondary significances, in which high amplitude strengths or over a wide ranges of the frequency bandwidth of the HRV frequency information data set activations were determined, with a larger factor for the assessment of the presence of human biological relevant influence on the vegetative test subject kt-regulatory mechanisms are acted upon as those events or secondary significances, in which lower amplitude strengths or over a small range of the frequency bandwidth of the HRV frequency information record reaching activations were determined. By evaluating events or secondary significances according to their intensity and thus with regard to their potential disturbing influence on the vegetative regulatory system of the body, the informative value of the method according to the invention can be substantially increased. The carrying out of such a weighting as a function of the intensity of the events / of the secondary significances can also be carried out analogously in the case of all other HRV-FP courses mentioned.

In a special analysis method, in turn, the respiratory sinus arrhythmia (RSA) which can be represented in the HRV frequency information data record or spectrogram or its profile lying in the range from 0.2 to 0.3 Hz is brought into focus, with an upper end of the frequency bandwidth towards substantially convexly curved course activated organ functions as (HFI-induced) secondary significance, wherein the convexly curved course preferably extends over a time interval between 30 and 160 minutes, more preferably between 60 and 120 minutes.

The already mentioned respiratory sinus arrhythmia (RSA) is in each case the high-frequency variability of the heart rate, which reflects the intensity of the modulation of the heart rhythm by the respiration and can be determined by standardized HRV calculation methods. The RSA is converted to the logRSA by means of a decadal logarithm, which according to a further embodiment variant of the analysis method according to the invention can also be investigated as an autonomous autonomous functional parameter (HRV-FP) with regard to HFI-induced significances.

Note: The course of respective organ functions analyzed in the HRV frequency information data record or in the spectrogram is not necessarily shown in the spectrogram as a continuous line as in the case of other vegetative HRV function parameter profiles described above, but as a more or less fragmented point or area cloud. wherein each point or each area of this cloud is associated with information about the amplitude strengths of respective organ systems at particular times. An objectively described convexity of the RSA curve with respect to the time axis is generally preceded by a weakening of the amplitude strength under the influence of HFI, which in the case of a graphic visualization or in the spectrogram, for example. B. as blurring or more direction blue spectrum tends coloring of previously even more direction red / yellow spectrum tends coloring. Such attenuation of organ functions corresponding to the respiratory sinus arrhythmia (RSA) in the frequency range of substantially 0.2 to 0.3 Hz of the HRV frequency information data record can additionally or alternatively already be (HFI-induced) secondary significance in the method according to the invention be rated.

It has surprisingly been found in the course of the development of the method according to the invention that the above-mentioned time interval (30 to 160 min or 60 to 120 min), in which lying in the frequency range of about 0.2 to 0.3 Hz organ functions in terms increase and decrease in their frequency or show a convex course, which corresponds to the so-called BRAC (basic rest-activity cycle). The BRAC designates an ultradian chronobiological rhythm with a period of approximately two hours, comprising an activation phase of i. d. R. 80 to 120 minutes and a regeneration phase of i. d. R. 20 to 30 minutes. During sleep, this rhythm manifests itself as a change between deep sleep and REM phase, with the latter showing on the lowered arch forms or end regions of the convex RSA progress sections. During the day a rhythmic change between about 90 min activation or sympathetic activity and about 30 min regeneration or parasympathetic / vagus activity shows (Note: the periodic 90/30 min change of the BRAC is an average time, which may be subject to dilation as mentioned above), this ratio is reversed at night or during sleep: the parasympathetic / vagus activity is about 90 minutes, the sympathetic activity about 30 minutes.

The BRAC-characteristic course of RSA activation in the HRV frequency information record reflects activation by the sympathetic and parasympathetic (rather than the respiratory center). A reflection of the BRAC in the otherwise substantially bar-shaped or parallel to the time axis (in a frequency range of usually about 0.25 Hz) running RSA course during sleep is to be regarded as a pathological tendency and thus represents a suitable criterion to detect an HFI-induced disturbance of the vegetative regulatory mechanisms of the test subject.

An increase in RSA progression associated with weakening of the respiratory center, e.g. At approximately 0.3 Hz indicates an anti-cyclical slowing of respiration, a decrease in RSA progression indicates anticyclic acceleration of the test subject's breath and thus increasing sympathetic activation; in total, the respiratory rhythm is at least partially chaotized.

According to a further embodiment variant of the analysis method according to the invention, the temporal progression of the heart rate of the test subject is analyzed as vegetative function parameter (HRV-FP) and the occurrence of short-term arrhythmias is detected as secondary (HFI-induced) secondary significance with respect to the temporally preceding and following heart rate progression.

In a preferred analysis variant, the arrhythmia of the heart rate and thus as (HFI-induced) secondary significance are recorded as the occurrence of extrasystoles, whereby an extrasystole occurs in the course of the heart rate measured in the unit: [number of heartbeats per unit of time, preferably per minute] short-term, erratic increase in the heart rate over the respective chronologically preceding and subsequent heart rate course and thus represents a significant shortening of the interval duration between two consecutive heartbeats. In a preferred variant of the method, erratic increases in the heart rate are then recognized as extrasystoles, if these elevations each exceed 30%, preferably more than 50%, more preferably more than 100% of the immediately preceding or following heart rate or the average, in the respective measurement period determined heart rate.

Colloquially, an extrasystole is also referred to as a double stroke or heart beat. In the analysis method according to the invention, in particular intermediate beats are detected in which the heartbeat-inducing stimulus does not originate from the sinus node of the electrical cardiac conduction system, but is to be considered as a consequence of high-frequency immissions. In the case of a graphical representation or evaluation, extrasystoles would be depicted as needle-shaped elevations in the heart rate progression. that A portion of the heart rate curve corresponding to an extrasystole corresponds to a curve with a very strong slope approaching infinity. In practice, extrasystoles are considered to be approximately orthogonal to the time axis (thus usually vertical to the abscissa of an HRV diagram) extending curve sections. The larger this is the short-term rash of the heart rate or a corresponding curve, the shorter the time interval between two successive heartbeats of the test subject.

According to a further preferred embodiment of the analysis method according to the invention, the temporal course of respiratory sinus arrhythmia (RSA), which reflects the respiration-related modulation of the cardiac rhythm, is analyzed as a vegetative functional parameter (HRV-FP), which is preferably converted to logRSA by means of a decadal logarithm. As previously stated, respiratory sinus arrhythmia RSA is the high frequency variability of heart rate, which reflects the rate of cardiac rhythm modulation by respiration. RSA or logRSA is also a measure of the tone of vagal activity.

Alternatively or additionally, the temporal course of the pulse-respiratory quotient (QPA) of the test subject can be used as a vegetative function parameter (HRV-FP), wherein preferably those events are detected as (HFI-induced) secondary significances in which the QPA course of A ratio pulse / breath = 4: 1 deviates by more than 20% and / or in which temporary peaks or maximum values of the QPA course occur.

The vegetative functional parameter of the QPA (pulse / breath quotient), which can also be calculated from the modulation of the heart rate as part of an HRV analysis, indicates how often the heart beats (= pulse waves) during a respiratory cycle. The QPA increases with tension and decreases with relaxation. The interplay between heartbeat and breath is referred to in chronobiology as the center of the rhythmic system in humans. The organism endeavors to continuously bring the unstable equilibrium between pulse and breath into a rhythmic balance. During an undisturbed sleep optimal synchronization between pulse and breath is shown in a ratio of 4: 1. It has been found that this autonomic function parameter is particularly suitable for detecting an HFI-induced load of the test subject.

In a preferred embodiment of the analysis method, all peaks or maximum values occurring within the QPA profile are detected here as (HFI-induced) secondary significances. In the case of a graphical representation of the QPA course, these again show themselves as approximately needle-shaped or jagged elevations. In accordance with claim 1, it is preferably checked here whether coincident primary significances in the HFI profile are also present at those points in time of the reference time interval at which such QPA peaks were determined.

• – temporäre Erhöhungen des vegetativen Quotienten (VQ) um mehr als 20% (gegenüber zeitlich unmittelbar vorangehenden VQ-Werten oder gegenüber einem im Referenz-Zeitintervall ermittelten VQ-Mittelwert) oder/und
• – Überschreitungen bzw. Sympathikus-Dominanz des vegetativen Quotienten von mehr als 3:1, vorzugsweise von mehr als 5:1.

According to a further preferred embodiment variant of the analysis method according to the invention, the temporal course of the vegetative quotient (VQ) of the test subject is analyzed as the vegetative function parameter (HRV-FP), which is determined by the ratio of low frequencies associated predominantly with the sympathetic, in particular the blood pressure rhythm, substantially between 0.04 -0.15 Hz extending HRV frequency ranges (LF) to the parasympathetic, especially the respiratory modulation associated high-frequency, substantially between 0.15-0.40 Hz extending HRV frequency ranges (HF) results, being detected as (HFI-induced) secondary significances:

• - Temporary increases in the autonomic ratio (VQ) by more than 20% (compared to immediately preceding VQ values or compared to a VQ average value determined in the reference time interval) and / or
• - Exceedances or sympathetic dominance of the vegetative quotient of more than 3: 1, preferably of more than 5: 1.

The HF (high frequency) range includes autonomic rhythm fluctuations with period lengths of approximately 2.5 seconds to 7 seconds and 0.15-0.40 Hz, respectively. The power in the RF range corresponds to the activity of the parasympathetic nervous system and mainly reflects heart rate variations that are due to modulation across the Respiration are due. The LF (low frequency) range covers the frequency range of approximately 7-25 seconds or 0.04-0.15 Hz). The performance in this frequency range is mainly influenced by the sympathetic nervous system and, to a lesser extent, the parasympathetic nervous system. This frequency range was formerly also called the baroreceptor area, because the activity of this receptor is very reflected here. The low frequency components of heart rate variability correspond in particular to the blood pressure rhythm.

It is again preferably checked in accordance with claim 1 whether there are also coincident primary significances in the HFI progression at those points in time of the reference time interval at which such elevations or sympathetic dominances were ascertained.

According to a further preferred embodiment of the analysis method according to the invention, the temporal course of the SDNN _RR (standard deviation of normal-to-normal intervals) of the test subject is analyzed as the vegetative function parameter (HRV-FP), ie an HRV-associated statistical scattering measure around the mean of the heart rates Interval time or its differences to represent the change in the total variability of substantially all frequency ranges of artifact-adjusted RR heartbeat interval series within a specified time interval. The SDNN _RR is a measure of overall variability over all frequency ranges. An evaluation as HFI-induced significance occurs in particular in course sections with spontaneously elevated SDNN _RR , but can also be evaluated according to other criteria described below. Instead of the SDNN _RR can also be a corresponding or modified statistical characteristic ("time domain measure") such. B. SDNNIDX / ASDNN of the test subject to be analyzed.

The activation of autonomous regulatory mechanisms or vegetative function parameters are subject to a large number of influences during the day or while awake, which are directly related to physical or psychological activities of the test subject. Therefore, by determining the HRV-associated vegetative function parameters in a time interval and using them for an analysis according to the invention with regard to HFI-induced significances in which the test subject sleeps, influences attributable to the subjective sphere of the test subject can be largely eliminated. Since extensive activation of the parasympathetic nervous system occurs during sleep and external variables are restricted or reduced, immediate reaction of the autonomic system to HFI immissions can be detected with much greater evidence than is possible with various biofeedback procedures during the day. The requirement for sleep-proofing is particularly indicated in the case of analysis of respiratory sinus arrhythmia (RSA), pulse-to-respiratory quotient (QPA) or SDNN _RR- related autonomic autonomic function parameters (HRV-FP), in particular This underlying synchronization of the heart rhythm by breathing usually occurs only during sleep. A test during the sleep phase is also indicated in an analysis of the activation of other organ systems in the HRV frequency information data set or spectrogram.

In a particularly preferred variant of the method, all events or course segments are detected as (HFI-induced) secondary significances in one of the above-mentioned HRV-FP profiles, which graphically shows a gradient k> 1 or a gradient angle α of more than 45 ° , preferably of more than 70 °, more preferably of approximately 90 ° with respect to the time axis (slope k = ∞) or an approximately needle-shaped elevation of the HRV-FP curve, wherein in the aforementioned events or detection criteria each exceeding the average HRV-FP must be at least 20%, preferably at least 30%. As an average HRV-FP curve or as a detection reference value, it is possible to use either an HRV-FP value averaged over the entire period of the test or the reference time interval, or a HRV-FP value averaged over a shorter period of time , z. Example, the detected event or the secondary significance before and / or subsequent period of a maximum of 120, 60, 40, 20, 10 or 5 minutes. The predefined slope or the pitch angle is measured in each case on a curve tangent applied to the graph at a certain point in time.

According to a further preferred variant of the method, it is provided that at least one sub-time interval is opened from the time of detection of HFI-induced secondary significances, wherein at least one as sufficient criterion for the positive evaluation of the presence of a human biological relevant influencing of the vegetative regulatory mechanisms of the test subject further, occurring within the sub-time interval secondary significance must be detected and wherein the opened sub-time interval is preferably at most 5 minutes, more preferably at most 1 hour.

In particular, sub-time intervals may also be determined according to the manner of the course of the power density of the HFI in the reference time interval, i. H. as a consequence of the detection of a primary significance in the HFI course.

As a sufficient condition for the positive assessment of the presence of a relevant HFI-indicated influencing, the requirement of detecting a defined number of secondary significances may furthermore be provided, preferably at least three, particularly preferably at least five, per reference variable. Time interval, wherein the reference time interval is preferably between 0.5 and 12 h, more preferably between 5 and 30 min.

In practice, the measured values or data acquired by the ECG measuring device or HRV-FP evaluation device and the HFI measuring device are provided in different recording frequencies. To allow computational comparability of the ECG or HRV-FP data and the HFI data, it is intended to synchronize them. To synchronize the HFI and the HRV-FP curve for the considered reference time interval (T _1-n ), an equal number of the HFI and HRV-FP measurement or calculation values constituting the two curves is generated, preferably by interpolation and / or or extrapolation and / or selection of detected HFI and HRV-FP measurement / calculation values. Time-corresponding HFI and HRV-FP measurement / calculation values are each provided with the same time indices. In a preferred embodiment, the reference time interval for both (HFI and HRV-FP) courses is subdivided into a multiplicity of sub-time intervals of a maximum of 30 seconds, particularly preferably of a maximum of 10 seconds. Here, in each sub-time interval in each case that HFI measurement / calculation value and that HRV-FP measurement / calculation value is selected, which is the most significant with respect to each defined as a detection criterion for primary and secondary significances, preferably z. Minimum, maximum, median, average or delta values. The data obtained from two different signal channels (the HFI measuring device and the ECG measuring device or the HRV-FP evaluation device) are thus brought to the same number of measurement / calculation values by means of block operations.

As a sufficient condition for the positive evaluation of the presence of a relevant HFI-induced influence, it can also be provided according to a further preferred variant of the method that at least one, preferably at least three coincidental significances (in the HFI and in the HRV-FP curve) are detected per hour or that in the analyzed reference time interval more than 20%, preferably more than 30%, particularly preferably more than 50% of the primary significances detected in the HFI curve correlate with coincidental secondary significances in the HRV-FP curve.

• – Maximalwerte bzw. Leistungsfluss-Spitzen, welche im Falle einer grafischen Darstellung als annähernd nadel- oder zackenförmige Amplitudenausschläge ersichtlich sind und/oder
• – annähernd treppenförmige Verlaufsabschnitte, hierbei vorzugsweise die Anfangs- und/oder Endabschnitte eines jeweils annähernd horizontalen bzw. eine gleichbleibende Leistungsflussdichte indizierenden Verlaufsabschnitts der Treppenform und/oder ebendiesen annähernd horizontalen Verlaufsabschnitt selbst, und/oder
• – mehrere, vorzugsweise mehr als drei, vorzugsweise innerhalb eines Zeitintervalls von maximal 10, 30 oder 60 Minuten aufeinanderfolgende Anstiege und Abstiege des Verlaufs (Anmerkung: somit eine kammförmige Verlaufsform) und/oder
• – Überschreitung einer Leistungsflussdichte von mehr als 0,1 mW/m2, vorzugsweise von mehr als 0,05 mW/m2, besonders bevorzugt von mehr als 0,01 mW/m2 (Anmerkung: Es versteht sich, dass die voranstehende Einheit mW/m2 auch in andere äquivalente Einheiten zur Bestimmung von Hochfrequenz-Immissionen bzw. Leistungsflussdichten umrechenbar ist) und/oder
• – annähernd gleichbleibende Leistungsflussdichten bzw. im Falle einer grafischen Darstellung im Wesentlichen parallel zur Zeitachse verlaufende Abschnitte des HFI-Verlaufs und/oder
• – Absenkungen und/oder Anstiege des HFI-Verlaufs bzw. der Leistungsflussdichte, welche gegenüber unmittelbar vorangehenden und/oder nachfolgenden HFI-Verlaufsabschnitten vorzugsweise eine Differenz von mehr als 10%, besonders bevorzugt von mehr als 30% aufweisen, wobei als, unmittelbar vorangehend hierbei ein Zeitraum von mehreren, z. B. 1–30 Minuten, vorzugsweise von 0–60 Sekunden, besonders bevorzugt von 0–10 Sekunden definiert werden kann. (Anmerkung: Die vorgenannten Detektionskriterien betreffend Anstieg und/oder Absenkung von HFI-Verlaufsabschnitten können in analoger Weise auch zur Detektion von HFI-induzierten sekundären Signifikanzen in einem der HRV-FP Verläufe angewendet werden. Des Weiteren können sämtliche vorgenannten Detektionskriterien auch zur Generierung der charakteristischen, HFI-induzierten sekundären Signifikanzen bzw. HRV-FP-Referenzdaten gemäß Anspruch 2 herangezogen werden.)

According to a preferred variant of the method, it is provided that one or more of the following characteristics are detected in a preferably automated manner as primary significances in the HFI curve:

• - Maximum values or power flow peaks, which are visible in the case of a graphical representation as approximately needle-shaped or jagged amplitude fluctuations and / or
• - Approximately stair-shaped course sections, in this case preferably the beginning and / or end portions of each approximately horizontal or a constant power flux density indexing course section of the staircase and / or ebendese approximately horizontal course section itself, and / or
• - Several, preferably more than three, preferably within a time interval of a maximum of 10, 30 or 60 minutes consecutive increases and decreases in the course (Note: thus a comb-shaped waveform) and / or
• Exceeding a power density of more than 0,1 mW / m2, preferably of more than 0,05 mW / m2, more preferably of more than 0,01 mW / m2 (Note: It is understood that the above unit mW / m2 can also be converted into other equivalent units for the determination of high-frequency immissions or power flux densities) and / or
• - Approximately constant power flux densities or in the case of a graphical representation substantially parallel to the time axis extending portions of the HFI curve and / or
• - Reductions and / or increases in the HFI curve or the power density density, which preferably have a difference of more than 10%, particularly preferably more than 30% compared to immediately preceding and / or following HFI-course sections, wherein, immediately preceding this a period of several, e.g. B. 1-30 minutes, preferably from 0-60 seconds, more preferably from 0-10 seconds can be defined. (Note: The above-mentioned detection criteria concerning increase and / or decrease of HFI-course sections can also be used in an analogous manner for the detection of HFI-induced secondary significances in one of the HRV-FP courses.) Furthermore, all aforementioned detection criteria can also be used to generate the characteristic , HFI-induced secondary significances or HRV-FP reference data according to claim 2.)

According to a special variant of the method, it is provided that the interval duration, ie the time intervals of successive primary significances in the HFI curve and preferably also their intensity, ie the difference of the power density between immediately preceding and / or following HFI history sections, are subjected to a frequency analysis and in this case statistical Characteristic values are generated for which threshold values or maxima and / or minima are respectively defined, wherein an exceeding or undershooting of these threshold values is recognized as primary meta-significance and the occurrence of such a primary meta-significance is correlated to a temporal correlation with HRV FP course detected secondary significances is checked, wherein the presence of such a correlation as an indication of the presence of a human biologically relevant influence on the autonomic regulatory mechanisms of the test subject is evaluated and wherein as a statistical characteristic preferably related to the interval duration of consecutive primary significances SDNN (standard deviation of normal -to-normal intervals) or an equivalent time domain characteristic.

In the course of the analysis method according to the invention, the course of the power flux density of the high-frequency immissions is thus not necessarily analyzed, but the HFI curve can be converted into statistical characteristic values, as appropriate, by means of mathematical functions such as the Fourier transformation, analogously to the HRV frequency analysis of the RR intervals discussed at the outset serve as comparative variables or as primary significances for comparison with the occurrence of secondary significances in the HRV-FP profile. For example, based on the magnitude of these statistical characteristics such as (HFI-related) SDNN and derived and associated characteristics, it can be directly determined whether or not an HFI influence measured in a particular time interval is considered relevant or particularly distressing. The classification of the relevance results here from the specific progression characteristic of the HFI curve measured by means of dosimeters, in particular from the number of LFD increases and / or LFD reductions and / or LFD peaks detected in a specific period and / or the ratio of the each at successive times measured LFD intensities. The immission scenarios or primary significances described in the following exemplary embodiments can thus also be defined and detected in an indirect manner. For example, a low or zero SDNN may indicate a less relevant HFI load or primary (meta) significance, while a relatively high SDNN may indicate a particularly relevant HFI load or primary (meta) significance. The calculated statistical characteristic values or SDNN values are each provided with time indices for chronological assignability. Again, the aforementioned detection criteria can also be used to generate the characteristic, HFI-induced secondary significances or HRV-FP reference data according to claim 2.

According to a particularly meaningful method variant, it is provided that a targeted initiation of a high-frequency immission acting on the test subject is performed by one or more specially provided test radio-frequency source (s) and thus one or more temporally determined primary significances are generated the simultaneously determined HRV-FP curve of the test subject is checked for secondary significances coincident to the primary significance (s), and wherein the initiation of the high-frequency immission is preferably in a (external, ie not caused by the test RF source High frequency immissions related) HFI-course area with approximately constant immission altitude over a defined period of time or in the presence of external high frequency immissions, which are below a defined threshold, preferably below a power flux density of 0.4 mW / m2.

According to a special variant of the method, it is provided that the test subject during one or more time intervals by a preferably surrounding on all sides of the test subject shielding such. B. shielded by electrically conductive metal fabric of external high-frequency immissions of the environment and the shielding either open for a defined period of time and thus the test subject is exposed to the external high-frequency immissions or that with the shielding device closed in the time interval of the shield at least one within the shielding arranged test RF source one or more times for a defined period of time is activated, wherein the simultaneously determined HRV-FP course of the test subject on the / by the test high-frequency source generated primary significance (s) coincident secondary significances resp on secondary significances occurring during the period of the opening of the shielding device. In the HRV-FP course of the test subject, discernible differences occur during the objective, artificially created immission conditions (duration of the shielding and / or duration of activation of the test high-frequency source) compared to preceding or subsequent time intervals, each with a comparatively regular immission level or high-frequency power flux density), this is assessed as the presence of a human biological relevant influence on the vegetative regulatory mechanisms of the test subject.

Through targeted debugging arrangements according to the preceding claims, the examination duration or the measurement operating time for the HFI and / or HRV-FP detection devices can be significantly reduced and the informative value of the analysis result can be increased. I. d. R. can do this for the test subject within a test period of 30 minutes, an evident analysis result on the presence or absence of an HFI-induced regulatory disorder are passed.

In a preferred embodiment of the invention, it is provided to carry out a frequency-specific analysis of the HFI and in this case to compare several HFI courses with respective HRV-FP courses. From a procedural point of view, such comparisons can take place either simultaneously or consecutively. Viewed time windows can be arbitrarily fragmented. Also, a preferably automated analysis of selected HFI frequency bands or their HFI progressions, including comparison with chronologically corresponding HRV-FP progressions, can only take place at those points in time or periods at which one or more defined significances are detected in the case of respective HFI progressions. The representational comparisons can z. B. typical frequency bands include the HFI as the transmission standards GSM, UMTS, LTE, WLAN, DECT, Bluetooth u. a. assigned.

From the high-frequency immissions acting on the test subject, it is thus possible to identify a plurality of HFI sources, each with different frequency band ranges, and to determine corresponding HFI progressions (intensity over time), which are respectively related to the test subject. In a preferred embodiment, in the method according to the invention, the frequency bands of all common transmission standards such as GSM, UMTS, LTE, WLAN, DECT, Bluetooth u. a. each detected as isolated HFI waveforms and compared with time-corresponding HRV-FP curves. It is also possible to use for the representational comparisons only those frequency bands or HFI courses for which a frequency-specific exceeding of defined high-frequency immission reference values was determined on site by measurement; a definition of such Immissionsrichtwerte can turn z. B. based on environmental medical recommendations such as the precautionary values of the Salzburg Medical Association or the "SBM standards of building biology measurement technology". Furthermore, it is also possible to combine selected frequency bands by summing or subtraction or by other mathematical functions in a common HFI curve or to separate from each other, d. H. filter out certain frequency bands and thus exclude them from the analysis.

Such a multi-layered, frequency-selective analysis method of the HFI for determining significant correspondences with the HRV-FP courses is advantageous in that it has been found in environmental medicine practice that electrosensitive persons in a very individual and difficult to estimate each existing high-frequency immissions react. Some persons do not show a response indication to immissions from certain frequency bands, while immissions from other frequency bands (or specific clocking / pulse rates) have a high response indication and vice versa. Since humans today are exposed to a growing variety of high-frequency emitters both externally and internally in their place of residence, it seems particularly important in terms of emission reduction to determine the transmission standard or HFI sources which are among the most significant individual burdens to lead.

A frequency-selective analysis method of the HFI can also be carried out for generating characteristic, HFI-induced secondary significances in a test arrangement according to claim 2 which precedes the current experimental setup.

According to a special evaluation variant, it is provided that the measurement or calculation values constituting the HFI profile and the HRV-FP profile are respectively converted to a (chronological) gradient sequence (G _HRV-FP and G _HFI ), which consists of a multiplicity of temporally successive gradients , ie the difference between each two consecutive time HFI or HRV-FP measurement or calculation values together. The gradient sequences (G _HRV-FP and G _HFI ) form in the case of a graphical representation as a bar graph and are analyzed in accordance with the invention in terms of their correlation.

According to a preferred variant of the method, the gradient sequences (G _HRV-FP and G _HFI ) are created corresponding graphical gradients, the gradient sequence corresponding to the HRV-FP gradient (G _HRV-FP ) and the gradient sequence (G _HFI ) corresponding to the HFI gradient to one another superimposing or side by side, in this case preferably mirror-symmetrical to a parallel or congruent with the time axis extending mirror axis are arranged. Preferably, at least one of the gradient sequences (G _HRV-FP and G _HFI ) is scaled in such a way that orthogonal to the time axis running average heights or elevations of the gradients are approximately equal in both graphs.

According to a further variant of the method, envelope curves are applied to the graphic sequences of the gradient sequences (G _HRV-FP , G _HFI ) which are to be compared with one another. In this way, any correlation in the time course of the gradient sequences (G _HRV-FP , G _HFI ) or the simultaneous occurrence of primary and secondary significances can be represented particularly clearly. Even significances with comparatively small fluctuations or only short-term duration can be made visible in this way.

According to a further variant of the method, a correlation value which is preferably given as a percentage is determined between the gradient _sequence corresponding to the HRV-FP gradient (G _HRV-FP ) and the gradient _sequence corresponding to the HFI gradient (thus for the determined within the considered reference time interval HFI and HRV-FP courses). If this correlation value has a respectively fixed reference correlation value of z. B. exceeds 30% or 0.3 or 40% or 0.4, there is a positive assessment of the presence of a human biologically relevant influence of the test subject.

It should be noted that a previously described gradient formation with arithmetic and / or graphical evaluation can also be carried out in an analogous manner for an analysis of HRV-FP progressions according to claim 2 and dependent variants of the method, d. H. The HRV-FP reference data or control characteristics or the characteristic, HFI-induced secondary significances can also be compared in the form of gradient sequences with the respectively currently determined HRV-FP profiles of the test subject.

In order to exclude imponderables which result from a consideration of individual HRV-FP, in preferred embodiment variants of the analysis method, an analysis of several HRV-FP profiles with respective detection of secondary significances takes place. Thus, it may be provided that, as a verification of the analysis, a comparison of the analyzed HRV-FP profile with at least one, preferably with at least two of the HRV-FP profiles listed in the preceding claims with regard to the presence of substantially simultaneously occurring or causally correlating secondary significances. Preferably, the analyzed HRV-FP profile and / or the additionally used HRV-FP profiles are each compared with in a time-corresponding HFI curve or with primary significances detected therein according to claim 1.

According to a further variant of the method, it is provided that, as verification of the analysis, a comparison of the analyzed HRV-FP profile in at least two, preferably in at least three, of the HRV-FP profiles or types of analysis given in the preceding claims has a defined number of ( HFI-induced) secondary significance must be detected.

According to a further variant of the method it can be provided that, depending on the determined frequency and / or standard deviation (size) of the HFI-induced secondary significances, a gradual evaluation of the probability of the occurrence of an incompatibility of high-frequency immissions, preferably given in percent, takes place. In this case, the standard deviation is understood to mean the deviation from the respective metrologically determined mean values or expected values, which take into account typical measured value maxima and minima. In the simplest sense, the standard deviation can be defined as the variance of the respective measured or calculated values by their expected value. Their basic determination from the square root of their variance can be supplemented by expanding functional parameters. In the determination of the standard deviation z. For example, process-specific factors and characteristics such as hysteresis, resonance, addition or multiplication effects, typical swelling and decay effects, and the like (each related to both HRV waveforms and HFI waveforms) may be included or filtered out.

Alternatively or additionally, a graded, preferably in a percentage, evaluation of the vegetative regulatory capacity of the test subject with respect to HFI-induced secondary significances can be carried out on the basis of a comparison with HRV-FP comparative data, this comparison data being based on current or past measurements on the test subject itself or on empirical values of external data HRV-FP test series are based.

A graded, preferably in a percentage, evaluation of the vegetative regulatory capacity of the test subject with respect to HFI-induced secondary significance may also be based on the speed recorded in the HRV-FP curve with which the organism of the test subject detects HFI-induced secondary significances in the HRV-FP. The process returns to a standardized or individually calculated HRV-FP control profile, ie returns to defined target values (the target values can in turn be stored statically on a database or dynamically generated in real time).

Claims 29 and 30 are directed to arrangements for carrying out the aforementioned analysis methods.

According to claim 29, this arrangement comprises a measuring device for measuring the intensity of high-frequency immissions (HFI), which is preferably determined as the power flux density, and an electrocardiogram (EKG) measuring device including measuring electrodes for determining the temporal variation of successive heart rate intervals and the course thereof determined with heart rate variability ( HRV) associated vegetative function parameter (HRV-FP) of the test subject and a processor-controlled evaluation together with this in data-connected storage device on which one or more evaluation algorithms for performing an analysis method according to one of claims 3 to 28 are deposited, wherein preferably the ECG measuring device and / or the evaluation device and / or the HFI measuring device are combined in a device applicable to the test subject or the evaluation device is positioned at an external location and by means of the ECG or HRV-FP and HFI measuring devices determined data sets to this external, preferably server-based evaluation device for carrying out the analysis method according to the invention can be transmitted.

In accordance with claim 30, the arrangement comprises an electrocardiogram (ECG) measuring device including measuring electrodes for determining the temporal variation of consecutive heart rate intervals and the course thereof determined, associated with heart rate variability (HRV) vegetative function parameters (HRV-FP) of the test subject and a processor-controlled evaluation together with this data-related storage device on which characteristic, HFI-induced secondary significances or progress characteristics are stored in the form of digital or analog data and one or more evaluation algorithms for performing an analysis method according to one of claims 3 to 28, wherein preferably the ECG measuring device and or the evaluation device is combined in a device that can be applied to the test subject or the evaluation device is positioned at an external location and by means of the EKG or HRV-FP and HFI measuring devices determined data sets to this external, preferably server-based evaluation device for performing the analysis method according to the invention or for comparison with the characteristic, HFI-induced secondary significances can be transmitted.

The external evaluation device is preferably designed as a server-based device, to which the data recorded by the ECG and / or HFI measuring devices records z. B. can be transmitted in a wireless manner or by means of an Internet-based upload. The server-based evaluation of the data records in accordance with the analysis method proposed according to the invention can be carried out fully or semi-automatically or with or without the supervision of a skilled person skilled in the art. A client or the test subject is thus given the opportunity to obtain information on the presence or absence of an HFI-induced stress on his vegetative organism within a very short time. The calculation of the HRV-FP historical data based on the ECG measurement data can be carried out both at the designated server and directly in the device that can be applied to the test subject. Likewise, the analysis method according to the invention could also be carried out directly in the device applicable to the test subject or in conjunction with a stationary PC provided with a data transmission interface and a corresponding analysis software.

The invention will now be explained in more detail with reference to exemplary embodiments. Note: The following figures or diagrams are each color in the original, have been resolved in pure black and white punctuation graphics to meet standard requirements. A given by originally color codings, additional information content of the graphics, z. B. concerning amplitude strengths in the spectrogram, is explained in the technical description. Show it:

1 an ECG chart with a heart beat corresponding to RR intervals

2 a representation of the positioning of ECG measuring electrodes on the test subject

3 a heart rate chart (heart rate) determined from the recorded RR intervals

4 a frequency-specific power spectral density (RR interval spectrum

5 according to respective frequency ranges 4 corresponding heart rate amplitudes (filtered heartbeats)

6 a HRV frequency information set, represented as a three-dimensional spectrogram, which provides the spectral information according to the preceding 1 - 5 contains (in particular vagus and sympathetic activity)

7 a high-frequency immission (HFI) waveform measured by dosimeter over a period of 8 h (peak HFI values marked as primary significances)

8th one for the same period as the HFI 7 determined course of the heart rate of a test subject with detected extrasystoles

9 a measured by dosimeter high frequency immission (HFI) course over a period of 8 h

10 one at the time of the HFI measurement according to 9 corresponding HRV frequency information record of a test subject represented as a spectrogram (areas from bottom to top: peripheral tissue perfusion / REM, blood pressure, RSA / BRAC, deep sleep phases)

11 one over the period according to the preceding 9 and 10 illustrated course of the vegetative quotient VQ of the test subject (sympathetic / parasympathetic activity)

12 measured by dosimeter HFI over a period of 8 h (peak HFI values marked as primary significances; HF dosimeter: GSM900down)

13 one for the same period as the HFI 12 determined course of the pulse-breath quotient (QPA) of a test subject - with focus on increase of the QPA according to HFI-induced secondary significances

14 over the same period as in 12 measured HFI course (HFI stair patterns or tableaux marked as primary significances)

15 one for the same period as the HFI 14 determined course of the pulse-breath quotient (QPA) of the test subject - with focus on the secondary significance of the QPA corresponding to the staircase patterns of the HFI course

16 a measured by dosimeter HFI course over a period of 8 h with primary significance

17 one for the same period as the HFI 16 determined course of the heart rate of a test subject with secondary significance

18 one over the period according to the preceding 16 and 17 determined course of the pulse-breath quotient (QPA) of a test subject with secondary significances

19 a measured by dosimeter HFI course over a period of 8 h with primary significance

20 one for the same period as the HFI 19 determined course of the heart rate of a test subject with secondary significance

21 one over the period according to the preceding 19 and 20 determined course of SDNN _RR (standard deviation of normal-to-normal intervals) of a test subject with secondary significances

22 an HRV frequency information record presented as a spectrogram during a sleep phase of the test subject

23 one over the period according to the preceding 22 illustrated course of the vegetative quotient VQ of the test subject (sympathetic / parasympathetic activity)

24 one over the period according to the preceding 22 illustrated course of the heart rate of the test subject with marked REM phases

25 one over the period according to the preceding 22 illustrated the course of the pulse-breath quotient (QPA) of the test subject with marked REM phases

26 an HRV frequency information record presented as a spectrogram during another sleep phase of the test subject (two nights after analysis in accordance with 22 - 25 )

27 one over the period according to the preceding 26 illustrated course of the vegetative quotient VQ of the test subject (sympathetic / parasympathetic activity)

28 one over the period according to the preceding 26 illustrated course of the heart rate of the test subject with marked REM phases

29 one over the period according to the preceding 26 illustrated the course of the pulse-breath quotient (QPA) of the test subject with marked REM phases

30 a measured by dosimeter HFI course

31 according to the HFI and heart rate curves 30 and 32 corresponding gradient sequences, mirrored about a horizontal axis

32 one for the same period as the HFI 30 determined heart rate history

33 the gradient sequences 31 with applied envelope / trend curves, mirrored around a horizontal axis

34 the gradient sequences 33 with applied envelope / trend curves superimposed on each other

1 shows in an electrocardiogram (ECG) recorded RR intervals of successive heartbeats of a test subject. An RR interval here refers to the distance between two R waves of an electrocardiogram measured along the time axis (here: abscissa) and thus the time interval between two successive pulse waves or contractions of the heart muscle of a test subject. For a frequency analysis described below, other interbeat intervals or derived indication variables could be used instead of the R-waves or RR intervals.

For exact measurement of the RR intervals, three ECG measuring electrodes E1 (negative electrode), E2 (positive electrode) and E3 (neutral electrode) are inserted in one according to 2 configuration applied to the chest area of the test subject. The detected sequence of RR intervals is referred to as a time series. In the present exemplary embodiment, the RR intervals were converted from a binary format into ASCII values and read in by an evaluation device. From the temporal variability of the RR intervals, the heart rate variability (HRV) can be determined by means of standardized mathematical operations. Heart rate variability represents a mathematical correlate for adapting the heart rate to changing requirements in the human organism and is regarded as an expression of neurovegetative regulatory capacity. The RR intervals are z. B. measured in the unit milliseconds [ms] and usually vary between about 700 and 1200 ms.

The determination of heart rate variability (HRV) and associated physiological parameters, including heart rate, QPA, VQ, SNNN _RR , is well known in the art and makes it possible to detect the finest changes in the regulatory system of the human body. The autonomic nervous system, also called vegetativum, regulates, among other things: heart activity, blood pressure, distribution of bloodstreams, breathing depth, respiratory rate, thermoregulation, glandular secretion and gastric and intestinal motility. It is divided into two subsystems, the sympathetic and the parasympathetic nervous system.

The heart rate or heart rate is determined on the basis of time indices of the detected RR intervals. To describe the tone of individual areas of the autonomic nervous system, a spectral analysis is performed ( 3 - 6 ). The measurement data of the heart rate are in this case transmitted by means of mathematical method from the time domain in the frequency domain and displayed as a power spectrum. In this way, various other frequencies present in the heart rate can be made visible, which are assigned empirically to specific body rhythms such as respiratory or blood pressure. For the analysis method according to the invention described below, in principle all according to the prior art known or future to be developed HRV sensor and software systems are used. A statistical processing of received data is done z. B. with proprietary evaluation routines such as MatLab ^® and SPSS ^® .

For the calculation of a frequency analysis, the heartbeat sequence is divided into equidistant sections and brought by a Fourier transformation of the time domain in the frequency domain. This transformation decomposes the total signal into individual sinusoids and reproduces the order of magnitude of the individual frequency components. The frequency bandwidth of substantially 0 to 0.5 Hz is calculated according to the order of the frequency components for specific time periods. In particular, the following frequency ranges are distinguished:
0.04 to 0.15 Hz: Low Frequency (LF) -with correspondence: predominantly sympathetic activity, to a lesser extent also vagal activity, in particular allocation of the blood pressure and circulatory rhythm;
0.15 to 0.40 Hz: High Frequency (HF) with correspondence: parasympathetic / vagus activity; Assignment, in particular of respiratory functions, corresponds in particular to the parasympathetic determined oscillation component of the respiratory sinus arrhythmia (RSA) and thus the respiratory synchronous heart rate fluctuation;
The power over the entire frequency range of 0.0033-0.5 Hz is called TOT (total frequency). The TOT indicates the size of the total area within all frequency ranges and is considered the measure of the influence of the vegetative on the organism. Within the respective frequency ranges, the power is determined and converted using the natural logarithm. As special frequency ranges ULF (ultralow frequency; <0.003 Hz) and VLF (very low frequency, essentially 0.003-0.04 Hz) can be specified or analyzed.

The activities of respective frequency ranges or vegetative organ functions over a certain period of time are stored in an HRV frequency information data record which can be represented graphically in practice as a spectrogram (see 10 ). The spectrogram is a clear representation of the complex rhythm information contained in the heart rate or heart rate variability. In chronobiology, it is considered an image of human proper time and is also called "AutoChrone image"("autos" = self, own; "chronos" = time), where the information in three dimensions (here: abscissa = time, ordinate = Frequency, color = amplitude). The amplitude characterizes the energy density of respective frequency ranges determined from the HRV frequency analysis and is preferably stored as a dimensionless numerical value. The scale of the amplitude can be chosen arbitrarily and z. Range from 0 to 1 or from 0 to 50, where values tending towards zero signify a very low amplitude strength and directions 1 and 50 indicate a very high amplitude strength.

Each (in 10 : vertical) line of the spectrogram is the result of frequency analysis of a short section of a time series, e.g. B. a heartbeat sequence. The amplitude of the respective rhythms is coded in color. A small amplitude is z. B. in blue color, a higher in white and yellow color, a very high in red color (see also 6 ). It should be noted that the originally colored graphics according to the figures were each resolved into pure black-and-white puncturing and therefore the color coding of the amplitude intensities is only poorly recognizable.

In particular, the spectrogram allows a statement about the sleep architecture, whereby a good sleep is cyclical and quiet sleep phases clearly differ from REM sleep phases (dream sleep). A bad sleep appears fragmented and vegetatively restless. The vegetative balance (tension recovery, see also the following defined vegetative function parameter VQ) in good sleep is vagotter than in bad sleep. Using a frequency analysis, autonomic rhythms and sleep stages can be calculated from the heartbeat or spectrogram. These metarhythms develop from the circadian rhythm, which is characterized by the change of day and night. The day and night events then follow the BRAC, a cycle of rest and activity cycles in a 2-h rhythm associated with the circadian rhythm. Activity phases (90-120 min.) And regeneration phases (5-20 min.) Alternate during the day, which continue in the nocturnal sleep architecture as longer deep sleep and shorter REM phases. The perfusion of peripheral tissues shows a minute rhythm (= 0.017 Hz), the rhythm of the blood pressure follows a 10 second oscillation (= 0.1 Hz), while the relaxed or sleeping human shows the rhythm of the respiration (= 0.2-0 , 3 Hz) in the heartbeat.

Further important characteristics of the autonomic nervous system or vegetative function parameters are listed below:
Heart rate: This characteristic already mentioned above denotes the absolute interval duration between two R-waves or their differences. This results in the presentation over the time axis to the heart rate Course. The heart rate is thus the number of heartbeats - represented by the R-waves - per unit time, usually per minute. In the approach of HRV is to be considered, however, that at a heart rate of z. B. 60 beats per minute, the heart rate within this minute can change very well. Thus, the heart rate is to be regarded as the average value within one minute. However, consider not the absolute number of R-waves in the time domain, but the distances from R-wave to R-wave (see 1 ), one arrives at a time measurement of the time differences of the RR distances in milliseconds (ms). The analysis in the time range of the absolute RR intervals leads to heart rate variability (HRV) - ie the difference between the heart beats.

LF / HF (vegetative quotient, VQ): The quotient of the two frequency ranges LF and HF contained in the aforementioned HRV frequency information data set reflects the current vegetative activation level of the organism. Higher VQ scores indicate an active, performance-oriented attitude of the body, low VQ scores a recovery-oriented one. The VQ is represented over time and represented as the ratio of activation of sympathetic (preferably red) and parasympathetic (preferably blue). A VQ of 1: 1 thus means an equally strong occurrence of sympathetic and vagal rhythm. Falling below this threshold, marked with a line, can be equated with the beginning of a good recovery; If the test subject stays in the sleep phase above the ratio VQ = 1: 1, the recovery is disturbed.

SDNN _RR (standard deviation of normal-to-normal intervals): The standard deviation, usually for 5 minutes of artifact-adjusted RR interval series, is a measure of the overall variability over all frequency ranges. Long-term studies have shown that overall variability is a measure of vitality and that people with lower SDNN _RR live much shorter. The SDNN _RR is statistically the spread around the mean of the interval duration (or its difference) within a given period of HRV analysis. This shows the level of variability of all RR intervals - and thus the heart rate variability - within a defined period of time.

The standard deviation is generally calculated:

Respiratory Sinus Arrhythmia (RSA): the respiratory change in heart rate. The RSA is calculated as RSA = median (| HR i - HR i-1 |), where the median represents the central value of the sorted sequence of numbers within a time period. The RSA represents the relationship between pulse and breath and is shown in the spectrogram as an oscillation between 0.2 and 0.3 Hz, which occurs during sleep and relaxation phases. This phenomenon is caused by the breath-synchronous fluctuation of the heart rate, which increases with inhalation (sympathetic) and decreases with exhalation (vagus). The function of RSA is to maximize gas exchange through optimal interaction of perfusion and ventilation with each breath. The RSA is converted to the logRSA by means of a decimal logarithm.

logRSA: The median of the absolute differences of consecutive heart rate values, like the HF, primarily refers to the rapid, breath-induced changes, but without drawing a strict limit at a particular frequency. Respiratory sinus arrhythmia (RSA) is thus the high-frequency variability of heart rate, which reflects the rate of cardiac rhythm modulation by respiration. It is also a measure of the tone of the vagus activity.

QPA: The pulse-to-breath ratio indicates how often the heart beats during a breath (ratio of heart beats to a respiratory cycle). During the night and in peace it could be observed that in healthy individuals QPA is set to a ratio of approximately 4: 1, regardless of the quotient under ergotrophic conditions during the day, which may be between 2: 1 and 22: 1.

• – Ermitteln des Verlaufs einer als Leistungsflussdichte gemessenen Intensität von Hochfrequenz-Immissionen (HFI) in Bezug auf das Testsubjekt über einen definierten Zeitraum,
• – Ermitteln der Varianz des zeitlichen Abstands von mittels Elektrokardiogramm-Messverfahren (EKG) erfassten, aufeinanderfolgenden Herzschlägen sowie des Verlaufs daraus ermittelter, mit der Herzratenvariabilität (HRV) assoziierter vegetativer Funktionsparameter (HRV-FP) des Testsubjekts über einen definierten Zeitraum,
• – wobei die HFI- und die HRV-FP-Verläufe zumindest abschnittsweise synchronisiert und auf mindestens ein gemeinsames Referenz-Zeitintervall T_1-n kalibriert werden. In der Praxis werden die von der EKG-Messeinrichtung bzw. HRV-FP Auswerteeinrichtung und der HFI-Messeinrichtung erfassten Messwerte bzw. Daten gerätebedingt in unterschiedlicher Aufnahmefrequenz bereitgestellt. Um eine rechnerische Vergleichbarkeit der EKG- bzw. HRV-FP Daten und der HFI Daten zu ermöglichen, wird durch Interpolation und/oder Extrapolation und/oder Selektion für beide zu vergleichende (HRV-FP und HFI-)Datenkanäle jeweils eine gleiche Anzahl an Mess- oder Berechnungswerten erzeugt. Zeitliche Synchronisation bedeutet somit in der Auswertepraxis, dass im betrachteten Referenz-Zeitintervall Messwerte bzw. Daten beider (HFI- und HRV-FP-)Signalkanäle chronologisch indiziert und quantitativ aufeinander abgestimmt werden.
• – Durchführen einer Analyse des HFI-Verlaufs, in welcher primäre Signifikanzen 1–n detektiert werden; hierbei handelt es sich um temporäre, d. h. innerhalb des Referenz-Zeitintervalls T_1-n zeitlich begrenzte Abweichungen des HFI-Verlaufs von definierten statischen oder dynamischen Mittelwerten oder HFI-Referenzdaten, insbesondere signifikante Anstiege und/oder Absenkungen des HFI-Verlaufs gegenüber zeitlich unmittelbar vorangehenden und/oder nachfolgenden Abschnitten des HFI-Verlaufs; die primären Signifikanzen dauern ebenso wie nachfolgend beschriebene sekundäre Signifikanzen jedenfalls kürzer an als das gesamte Referenz-Zeitintervall T_1-n.
• – Durchführen einer Analyse des HRV-FP-Verlaufs, in welcher sekundäre Signifikanzen 1'–n', i. e. temporäre, d. h. innerhalb des Referenz-Zeitintervalls T_1-n zeitlich begrenzte Abweichungen des HRV-FP-Verlaufs von HRV-FP-Referenzdaten bzw. Regelverläufen, welche aus empirischem Datenbestand geschöpft und/oder dynamisch generiert, d. h. aus aktuellen Mess-/Berechnungsdaten bzw. HRV-FP-Verläufen abgeleitet sind, detektiert werden, insbesondere signifikante Anstiege und/oder Absenkungen des HRV-FP-Verlaufs gegenüber zeitlich unmittelbar vorangehenden und/oder nachfolgenden Abschnitten des HRV-FP-Verlaufs;
• – bei Detektion einer definierten Anzahl an koinzidenten, d. h. im Wesentlichen zeitgleich auftretenden primären und sekundären Signifikanzen 1–n, 1'–n' im HFI-Verlauf sowie im HRV-FP-Verlauf eine positive Bewertung des Vorliegens einer humanbiologisch relevanten Beeinflussung der vegetativen Regulationsmechanismen des Testsubjekts erfolgt.

In the course of a method according to the invention for analyzing the interaction of electromagnetic high-frequency immissions (HFI) with vegetative regulation mechanisms of a test subject, the following method steps are fundamentally provided:

• Determining the course of a measured as power density density intensity of radio frequency emissions (HFI) with respect to the test subject over a defined period,
• Determining the variance of the time interval of successive heart beats detected by means of electrocardiogram measurement (ECG) and the course of the same determined with the Heart rate variability (HRV) of associated vegetative function parameters (HRV-FP) of the test subject over a defined period,
• - Wherein the HFI and the HRV-FP curves are at least partially synchronized and calibrated to at least one common reference time interval T _1-n . In practice, the measured values or data recorded by the ECG measuring device or HRV-FP evaluation device and the HFI measuring device are provided in different recording frequencies as a result of the device. In order to allow computational comparability of the ECG or HRV-FP data and the HFI data, by interpolation and / or extrapolation and / or selection for both (HRV-FP and HFI) data channels to be compared in each case an equal number of measurements or calculation values. Time synchronization thus means in the evaluation practice that measured values or data of both (HFI and HRV-FP) signal channels are chronologically indexed and quantitatively coordinated with one another in the considered reference time interval.
• Perform an analysis of the HFI history in which primary significances 1 -N are detected; this is temporary, ie within the reference time interval T _{1 -n,} time-limited deviations of the HFI curve from defined static or dynamic mean values or HFI reference data, in particular significant increases and / or decreases in the HFI curve compared to immediately preceding ones and / or subsequent sections of the HFI history; The primary significances, as well as the secondary significances described below, are always shorter than the total reference time interval T _{1 -n} .
• Perform an analysis of the HRV-FP history in which secondary significances 1' -N ', ie temporary, that is within the reference time interval T _1-n time-limited deviations of the HRV-FP curve of HRV-FP reference data or rule progressions, which drawn from empirical data and / or dynamically generated, ie from current Are detected, in particular significant increases and / or decreases in the HRV-FP course over temporally immediately preceding and / or subsequent sections of the HRV-FP course;
• - Upon detection of a defined number of coincident, ie substantially simultaneously occurring primary and secondary significances 1 -n, 1' In the HFI course and in the HRV-FP course, a positive evaluation of the presence of a human biological relevant influencing of the vegetative regulatory mechanisms of the test subject takes place.

Changes or correlations in the HRV-FP course and / or in the HFI course are immediately and immediately, i. H. recorded in real time. The measurement of the HFI intensity or power density is performed z. B. with a Maschek ESM 140 dosimeter, but can also be performed with other suitable HFI measuring devices and spectrum analyzers. Immission measurements in the high-frequency range can in principle be carried out with broadband measuring devices or with frequency-selective devices. Broadband meters are easy to use and allow quick overview measurements. Due to their low dynamic range and especially the lack of frequency selectivity but they are not suitable to determine from the entire immission spectrum that part of specific high-frequency emitters such. B. mobile base stations goes out. In addition, no correct extrapolation of the measured power flux density instantaneous value to the immission at maximum system utilization is possible.

Within the evaluation, the originally measured power or voltage values are converted into the power density density or field strength and extrapolated to the HFI at maximum system utilization. This is possible in the case of GSM base stations in that the immission of the control channels BCCH is identified from the measured spectrum and multiplied by a factor which is determined by the number of maximum available frequency channels. It is common to specify the power density in units of W / m2, mW / m2 or μW / m2 (1 watt = 1 W, 1 milliwatt = 1 mW = 0.001 W, 1 microwatt = 1 μW = 0.000 001 W).

Whenever we talk about power flux densities (LFDs), we refer to HFI intensities at specific times. The terms HFI and LFD are thus to be understood synonymously. Of course, in the following 7 . 9 . 12 in units of mW / m2 scaled HFI waveform diagrams are also presented in alternative units for measuring the power flux densities or field strengths caused by external high frequency sources. The measurement of the power density is continuous, individual HFI measurements are provided with time indices and logged chronologically. The recording intervals of the HFI meter are z. B. 0.5 seconds.

The HFI measuring device is directly on the body, z. B. attached to the arm or in the immediate vicinity of the body of the test subject. It preferably has a frequency-selective measurement of mobile radio bands corresponding to the state of the art or of other frequency ranges used by HF wireless technologies, in particular GSM-900, GSM-1800, DECT, UMTS, LTE, WLAN and others. Measuring data acquired by the measuring device can be output as a graphic or tabular representation. Recording of the HFI meter is initiated by the test subject by pressing a switch or by other input method. Calibration and synchronization with the recording data sets of a HRV-FP measuring device which is also provided for monitoring the same reference time interval but is activated and deactivated at a later time is carried out by a method-compliant evaluation algorithm based on time indices of the HFI and HRV-FP data sets or event histories.

Detected HFI frequency ranges or mobile radio bands can be selectively included or hidden in a subsequent evaluation. First of all, the use of those HFI courses or mobile bands in which the highest power flux densities or the most primary significances were determined during the investigation period is recommended. In addition to the usual average power density RMS (6 minutes ICNIRP averaging) average calculation, a peak function is also possible to represent the actual peak HFI loads over time. For each measurement sequence, a unique identity code is defined, which later enables the identification and timing of the performed HFI measurements. For graphical evaluation, selected frequency bands or HFI profiles can be exported as an image file (eg using the visualization software Graph ESM-140).

• – Maximalwerte bzw. LFD-Spitzen, welche im Falle einer grafischen Darstellung als annähernd nadel- oder zackenförmige Amplitudenausschläge ersichtlich sind,
• – annähernd treppenförmige Verlaufsabschnitte, hierbei vorzugsweise die Anfangs- und/oder Endabschnitte eines jeweils annähernd horizontalen bzw. eine gleichbleibende LFD indizierenden Verlaufsabschnitts der Treppenform und/oder ebendiesen annähernd horizontalen Verlaufsabschnitt selbst,
• – mehrere, vorzugsweise mehr als drei, vorzugsweise innerhalb eines Zeitintervalls von maximal 10, 30 oder 60 Minuten aufeinanderfolgende Anstiege und Abstiege des Verlaufs,
• – Überschreitungen einer LFD von mehr als 0,1 mW/m2, vorzugsweise von mehr als 0,05 mW/m2, besonders bevorzugt von mehr als 0,01 mW/m2,
• – annähernd gleichbleibende LFD bzw. im Falle einer grafischen Darstellung im Wesentlichen parallel zur Zeitachse verlaufende Abschnitte des HFI-Verlaufs,
• – Absenkungen und/oder Anstiege des HFI-Verlaufs bzw. der LFD, welche gegenüber unmittelbar vorangehenden und/oder nachfolgenden HFI-Verlaufsabschnitten vorzugsweise eine Differenz von mehr als 10%, besonders bevorzugt von mehr als 30% aufweisen; als, unmittelbar vorangehend kann hierbei ein Zeitraum von mehreren, z. B. 1–30 Minuten, vorzugsweise von 0–60 Sekunden, besonders bevorzugt von 0–10 Sekunden definiert werden.

As primary significances 1 In the HFI curve, in particular the following characteristics are detected:

• Maximum values or LFD peaks which, in the case of a graphical representation, can be seen as approximately needle-shaped or jagged amplitude fluctuations,
• Approximately step-shaped course sections, in this case preferably the start and / or end sections of a respectively approximately horizontal or a constant LFD-indexing course section of the staircase form and / or the same approximately horizontal course section itself,
• A plurality of, preferably more than three, successive increases and decreases of the course, preferably within a time interval of a maximum of 10, 30 or 60 minutes,
• Exceeding an LFD of more than 0,1 mW / m2, preferably of more than 0,05 mW / m2, more preferably of more than 0,01 mW / m2,
• Approximately constant LFD or, in the case of a graphical representation, substantially parallel to the time axis extending sections of the HFI curve,
• - Reductions and / or increases in the HFI curve or the LFD, which preferably have a difference of more than 10%, particularly preferably of more than 30% compared to immediately preceding and / or following HFI-course sections; as immediately preceding this can be a period of several, z. 1-30 minutes, preferably 0-60 seconds, more preferably 0-10 seconds.

As primary significances, not necessarily only HFI / LFD peaks or the occurrence of the aforementioned characteristics need to be defined. On the other hand, a phase of the HFI course in which the LFD is particularly low and thus less burdensome can also occur. The detection criteria for the detection of primary as well as secondary significances can thus also be defined negatively, so that a significant improvement of vegetative function parameters (preferably in case of simultaneously detected change or immission reduction in HFI course) is also detected as secondary significance, which is based on a human biologically relevant Influencing the test subject suggests.

The mathematical calculation is one explained in the following 16 - 19 illustrated HRV diagram sequence were selected from the exemplary comparison points over all time intervals, which each coincided with a primary significance in the HFI course and were compared in the sequence each with simultaneously determined HRV-FP progressions.

For analysis evaluation, detected primary significances 1 -N are weighted differently according to their specific characteristics and / or their occurrence in respective HFI frequency ranges. For example, a relatively small, spontaneous increase in HFI progression after a phase of relatively low or constant HFI stress may have a more significant effect on a co-determined HRV-FP outcome than a large increase in HFI progression in the midst of one already Variety of LFD peaks or HFI loads labeled reference time interval T _1-n . As well as the height of the LFD peaks, so can the size of the time intervals between significant changes in the LFD or between primary significances 1 -N are used as a criterion for assessment, whereby events or significances with greater time intervals are assigned a greater load prevalence than those with smaller time intervals.

Furthermore, because cellular / wireless transmission standards each have specific timings or pulse rates, it may be the case that the human organism is more sensitive to individual HFI frequency bands than to others. In analytical practice, it has been found that HFI-induced physiological reactions of the test subject take the form of secondary significances detected according to the invention 1' -N in each case individually and therefore a customary in the interior analytical field measurement of mere HFI power flux densities has only a limited significance. For example, it may happen that the test subject responds to an LFD of 0.02 mW / m ² in the WLAN frequency band with equally high physiological load as to an LFD of 0.5 mW / m ² in the GSM frequency band. An inventively proposed analysis of the HRV-FP courses in conjunction with HRV-FP reference data and HFI courses circumvents this evaluation problem and makes the respectively given, HFI-induced stress of the vegetative organism of the test subject directly visible. Objective evaluation criteria or prevalence factors for the evaluation of primary or secondary significances detected in HFI courses and / or in HRV-FP courses can be stored in algorithmic form on a storage device and used as automated processor routines.

To determine the ECG data or the HRV-FP courses, a suitable ECG recorder with associated HRV evaluation device is used. Due to its compactness and portability with simultaneous measuring precision, the HRV measuring device "HeartMan" from the company HeartBalance ^{® has} proven its worth in the present field of application. The HRV meter includes three self-adhesive electrodes E1, E2, E3 and, due to its small size, it can also be attached to the body of the test subject during sleep, for B. are glued by means of plaster strip.

As HRV-FP, all known as "time domain measures" or "Frequency Domain Measures" or modified by these parameters can be used, which are suitable to constitute a time course. It is understood that it is also possible, the standardized in the course of HRV measurement process parameters or HRV-FP z. B. by statistical operations or by a variation of the number or recording frequency of each considered measurement or calculation variables to adapt. In particular, if in the present context of SDNN is mentioned, this statistical characteristic could also be replaced by equivalent or adapted statistical characteristics or "time domain measures" such. SDNNIDX / ASDNN (Mean of the standard deviation in all 5-minute segments of a 24-h recording), AVNN (SDNNIDX / ASDNN (Standard deviation of the average of NN intervals in all 5-minute segments of a 24-h) Average of all NN intervals), rMSSD (square root of the mean of the squares of the differences between adjacent NN intervals), pNNx such as. B. pNN50 (Percentage of differences between adjacent NN intervals> 50 ms)

The data collected by the HFI meter and the HRV meter (electromagnetic power flux densities and RR intervals) are recorded at different rates depending on the device. The HFI meter or dosimeter measures and stores the value of the current power flux densities approximately every 6-7 seconds, while the heartbeat or the resulting RR intervals are recorded in a much higher recording frequency. In order to make the data acquired by both measuring devices (where applicable more than two HFI and / or HRV measuring devices can be used) comparable, they must be converted to the same time units so that the same number of measured values are available in each of the two signal channels place. For this purpose, the reference time interval for both signal channels or for both (HFI and HRV-FP) gradients is divided into a plurality of time blocks or sub-time intervals. The duration of a sub-time interval can be configured variably as an input parameter depending on the required accuracy and characteristic of the analysis. It is recommended to create sub-time intervals of 1-10 seconds, preferably 3-8 seconds, to preserve as many RR interval readings as possible provided by the ECG meter. For extra accuracy, overlapping sub-time intervals can be calculated. Subsequent to the sub-time intervals, various operations (eg, selection / detection of maximum, minimum, median, average, or delta values) may be applied to provide the most interesting information, depending on the significance of the signal to be considered to capture. In the case of the HFI curve, for example, the maximum value of the sub-time intervals is selected in each case. In order to ensure a high resolution and detection accuracy, the sub-time intervals in the present embodiment are each 7 seconds.

As secondary significances 1' In the HRV-FP curve, all events or course segments can be detected which, when plotted, have a pitch k> 1 or a pitch angle a of more than 45 °, preferably of more than 70 °, particularly preferably of approximately 90 ° the time axis (slope k → ∞) or one or more approximately needle-shaped elevations of the HRV-FP curve have. Analogous to the assessment of the HFI curve, suitable detection criteria can also be defined and weighted differently with regard to the HRV-FP curve. As a detection criterion for secondary Significances can z. B. an exceeding of the average HRV-FP curve or of the respectively in a sub-time interval analyzed HRV-FP history section immediately preceding and / or subsequent HRV-FP history section by at least 20%, 30%, 50% or 100% be set. Since the autonomic functions of humans are governed by a plurality of autonomous rhythms or are related to each other, any arrhythmia or distortion ("distortion") of an HRV-FP course that deviates from empirical empirical values can be defined as a detection criterion for secondary significance. in particular also all the events represented by the following analysis examples.

Numerous modifications and specifications are possible in order to optimize the validity of the analysis method according to the invention or to minimize the required measurement time. In a specific variant of the method, for example, a specific initiation of a high-frequency immission acting on the test subject can take place by means of a specially provided test high-frequency source and thus one or more temporally determined primary significances 1 -N are generated. A simultaneously determined HRV-FP curve becomes coincident secondary significance 1' -N 'checked. The radio frequency emissions (or emissions) generated by the test radio frequency source may preferably have realistic power flow densities, as typically provided by transmission equipment in accordance with common radio frequency standards such as GSM, UMTS, LTE, WLAN, Bluetoooth / IEEE 802, DECT and the like. In general, especially in the domestic area occur correspond or simulate those high frequency sources.

Also, the test subject during one or more time intervals by a preferably on all sides of the test subject surrounding shielding device such. B. shielded by electrically conductive metal fabric of external high-frequency emissions of the environment. The shielding device is either opened for a defined period of time or, with the shielding device closed, a test high-frequency source, for example a mobile telephone, arranged within the shielding device is activated once or several times for a defined period of time during the shielding interval, the HRV detection time determined simultaneously. FP test of the test subject to the / by the test radio frequency source generated primary significance (s) coincident secondary significances or on occurring during the period of the opening of the shielding secondary significances is checked.

It has z. B. proven, the test subject 5-10 min. to expose to HFI in a first phase with the shielding device still open, in a second phase immediately following, the shielding device for 5-10 min. close and in a subsequent third phase the shielding device for 5-10 min. open again (the specified period may vary). In this case, in one or more of the listed phases activation of the test radio frequency source, z. B. a mobile phone made. Such activation preferably takes place without the knowledge of the test subject, ie either automatically or by a third person. The test subject wears a blindfold during testing to prevent falsification of the test result by visual stimuli.

In a further development of the analysis method according to the invention, provision may be made for a vibro-acoustic relaxation program to be carried out before the test subject is tested, preferably during the first phase described above, in which the test subject is simultaneously exposed to two different but mutually correlated actions: on the one hand a vibration sequence, generated by a lying with mechanical-electrical actuating means vibratable lying device, on the other hand, an acoustic sequence. In this case, the vibration and / or the acoustic sequence is modulated onto a frequency corresponding to the BRAC (basic rest-activity cycle). Existing experiments have shown that a particularly high detection accuracy of HFI-induced secondary significances is ensured after application of an objective BRAC rhythm-based vibro-acoustic relaxation program. As demonstrated in a pertinent publication, emotional-functional stress parameters / EFSP improve significantly after performing the vibro-acoustic sequence ( Matissek, 2011: "Follow-up of mood change in 20 subjects using acoustic rather than light-induced stimulation of the base - resting activity - cycle / BRAC" ). The use of such relaxation program already known from the prior art has therefore proven to be particularly advantageous in combination with the analysis methods proposed according to the invention and focused on HFI loads. By exposing the test subject to two different but correlating sensory stimuli, any further external stimuli that may affect the identification of the HRV-associated autonomic function parameters can be largely eliminated.

According to a further variant of the method, depending on the determined frequency and / or standard deviation (size) of the HFI-induced secondary significances, a z. As stated in percent, gradual assessment of the likelihood of the presence of an incompatibility of high-frequency emissions. Also, a gradual evaluation of the vegetative regulatory ability of the test subject against HFI-induced secondary significance can be made by comparison with HRV-FP comparative data from external test series. The analysis according to the invention or the comparison of the event histories of the HFI and / or HRV-FP profiles over a selected reference time interval can each be automated, semiautomatized or even manual. An illustrative comparison results, in particular, in the case of a graphical superimposition of HFI and HRV-FP progressions, which have been synchronized at least in sections and calibrated to at least one common reference time interval T ₁ -n.

The following diagrams, exported as digital graphics, of the comparative analysis always start with the same start time of the comparison period on the left side. Subsequently, all other graphics, in particular HRV-FP progress diagrams and activity logs, can then be positioned exactly one below the other. To give the client or the test subject a quick and plausible overview of the correlations detected in the evaluation or the correlation between primary and secondary significances 1 -n, 1' -N, the history diagrams may each be provided with preferably orthogonal to the time axis, thus in the present embodiments vertical grid lines. Equidistant raster lines can be provided as well as specific raster lines, which can be provided at times of relevant events or when primary and secondary significances occur 1 -n, 1' -N 'have been set in the HFI or HRV-FP courses and according to 7 can also extend over several diagrams. In this way, the immediate correlation of a trigger in the HFI curve with corresponding effects in the HRV-FP curve is illustrated. The HRV meter and / or the HFI meter may also be equipped with a GPS locator function to provide secure evidence of the local relationship of the assay result.

As an alternative to establishing an immediate correlation of a trigger in the HFI curve with reactions in the HRV-FP, the determination of such a correlation can also be made indirectly:
In a special variant of the method, an automated comparison of the currently determined HRV-FP profile with characteristic, HFI-induced secondary significances or HRV-FP reference data or control sequences is carried out, each of which is based on a current experimental setup or at least one of these preceding experimental setups the current test subject or third test subjects were determined and are now held in the form of digital or analog data, preferably in the form of graphical progressions, alphanumeric or algorithmic data, functions or function graphs on a memory device. Upon detection of a defined, at least partially present agreement of the currently determined HRV-FP curve with characteristic, HFI-induced secondary significances or deviation of the currently determined HRV-FP curve from the HRV-FP reference data, once again a positive evaluation of the presence is obtained a human biologically relevant influence on the vegetative regulatory mechanisms of the test subject made. All arrhythmias or secondary significances documented on the basis of subsequent embodiments 1' Therefore, -n could also be detected by the aforementioned recognition algorithms or by comparison with characteristic, HFI-induced secondary significances or HRV-FP reference data obtained at an earlier time in the course of an HFI correlation analysis.

In the diagram according to 8th is a heart rate history of the test subject over a period of eight hours shown. In the history diagram ( 7 ), the simultaneously measured HFI curve in the mobile network GSM900 is considered. The graphs were calibrated to the sleep phase of the test subject, beginning with mark A and ending with mark D (sleep end).

The diagram according to 8th as well as all below described HRV-FP profiles can each be provided with activity logs, so that spontaneous events such. B. a possible waking up and interrupting the sleep phase can be associated with corresponding abnormalities in the HRV-FP courses or in this case detected secondary significance as not HFI-induced excreted from the evaluation. According to 8th For example, sleep was abruptly interrupted at mark B at about 4:00 am, but resumed shortly thereafter. In the morning, the test subject wakes up at mark C (about 7:00 am), but continues to sleep until mark D, but then wakes up again and gets up (7:30 am).

Excursus on heart rate variability (HRV): The heart rate diagram according to 8th The apparent dark main line of the heart rate corresponds to the average of the heartbeats represented by the R-waves in the Time interval of one minute. However, there is also a metrological determination of the interval duration of the individual heartbeats relative to each other, which is shown in diagram form as a fine, in transverse to the time axis or vertical direction above and below the dark average curve protruding lines. Lines extending above the average curve each indicate a shortening of the heartbeat interval between two heart beats, and lines extending below the average curve each extend the heartbeat interval. One according to 8th The visualization of the heartbeat intervals shown in chronological order is shown in an approximately tubular, meandering course. The average and interval duration curves originally available in color or in light-dark color were resolved into pure black-and-white puncturing for the present documentation. The resultant pixelated representation thus does not correspond to the original progression graphic (this note also refers to all figures described below which are originally color-coded in the original).

• – „Maschinentakt”: Die Herzschläge folgen einer exakten Regelmäßigkeit und zeigen damit ein eingeschränktes Anpassungsvermögen des Organismus. Es finden sich nur sehr kurze senkrechte dünne Linien über und unter der Durchschnittskurve (niedere HRV).
• – Normale Variabilität: Die Variation der Intervalldauer von Herzschlag zu Herzschlag zeigt die Regulationsfähigkeit des Organismus. Je weiter die gegenständlichen Linien über und unter die mittlere Durchschnittskurve reichen, desto schneller und flexibler passt sich das Herz an innere und externe Einflüsse an (hohe HRV).
• – signifikante Arrythmien/Extrasystolen (Doppelschläge): Dabei wird die Intervalldauer zwischen zwei Herzschlägen extrem verkürzt, was sich in der grafischen Auswertung sich in einer senkrechten langen Linie über der Durchschnittskurve und somit in Form einer signifikanten Abweichung der angrenzenden Variabilität zeigt. Je länger die gegenständliche Linie ist, desto stärker weicht sie von der durchschnittlichen Intervalldauer ab.

Basically, one can describe three characteristic courses in the heart rate diagram:

• - "Machine cycle": The heartbeats follow an exact regularity and thus show a limited adaptability of the organism. There are only very short vertical thin lines above and below the average curve (lower HRV).
• - Normal variability: The variation of the interval from heartbeat to heartbeat shows the regulatory capacity of the organism. The further the representational lines reach above and below the mean average curve, the faster and more flexible the heart adapts to internal and external influences (high HRV).
• - Significant arrhythmias / extrasystoles (double strokes): The interval between two heartbeats is extremely shortened, which is shown in the graphical evaluation in a vertical long line above the average curve and thus in the form of a significant deviation of the adjacent variability. The longer the objective line, the more it deviates from the average interval duration.

In the course of the method according to the invention, it is provided that the chronological progression of the heart rate is analyzed and in this case the occurrence of short-term arrhythmias, ie irregularities with respect to chronologically preceding and / or subsequent heart rate progression sections as secondary significance 1' -N is detected. The detection accuracy can be determined parametrically or according to a gradually determinable sensitivity factor, so that arrhythmias of greater severity can be recognized as secondary significance, such as weak, masked, distorted or partially compensated arrhythmias. For this, the heart rate curve as well as other HRV-FP curves can be viewed under any macro or micro scale or scaling. The same applies to the detection of irregularities in other HRV-FP described in the present application. As a particularly significant arrhythmia in heart rate progression, extrasystoles are preferably detected.

To the event history in the courses according to 7 and 8th : The time interval T1 shows a very low or negligible LFD in the HFI diagram according to 7 and also no extrasystoles in the heart rate diagram according to 8th , In the time interval T2, a sudden increase in LFD sets in, which is characterized by a chaotic sequence of short and substantially constant LFD (staircase shape) and small HFI peaks. It has been shown that such a scenario exerts the strongest stimulus on the organism, which reacts immediately with a massive increase in the heart rate. The intervals T3 to T5 show the typical course of constant LDF (staircase shape). This always begins and ends with an LFD peak (primary significance 2 - 5 ), which are also regularly in the simultaneous occurrence of extrasystole and thus a secondary significance 2 ' - 5 ' in the heart rate diagram shows. The organism of the test subject is less activated by these LFD tips and the subsequent consistent LFD in that it immediately compensates extrasystoles for normal variability (each time for nearly equal extension of the heartbeat interval) and for the remaining lesser variations in LFD overall Variability of HRV. In the process, corresponding stair patterns also occur in the course of the heart rate ( 8th ).

Time interval T6 shows substantially similar events to time intervals T3 to T5 with respect to HFI and LFD characteristics, respectively, except that the time between the constant LFD and the LFD peaks is about half as long as before, with the LFD peaks slightly larger. This also leads to the increase of extrasystoles in exact chronological order and an increase of the heart rate ( 8th ). Time interval T7 is again characterized by a relatively low LFD but with 2-3 LFD peaks. Overall, the heart rate flattens and resembles the externally triggered extrasystoles (LFD peaks or primary significances 14 . 15 ) by the increased normal variability. In time interval T8 there is an extremely short change between LFD plateaus and LFD tips, which have also increased in terms of their height. The heart rate in this case corresponds to the LFD, increases steeply due to the short LFD changes, with corresponding Increases in extrasystoles and decreases immediately as the LFD declines to rise again on subsequent LFD peaks (compare primary significance) 14 - 22 in 7 with secondary significances induced by these 14 ' - 22 ' in 8th ). This course of LFD eventually leads to a sharp, sudden increase in heart rate, which accompanies the simultaneous awakening (mark B), which will be repeated in similar scenarios, as illustrated below by marks C and D.

Time interval T9 again shows a very low and constant LFD ( 7 ), the heart rate also decreases in the sequence ( 8th ). The long vertical lines below the dark heart rate average curve indicate the active regulatory activity of the organism, which again leads to a continuous lowering of the autonomic stress level (corresponding courses are further explained by further parameters such as QPA and SDNN _RR / logRSA). Time interval T10, like time interval T2, shows a very chaotic sequence of very short, consistent LFD and high LFD peaks (see Primary Significances 25 - 36 in 7 ). The heart rate variability is again very high in the sequence with correspondingly frequent occurrence of extrasystoles (see secondary significance 25 - 36 in 8th )

The HFI curve or the LFD in time interval T11 initially exhibits a similar characteristic as in time interval T10, but with increasing height of the LFD peaks. At mark C, a simultaneous occurrence of a very high LFD peak or primary significance will occur 39 and a temporally exactly accompanying extrasystole or secondary significance 39 ' detected. At this time, the test subject wakes up briefly, falls asleep again in the episode, and shortly thereafter again at the same time with very high LFD peaks or primary significances 40 . 41 and thus each time exactly associated extrasystoles or secondary significances 40 ' . 41 ' to wake up. It is followed by a second fall asleep and finally a final wake-up, which in turn coincides with a simultaneously detected LFD peak or primary significance 42 and an accompanying extrasystole or secondary significance 42 ' , The coincident sequence of primary significances 1 - 42 in 7 and secondary significances 1' - 42 ' in 8th shows that a coincidence in the correlation between HFI loads and the heart rate progression or the occurrence of extrasystoles is excluded. In the preceding comparison in a reference time interval of 8 hours, there is thus a correspondence of primary and secondary significances of approximately 100%. The externally induced activation of the heart rate leads to an increased burden on the heart, which is also expressed in an increased number of strokes. This can be calculated exactly from the heart rate, whose level in the time interval T ₁ (almost no HFI / LFD) was about 53 beats per minute. Since the heart rate during sleep in the REM phases (see also Explanations concerning sleep architecture and RSA) increased only slightly and in the short term, results in the reference time interval, an additional burden of the heart by approximately 6,878 strokes.

Under the conditions described, the organism can no longer follow its own vegetatively controlled rhythm but is forced to constantly compensate for the events of external, HFI-induced influences, which leads to a disturbance of the body's own regulatory processes. Such a disorder of the regulatory processes can lead to the aforementioned EMF symptoms such. As reduced performance, sleep disorders, poorer regenerative ability, mood disorders, anxiety and depression. While an assignment of such nonspecific disorders to respective EMF exposure previously had to be tested by environmental medicine outpatient clinics in complex differential diagnostic procedures, blood findings and laboratory analyzes, there is now a possibility ready to make such an assignment in time and cost-saving manner.

Additionally or alternatively, an influencing of the test subject described above by HFI can also be detected on the basis of further vegetative functional parameters (HRV-FP), eg. B. by an analysis of the already initially defined pulse-respiratory quotient (QPA) according to 13 , In the preceding HFI history diagram according to 12 in turn became a host of primary significances 1 - 29 detected, which with substantially simultaneously occurring secondary significances 1' - 29 ' in the form of QPA maxima in the QPA history diagram according to 13 correlate. The event history in detail:
Marks A, B, C, D again correspond to the events: going to bed at 23:57, interim waking up and falling asleep at 4:00, waking up at 7:00, and finally getting up at 7:30). The time interval T1 shows almost no LFD in the HFI curve according to 12 , wherein the QPA according to 13 is approximately 4: 1, which is considered to be the optimal ratio and indicator size for normally functioning regulation. In the QPA diagram according to 13 Therefore, the level is 4: 1 marked with a horizontal broken line.

In the time interval T2, a sudden increase in LFD begins, which is characterized by a chaotic sequence of short and consistent LFD and a small LFD peak (detected as primary significances 1 - 4 ). The organism responds to this scenario with a significant increase in QPA (see especially secondary significances 2 ' . 3 ' . 4 ' in 13 ). The QPA curve thus jumps from the optimal ratio of 4: 1 to 7: 1 within a few minutes, and then to 6: 1 (secondary significance 4 ' ) to sink. The intervals T3-T5 show the typical course of consistent LDF (staircase shape). This always begins and ends with an LFD peak, which also regularly shows in the simultaneous occurrence of a temporary peak in the QPA course. If the LFD falls, then immediately the QPA begins to descend. In the time interval T6, T8, T10 and T11, there are further significant correlations. Each LFD peak or primary significance in 12 leaves a QPA peak or secondary significance in 13 follow, with the QPA decreasing immediately after the LFD is reduced.

The magnitude of the QPA peaks and secondary significances, respectively, is variably related to the strength of the LFD peaks, and is particularly compounded by extremely short changes in the LFD every second or minute. For example, in the time interval T8, there is an extremely short change in the HFI curve or at several short-term successive LFD peaks, so that the QPA curve in the case of secondary significance 14 ' is higher (8: 1) than LFD tips of equal strength (eg, secondary significances 6 ' . 8th' which "only" lead to an increase of the QPA to the ratio 5: 1. In the time interval T11, this correlation is again clearly evident at the highest LFD peaks (primary significances 27 - 29 ) which also lead to the highest QPA ratios of approximately 9: 1 (secondary significances 27 - 29 ). Again, there is a significant match between LFD peaks in the HFI curve ( 12 ) and QPA maxima in the HRV-FP course ( 13 ). Also in this comparison, in a reference time interval of 8 hours, there is a match between primary and secondary significances of approximately 100%.

The diagrams according to the 14 and 15 illustrate analogous to the preceding 12 and 13 again an analysis of the QPA progression, but now with regard to lowering of the QPA (the observed period T1-T11 or the HFI and QPA gradients are with those according to 12 and 13 ident, but with new, topic-specific markings).

This is followed by a temporal comparison of all time intervals with low LFD with the QPA course. As already mentioned above, in a healthy organism a QPA of 4: 1 sets in during sleep. If external events such as LFD peaks trigger an activation, the organism immediately strives for optimal balance (synchronization). The time interval T1 indicates the starting situation with a QPA of 4: 1 without HFI-related activation and thus a still normally functioning regulation. Even a consistent LFD produces no or only a small stimulus and the QPA begins to decline in the sequence. In the time intervals T3 and T4, a period of 15-20 minutes with constant LFD results in the return of the QPA profile to the ratio of approximately 4: 1. This is repeated in the time intervals T7, T9 and T11. In the time interval T8 and T10, a chaotic sequence of short-lasting LFD levels and LFD peaks leads to a permanently increased QPA of 5: 1, which is no longer undershot.

As the above example shows, a labile equilibrium relationship between organ groups such as the heart and the synchronized with this in the sleep phase breathing (but also other organ groups or autonomic control circuits are affected) is disturbed by very low LFD, which is responsible for the regeneration in the deep sleep phases is essential. Numerous studies on HRV have shown that even with very different daily outcomes of QPA, there is always a normalizing effect on the 4: 1 ratio at night, with the vegetative mechanisms during sleep as a system of highest order versus chaotic daily routines come. As it turns out, this order, visible in integer coupling, is completely lost through external, HFI-related activation and subsequent chaotic QPA. The optimal integer ratio of the QPA of 4: 1 shows up only at time intervals with almost no LFD or HFI load (see eg T1, T4, T9 with significances 1 / 1 . 3 / 3 ' . 7 / 7 ' ). Thus, not the LFD peaks and temporally corresponding QPA peaks were evaluated as primary and secondary significances in the present analysis, but rather approximately plateau-shaped courses, which correlate temporally with each other in the HFI course and in the QPA course. Such evaluation may be complementary or alternative to the detection of correlating peaks in the HFI and QPA described above.

Incidentally, the 4: 1 QPA regulatory structure occurring during sleep can be determined not only between heartbeat and respiration but also with respect to blood pressure and peripheral tissue perfusion rhythms. An analysis according to the invention with regard to correlating significances could thus also be carried out in an analogous manner under consideration of functional parameters or HRV frequency ranges related to blood pressure or peripheral tissue perfusion rhythm.

As a further HRV-associated vegetative function parameter, the time course of the SDNN _RR (standard deviation of normal-to-normal intervals) of the test subject can be used. At an in 21 The illustrated SDNN _RR run is the representation of a statistical spread over the time axis around the mean value of the heartbeat interval duration or its differences. This function parameter is suitable for representing the change in the total variability of substantially all frequency ranges of artifact-adjusted RR heartbeat interval series within a specified time interval. The standard deviation of the RR distances is calculated in milliseconds (ms). In an exemplary analysis sequence according to 19 - 21 A comparison of simultaneously detected HFI, heart rate and SDNN _RR events is performed. The with the preceding 19 and 20 related event history of the SDNN _RR run according to 21 in detail:
In the HFI history diagram according to 19 in turn became a host of primary significances 1 -N detected, which with substantially simultaneously occurring secondary significances 1' -N in the heart rate history diagram according to 20 as well as with corresponding secondary significances 1'' -N '' in the SDNN _RR trace diagram according to 21 coincide in time. This is followed by a time comparison based on the previously performed graphical evaluation regarding extrasystoles and QPA. These are transferred to the SDNN _RR record and compared in the time intervals T1-T11.

The time interval T1 shows (almost) no LFD and thus a negligible HFI-related influence. The SDNN _RR is increased due to the increased variability from the heart rate. During undisturbed sleep, fluctuation in the course of the natural fluctuation range, accompanied by a QPA in the optimal ratio 4: 1, a high RSA, which shows in the form of the logRSA course (second curve below the SDNN _RR run and a flat, In practice, the SDNN _RR and logRSA gradients, or the areas enclosed between corresponding graphs and abscissas corresponding to the time axis, are color-coded, for example, blue and violet, but this color coding has been described in US Pat 21 again resolved into a black and white dot graph. The areas indicated by black oblique hatching (= high RSA) of the logRSA curve running below the abscissa are associated with deep sleep phases. Here, a deep sleep phase is just ending in time interval T1, while a new deep sleep phase begins (again hatched) between time interval T2 and T3, but is interrupted by the in 19 apparent increase in HFI load or LFD tips can not properly form.

Between time intervals T2 and T6, typical staircase courses in the LFD (shown in FIG 19 as primary significances 1a . 2a . 3a ) with significant peaks at the beginning of each staircase and then temporarily (about 15 minutes) of consistent LFD. These LFD tips (plotted in 19 as primary significances 1 . 2 . 3 . 4 ) match coincidental extrasystoles in heart rate progression 20 (plotted as secondary significances 1' . 2 ' . 3 ' . 4 ' ), along with increased autonomic activation in the SDNN _RR . Simultaneously, in the phases of steady LFD, the lowering of the QPA and a corresponding reduction of the SDNN _RR (plotted in FIG 21 as secondary significances 1a ' . 2a ' . 3a '' ). Noteworthy are also analogous flattenings of the heart rate course (plotted in 20 as secondary significances 1a ' . 2a ' . 3a ' ) each shortly after the end of in 19 marked extrasystoles 1 . 2 . 3 or during the horizontal sections of the designated LFD staircase form (= primary significances 1a . 2a . 3a ).

The time intervals T2-T6 were determined in the SDNN _RR flow chart according to 21 marked with a horizontal black line as the 1st (activation) level. In time interval T6, the duration of the constant LFD ( 19 ), which is accompanied by an increase in the SDNN _RR (increased activation) and decreases again to the first level by a longer lowered LFD in time interval T7. In time interval T8 there is accumulation of primary significances (in 19 each marked by arrows) in the form of extremely short changes, ie swelling and decimation of the LFD with many significant LFD peaks, which in accordance with 20 each corresponding extrasystoles result in and increase the QPA significantly. In the SDNN _RR run, the significant increase in autonomic activation is seen as secondary significance (see indication arrows in 21 or an oblique line drawn between the 1st and 2nd levels to illustrate the SDNN _RR increase).

In time interval T9 the LFD decreases according to 19 again to a very low level and it begins again to form a deep sleep phase (see hatched area in the logRSA course). At the same time, in this time interval with the onset of the deep sleep phase, a compensation initiated by the autonomous organ system sets in for the previous activation, which shows itself in the heart rate progression by numerous interval extensions (vertical thin lines below the thick average curve). In the SDNN _RR , this initial activation shows a lowered heart rate, optimal QPA 4: 1, and high RSA (shaded area). The SDNN _{RR will} decrease until the next increase in LFD ( 19 ) on a in 21 marked level 2 and thus differs completely from externally induced activation. With time interval T10 then begins a massive increase and chaotic course of the HFI ( 19 ) with numerous LFD tips, which exerts the strongest attraction on the organism and at the same time raises the SDNN _RR to its highest (3rd) level. Also a short-term decline of the LFD according to 19 Autonomous activation of an organism does not sink any more below the 3rd level.

Especially in the present time interval T10, it becomes clear how the test subject's organism tries to maintain the deep sleep phase, visible in the toned area in the logRSA course and the heart rate with numerous vertical lines, but this internal compensation attempt becomes fast due to the primary significance changing LFD massively impaired or chaotized with numerous needle-shaped peaks. The organism considered in the previous analysis sequence shows phases such as under-day activation and resolution sleep architecture.

In particular, a heart rate variability (HRV) frequency information data set that can be represented as a "spectrogram" can be analyzed as an HRV-associated autonomic functional parameter. The spectrogram already explained above represents the activation which reaches over a frequency bandwidth of substantially 0 to 0.5 Hz in each case to a specific frequency range of associated vegetative organ functions of the test subject 9 - 11 described analysis sequence described. In this case, a comparison of the HFI curve calibrated with respect to the time intervals T1-T11 is determined according to FIG 9 , according to the spectrogram 10 and the VQ history according to 11 performed.

The loss of the sleep architecture of the test subject already mentioned in the previous evaluation of the SDNN _RR run, according to high-frequency immissions, can be represented here in a significant way. It should be noted in advance that the interaction of breath, blood pressure and blood flow is an expression of the vegetative control of the heartbeat (VQ: LF / HF), whereby the vagus (HF: rest and recovery) in particular causes the modulation of the respiration (RSA ) and blood pressure and circulation of sympathetic control (tension) subject.

According to 9 shows in the time interval T1 a very small and therefore negligible LFD. According to a good sleep architecture, a deep sleep phase (indicated as arcuate line / indication P1) with clearly pronounced RSA (frequency range between 0.2-0.3 Hz) is shown. The BRAC (basic resting activity cycle), which is also already defined at the beginning, shows the sinusoidal or convex course of the RSA (= deep sleep phase) alternating with the REM phase. This change usually takes place with 90-120 minutes deep sleep phase (recovery phase) and 5-20 minutes REM phase (activity phase, the sinusoidal course of the BRAC is also in 27 drawn in an exemplary way). In the VQ this already shows a clear reduction of the activity level with a still slightly dominant sympathetic nervous system (ratio LF / HF = 2: 1).

The color coding used in the following embodiment blue, white, yellow, orange, red (each frequency-specific pixels shown in the spectrogram also has depth information regarding activation or amplitude strength - see also the initially defined assignment) according to the three-dimensional information content of the spectrogram correspond one of the practice taken original evaluation. The spectrograms converted into the monochrome or pure black-and-white pixelization according to the present invention 10 . 22 and 26 can only give a rough impression of this color gradation.

to 10 In the event interval T1 of the spectrogram, predominantly blue (in 10 : dark) with some white amplitudes, indicating an irregular depth of sleep. In contrast to the REM phase, there are no red amplitudes (in the frequency range between = and 0.1 Hz) with respect to peripheral tissue perfusion (0.017 Hz) or activation of the neuronal system with red amplitudes to just below 0.1 Hz Strong activation in the range of 0.1 Hz, which corresponds to the blood pressure range, but usually also in the REM phase is omitted and only in the daily rhythm (waking phase).

Time interval T2 lies between the deep sleep phases P1 and P2, the in 10 drawn REM phase in the spectrogram with a first increase in LFD (see HFI course according to 9 ) coincides. As a result, the blood circulation sets in and in the VQ shows an increase in the activity level (sympathetic), which is the normal course of change between deep sleep and REM phases to assign. However, the HFI course shows a short change, ie a swelling and deceleration of the LFD and some acicular tips (primary significances, not indicated here by Pos. Nrn.), Which irritation on the vegetative system are detectable. This is also reflected in an unnaturally strong increase in VQ (over 10: 1) in 11 and in the blood pressure activation (red amplitude) just above 0.1 Hz in the spectrogram according to 10 , Such a blood pressure activation normally would not arise even in the REM phase and is therefore already detected in the method according to the invention as a secondary significance. This is clear in comparison to the second REM phase in the time interval T6, in which the HFI curve again shows a staircase form with a constant (approx. 10 min.) Constant LFD, which exerts less of a stimulus on the organism. There is no activation of blood pressure in the frequency range of approx. 0.1 Hz, the VQ does not rise so high (VQ = 5: 1), the general activation in the spectrogram also falls 10 much lower in the frequency range below 0.1 Hz.

Also worth mentioning are three small peaks occurring in the time interval T6 in the VQ progression diagram, which reach an LF / HF level of approximately 10: 1. These peaks or needle-shaped elevations are detected as secondary significances (in 11 indicated by three black dots without pos. no.). As a comparison with the event history in the HFI progress diagram shows, these secondary peaks also fall into 9 also marked by three points in time primary significances (acicular peaks or striking endpoints of LFD staircase forms) together and are therefore evaluated as HFI-induced. This example shows how even relatively small LFD peaks or primary significances can possibly lead to significant reactions in HRV-FP processes.

The time intervals T2 to T5 show according to 10 the course of a second deep sleep phase P2. The RSA is still clearly formed in the spectrogram at the beginning, as can be seen from the white amplitudes at the beginning of the sinusoidal or arcuate course (in the black-and-white resolution according to US Pat 10 only poorly recognizable), but then decreases successively in time with the occurrence of several LFD peaks or primary significances 1 . 2 . 3 . 4 according to 9 ). Although a LFD staircase pathway activates the sympathomus less than described above, the regular intervals between the LFD peaks and primary significances act as a meta-rhythm which attenuates the respiratory rhythm in the heart rate (RSA). This is again clear, in contrast to time interval T10, in which the chaotic HFI course with numerous LFD peaks activates the organism again, but the RSA, although fragmented, has remained intact in its intensity as at the beginning of the deep sleep phase P4.

This also corresponds to the previously described evaluation of the SDNN _RR in the logRSA curve ( 21 ): The hatched areas of the logRSA path coincide with the distinct RSA, while the logRSA decreases significantly during the periods of attenuated RSA. The consequence of the attenuation of the RSA is then seen at the beginning of the deep sleep phase P3 in the time interval T7 (FIG. 10 ), in which the RSA is barely recognizable and despite low LFD in the HFI curve according to 9 in the VQ ( 11 ) shows only a below-average reduction, especially since the tone of the vagus is weakened.

The VQ in the deep sleep phase P2 shows a largely lowered course over the time intervals T2-T5. However, the VQ depression is massively interrupted four times with spontaneous activation peaks up to a ratio of 10: 1 (plotted in FIG 11 as secondary significances 1'' . 2 '' . 3 '' and 4 '' ), which coincide completely with LFD peaks in the HFI curve according to 9 occur (indicated as primary significances 1 . 2 . 3 and 4 ).

At the same time, a very strong activation at the onset of blood flow up to the activation of blood pressure is also evident (seen as red-yellow-white color coding in the spectrogram; in the present black and white pixel graphic according to FIG 10 recognizable only as white columns or tongue-shaped pixel accumulations in phase P2; the regions of these - and also the other - bright pixel accumulations lying close to the abscissa or between 0 and 0.05 Hz are marked red respectively in the original spectrogram or with a color corresponding to the highest activation. However, this red coding comes in the present 10 was not properly expressed, but was in the course of the black-and-white transformation of the graph also converted into a predominantly black pixel accumulation as the oppositional blue areas, which indicate a minimal or non-existent activation of respective organ systems).

As in the VQ history according to 11 can be seen, between the aforementioned LFD peaks or primary significances 1 - 4 At the beginning, the vagus, which was trying to compensate, still prevailed over and over again (step-shaped course in the VQ diagram / 11 ), according to 10 even leads to a deepening / improvement of the deep sleep phase - in comparison to the previous deep sleep phase P1 with significantly less white coded amplitudes. Due to the four-time significant interruptions in the time intervals T3-T5 but the organism does not reach such a deep depression of the sympathetic that the vagus outweigh and would correspond to an intact sleep architecture. A third deep sleep phase P3 beginning in the time interval T6 (after the second REM phase) points in the spectrogram according to FIG 10 almost no RSA activation on (recognizable in the dark range between 0.2 and 0.3 Hz). As previously described, RSA has been attenuated by the HFI-induced, staircase-shaped metarhythm, and after the REM phase can not be re-amplified in similar LFD staircase patterns or primary signatures in the HFI course. In the time interval T7, although a decrease in the LFD, which is to be recorded in the HFI curve, occurs with a decrease in the VQ 11 but this reduction remains below average. In this deep sleep phase P3, the time course, which would have continued due to the BRAC cycle explained above (a deep sleep phase usually lasts 90-120 min.) Abruptly by the beginning of the time interval T8 very strong increase in LFD or more in 9 apparent needle-shaped significances were aborted. In the spectrogram according to 10 again shows a simultaneous activation of the frequency range associated with the circulation (0-0.1 Hz) and a strongest in the previous event history activation of the blood pressure associated frequency range (range around 0.1 Hz).

Shortly after 04.00 o'clock (time interval T8) shows in the HFI course according to 9 again a short low LFD, what with a simultaneous decrease of the VQ in 11 and the transition to a fourth deep sleep phase P4 goes along. At this point it should be noted that not only elevations or peaks, but also pre-defined subsidence, thus temporary "improvements" of the autonomic balance as secondary significance in the HRV-FP course, z. B. can be detected in the VQ curve, if a correlation with detected or defined as characteristic HFI events / history sections is given. The exact location of the following three REM phases is now difficult to detect from the spectrogram, since a strong LFD peak or primary significance in the HFI peak coincides with the transition between deep sleep phase P3 and P4 (change from time interval T8 to T9). Course according to 9 occurs. In the time interval T9, a longer time span (almost) without LFD appears for the first time, so that at the same time the development of a stronger RSA in the spectrogram ( 10 ) and a significant decrease in VQ until the first prevalence of the vagus in 11 ,

In the fourth deep sleep phase P4 there is again a normal course of the sleep architecture, without activation of the frequency ranges circulation or blood pressure, with predominantly blue coded, ie weak amplitudes. However, the deep sleep phase P4 is abruptly disturbed from the time interval T10. If it begins in the spectrogram at around 4:30 am with distinct RSA without red, but predominantly only blue-coded amplitudes (with regard to color coding or its graphical representation, the above statement still applies), then the deep sleep phase has only 45 minutes. from the usual 90-120 min. (BRAC) when they coincide with the onset of a sharp increase in LFD in the HFI curve at approximately 5:15 pm (seen as a vertical upward comb-shaped primary significance in 9 ) is suddenly interrupted by a strong activation of the sympathetic massive bloodstream up to the blood pressure activation (seen in the secondary significance detected, columnar activation in the spectrogram, the activation is sufficient here according to 10 over more than 70% of the entire frequency bandwidth of the spectrogram or in the purely computationally evaluable HRV frequency information data set).

This strong activation of the entire organism then stops at the same time as the chaotic sequence of LFD peaks or primary significances in the HFI course 9 until 7:15. In the spectrogram the RSA / frequency range 0.2-0.3 Hz) is completely torn in its previous arcuate course, the course of the BRAC (change of deep sleep and REM phases) is completely lost. Only at 07.15 clock in time interval T11, as it was for about 20 min. According to the spectrogram, a fifth deep sleep phase P5 can prevail - with short exposure of the blood circulation, no blood pressure activation (recognizable by the blue or dark coding of the pixels arranged in the corresponding frequency ranges) and an in 11 apparent, marked lowering of the VQ with preponderance of the vagus.

For the evaluation of the HRV frequency information data set or spectrogram or for recognition as secondary significance, suitable criteria or threshold values can be defined. For example, it can be considered secondary significance if the amplitude strength of the respective frequency ranges increases by more than 20%, 30%, 50%, 70% or 100%.

In one 22 - 29 The analysis variant represented in the HRV frequency information data set or spectrogram is considered to be the RSA or its course lying in the frequency range from 0.2 to 0.3 Hz, with a substantially convex curve of activated organ functions in the upper end of the frequency bandwidth 22 indexed by white bows 1' - 5 ' , is evaluated as HFI-induced secondary significance. The convex curves or secondary significances 1' - 5 ' extend here over time intervals between 30 and 120 minutes and also show a decreasing activation or amplitude strength (in the present 22 due to black and white transformation only inadequately recognizable, in the original spectrogram, however, clearly as Passer-becoming or as increasingly towards white coding tendencying coloration apparent). In the present RSA frequency range, in the case of significant HFI loads, the previously described chronobiological rhythm of the BRAC (basic resting activity cycle) is often reflected, which means activation by the sympathetic and parasympathetic nerves - and thus not by the respiratory center. displays. In sleep, the BRAC expresses itself as a change between deep sleep and REM phase (see also black, arcuate indication lines in 24 and 25 ), wherein the REM phases also show on the lowered arc shapes or end regions of the convex RSA course sections.

A like in 22 apparent reflection of the BRAC in the otherwise substantially bar-shaped or parallel to the time axis - in a frequency range of usually about 0.25 Hz - running RSA course during sleep (see 26 ) is to be regarded as a pathological tendency and thus represents a suitable criterion for detecting an HFI-induced disturbance of the vegetative regulatory mechanisms of the test subject. An increase in RSA progression associated with weakening of the respiratory center, e.g. At approximately 0.3 Hz indicates an anticyclic slowing of respiration, a drop in RSA progression indicates an anticyclic acceleration of the breath of the test subject and thus an increasing sympathetic activation; in total, the respiratory rhythm is at least partially chaotized. Corresponding anomalies or secondary significances can also be detected in the simultaneously considered QPA course (see 25 ).

The present analysis of the spectrogram is also an example of the possibility of a time-delayed appearance or detection of secondary significances, especially since the illustrated, HFI-related RSA / BRAC curve can also be observed in a period in which the test subject does not have any relevant HFI. Is no longer exposed to stress or primary significance. 22 already shows such a scenario - was shown a about 24 hours on strong HFI exposure following sleep phase of the test subject, with no relevant LFD was measured in this second night lt. Dosimeter now. Only later, here specifically in the 26 - 27 the third HFI exposure-free night, the described pathological tendency in the RSA frequency range is lost and the RSA curve smoothes out again or exhibits an approximately bar-shaped form running parallel to the time axis (see 26 ).

Since the above-described arrhythmias or secondary significances in one or more HRV-FP are associated with relevant physiological impairments or a chaotization of the vegetative balance, upon detection of a defined number of such significances per reference time interval, a positive assessment of the presence of a human biologically relevant Influencing the vegetative regulatory mechanisms of the test subject done. In the case of prolonged nocturnal disturbances, in addition to a reduced vagal influence, cardiac vagal modulation of the cardiac frequency with reduced performance also occurs during the day. Sleep-related breathing disorders (eg, hypopneas) also include symptoms such as falling asleep and staying asleep, restlessness, nightmares, depression, and anxiety. As a further consequence, there is often a melatonin deficit, according to predominantly sympathetic activation, a disproportionate formation of cortisol in specific areas of the brain and a sinking memory in declarative memory contents.

The 30 - 34 illustrate a particularly advantageous variant for the evaluation of the HRV-FP and HFI courses. 30 shows by way of example an HFI / LFD curve, 32 a heart rate history acquired in the same reference time interval. Instead of the heart rate trace, any other HRV-FP trace may also be analyzed in a manner described below.

As already described above, the HFI and HRV-FP measurement / calculation values obtained from two different signal channels were brought to the same number by means of block operations (eg selection of maximum values) in respective sub-time intervals lasting 7 seconds here. First, both the measurement or calculation values constituting the HFI curve and the HRV-FP curve are respectively converted to a chronological gradient sequence G _HRV-FP and G _HFI , which consists of a multiplicity of successive gradients, ie difference formations between in each case two temporally successive HFI resp HRV-FP Compose measurement or calculation values: Gradient = Signal (index) - Signal (index + 1). 31 shows how absolute values of the gradients are displayed in the form of bar graphs. The analysis of the gradient sequences G _HRV-FP and G _HFI described below with regard to their correlation can be carried out on the basis of the graphic courses or also in a purely mathematical manner. The Gradients of the gradient sequences G _HRV-FP , G _HFI can also be defined and evaluated as vector variables.

The first gradient sequence G _HRV-FP corresponding to the HRV-FP profile, in the present case the heart rate, and the second gradient _sequence G _HFI corresponding to the HFI profile are arranged on both sides of a mirror axis extending parallel to the time axis or corresponding thereto. The individual, needle-shaped or bar-shaped gradients of the gradient sequences G _HRV-FP and G _HFI respectively start from the mirror axis denoted by zero (in this case horizontal) and run orthogonal to the latter. In the present embodiment according to 31 The gradients of the first gradient sequence G _HRV-FP rise more or less upward according to their absolute value, while the gradients of the second gradient _sequence G _HFI extend more or less downward according to their absolute value. With regard to the graphic arrangement and evaluation of the gradient sequences G _HRV-FP , G _HFI , a large number of options is again available. In principle, all the visualization and analysis options already mentioned above for the analysis of the HFI and HRV-FP profiles can also be used here. Something like the starting point of the gradient sequences G _{HRV FP,} G _HFI or its zero level can be spaced to the mirror axis, the charts could run in the vertical direction, color coding etc.

For better visual comparability, the gradient sequences G _HRV-FP and G _HFI can be increased or decreased with respect to their extension normal to the time axis. In the example according to 31 (also in 33 and 34 ), the size of the gradient _sequence corresponding to the HFI gradient G _HFI or the normal to the time axis, thus extending here in the vertical direction extension of the HFI gradient was increased. Such an adjustment or scaling is preferably carried out with reference to mean values of the HRV-FP and HFI courses or their gradient sequences G _HRV-FP and G _HFI within the _particular reference time interval considered.

33 shows how to the to be compared graphical gradients of the gradient sequences G _HRV-FP , G _HFI envelopes (trend curves) were created. In this way, any correlation in the time course of the gradient sequences G _HRV-FP , G _HFI or the simultaneous occurrence of short-term primary and secondary significances can be represented particularly clearly.

In an alternative representation variant according to 34 For example, the gradient sequences G _HRV-FP and G _HFI or their envelopes are superimposed on each other so that any correlations between the two gradient sequences G _HRV-FP and G _HFI or HRV-FP and HFI curves are made directly visible. As in particular in 34 visible (see also 31 and 33 ), the gradient sequences G _HRV-FP and G _{HFI run} over long distances approximately synchronously or at least with a conforming tendency. Significant maxima in the first gradient sequence G _HRV-FR coincide in terms of their temporal occurrence with corresponding maxima in the second gradient sequence G _HFI representing the HFI emissions acting on the test subject.

In the 33 and 34 The envelopes shown in each case affect the peaks of the gradients facing away from the zero level or the time axis, respectively, or are nestled against them. This z. B. to the designated gradient peaks virtual tangents are applied to which the envelope approaches each asymptotically or finds a reversal point. In the present evaluation, the gradients of the gradient sequences G _HRV-FP and G _HFI were computationally generated by means of Nadaraya-Watson kernel regression.

Between the gradient sequence G _HRV-FP corresponding to the HRV-FP profile and the gradient sequence (G _HFI ) corresponding to the _HFI or LFD curve, a correlation value, e.g. B. determined as a weighted cross-correlation coefficient. The correlation value can be z. B. can be specified as a decimal value or in percent. In the present evaluation, it has been determined, for example, that two time-correlated HRV-FP and HFI gradients, ie primary and secondary significances, have a correlation of 1, while non-correlated HRV-FP and HFI gradients are assigned a correlation of 0 and exactly opposite For HRV-FP and HFI gradients a correlation of -1. In the present evaluation according to 30 - 34 In this way, a correlation of 0.656 was achieved, which is considered to be a remarkable example of the presence of a human biologically relevant influence on the vegetative regulatory mechanisms of the test subject.

In an in 16 - 18 The special evaluation regarding QPA / RSA / respiratory rate, the physiological role of these functional parameters and the HFI-related influence on sleep will be discussed illustrated. As already mentioned, the oscillation of the RSA in the spectrogram is found in a healthy adult in the frequency range between 0.2 and 0.3 Hz. With the onset of RSA, the heart rate shows a respiratory-synchronous change in the heart rate and the RR intervals shorter when inhaled can be accelerated and extended (slowed down) when exhaling. It is mainly mediated by the parasympathetic / vagus, which becomes active when exhaling. In addition to the spectrogram, RSA is also present in the logRSA, which accurately calculates this rapid respiratory-induced change in RR interval changes, thus reflecting the rate of cardiac rhythm modulation by respiration (see also hatched areas of logRSA) 21 with the RSA in the spectrogram according to 10 ).

The function of RSA is to optimize the gas exchange during each respiratory cycle by timing heartbeats for pulmonary blood flow to respiratory ventilation (through the breaths). The strength of RSA depends on: tone of the parasympathetic nervous system (modulation capacity of the nervous system), respiratory rate, tidal volume.

If the depression of the tidal volume has only a slight influence on the amplitude or strength of the RSA, and if the direct influence of the radio frequency on the nervous system can not be represented here, then a change in the respiratory rate directly affects the RSA and also has a specific dependency on functional requirements (eg hypopneas). An increase in the respiratory rate of z. B. 11 to 15 breaths per minute increases the frequency of the RSA in the spectrogram from 0.19 Hz to 0.25 Hz and vice versa, thus showing the influence of the respiratory rate on the heart rate. A maximum of RSA amplitude is observed between 12-15 breaths per minute. Unlike RSA, which uses a vagus rhythm to beat heartbeats during a breathing cycle, QPA optimizes the number of heart beats to one breath cycle (4: 1), maximizing the vagal impact of RSA. This relationship is shown graphically by the amplitude in the spectrogram and mathematically in the following sample calculation.

Thus, RSA and QPA together optimize the performance of the heart and lungs during sleep, which occurs with strong coupling (4: 1) with a shortest possible RR interval during late inspiration (which accelerates the heart rate) and a longitudinal RR interval Beginning of the next inspiration (has slowed the heart rate again) shows. It forms a wave motion, which supports a maximum acceleration of the heart rate in the inspiration. This vegetative synchronization relieves the heart and thus provides energy for the physical regeneration. The effect of the primary significances or HFI / LFD peaks has so far been clearly evident in the comparison with the triggering of extrasystoles and the sudden increase in QPA progression (see 12 and 13 such as 16 and 18 ). This erratic increase in QPA by external activation thus destroys the ideal coupling (= 4: 1) between heartbeat and respiration occurring during sleep, depending on the HFI progression or the temporal incidence of primary significance.

In the QPA course according to 18 (see also time interval scale according to 15 ) shows in the first time interval T1 the optimal integer ratio of 4: 1, which the organism always strives for every longer depression (time intervals T4, T7, T9) of the LFD and which increases abruptly at every LFD peak and prolongs the respiratory rate , This episode has not yet been discussed and is reflected in the spectrogram as a result of the effect of the altered RSA in the HRV analysis. This relationship is explained mathematically and graphically below: The mathematical calculation is one in the 16 - 19 based on the exemplary HRV diagram sequence were selected from the example of nine comparison points over all time intervals, always with a primary significance in the form of an LFD peak ( 2 . 3 . 4 . 6 . 7 . 8th . 9 ) or with an almost LFD-free HFI phase ( 1 . 5 ) and subsequently in each case with the simultaneously determined heart rate according to 17 and the QPA according to 18 were compared.

From the following calculation, a simple extrapolation can be deduced for the additional burden of the heart, which can be exactly verified from the original measurement data of the ECG: The average number of heartbeats of the heartbeat according to the 17 marked comparison points 1' - 9 ' is 67.33 / min. In comparison, the heart rate is in the first, largely LFD-free LFD time interval T1 (see comparison point / primary significance 1 in 16 ) 53 / min. This results in an additional burden of 6,878 strokes with eight hours of sleep. With intact sleep architecture, the heart rate increases only slightly between sleep phases and REM phases, which is why this was neglected in the calculation. Points of comparison / significances 1 / 1' / 1 2 / 2 ' / 2 '' 3 / 3 ' / 3 '' 4 / 4 ' / 4 '' 5 / 5 ' / 5 '' 6 / 6 ' / 6 '' 7 / 7 ' / 7 '' 8th / 8th' / 8th'' 9 / 9 ' / 9 '' HR beats / min (average) = 67.33 / min. 53 64 70 84 60 67 62 62 84 / QPA (X: 1) 4 6.5 5.7 8.2 4 5.5 5.7 6.5 8.3 Breaths / min 13.25 9.84 12.28 10.24 15 12.18 10.87 9.53 10.12 1 min = 60 sec. 60 60 60 60 60 60 60 60 60 / Breaths / min. 13.25 9.84 12.28 10.24 15 12.18 10.87 9.53 10.12 = Duration of breath in sec. 4.5 6.09 4.88 5.85 4 4.92 5.5 6.29 5.92 1 1 1 1 1 1 1 1 1 1 / Duration of breath in sec. 4.5 6.09 4.88 5.85 4 4.92 5.5 6.29 5.92 = Respiratory rate in Hz (according to RSA) 0.22 0.16 0.21 0.17 0.25 0.20 0.18 0.15 0.17

The corresponding spectrogram according to 10 shows in the time intervals T1 and T9 (corresponding to the calculated comparison points 1 and 5 according to FIG 16 ) clearly marked white amplitudes of the RSA with sinusoidal or convex course and strong coupling. Time interval T10, on the other hand, shows, evident from the white, chaotically structured amplitudes (indicated by a black wavy line in FIG 10 ) clearly a fragmentation of the RSA in the range between 0.2-0.3 Hz (RSA / BRAC) and beyond, which is shown in the above calculation. This is the correspondence between respiratory rate and RSA. It practically indicates the above calculated respiratory rate. The objective relationship between primary significances in the HFI course and the simultaneous change in the respiratory rate or its chaotic course, which acts on the respiratory synchronous heart rate via the RSA, can therefore in a preferred analysis variant of the method according to the invention as a criterion for the detection of HFI-induced secondary significances be used.

In general, any external disturbance intermittent to physiological pulmonary respiration, or any disproportionate acceleration of the heartbeat to the respiratory rhythm, will prove to be a stressor, not necessarily pathological, but at least subliminal dyspnoea (respiratory distress). A, albeit subconscious, respiratory distress or oxygen deficiency always causes a subconscious, anxiety reaction in humans on a vegetative level. The increased occurrence of anxiety disorders in connection with fatigue / depression symptoms to be observed in clinical psychology is certainly to be recognized as a multicausal phenomenon according to the current state of knowledge, but can receive a diagnostic and preventive support by the HRV / HFI analysis method according to the invention.

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

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Claims (30)

Method for analyzing the interaction of electromagnetic high-frequency immissions (HFI) with vegetative regulation mechanisms of a test subject, comprising the following method steps: determining the course of an intensity of high-frequency immissions (HFI), preferably measured as power flux density, with respect to the test subject over a defined period of time, Determination of the variance of the time interval of successive heartbeats recorded by means of electrocardiogram measurement (ECG) and the course of the thereof, associated with the heart rate variability (HRV) associated vegetative function parameters (HRV-FP) of the test subject over a defined period of time, - wherein the HFI and the HRV-FP profiles are synchronized at least in sections and calibrated to at least one common reference time interval (T ₁ ), performing an analysis of the HFI profile in which primary significances ( 1 -N), ie temporary, ie within the reference time interval (T _1-n ), temporally limited deviations of the HFI curve from defined static or dynamic mean values or HFI reference data, in particular significant increases and / or decreases in the HFI curve over time preceding and / or subsequent sections of the HFI course, - performing an analysis of the HRV-FP course, in which HFI-induced secondary significances ( 1' -N '), ie temporary, ie within the reference time interval (T _1-n ), time-limited deviations of the HRV-FP profile from HRV-FP reference data or control courses, which are drawn from empirical data and / or dynamically generated, that is, derived from current measurement / calculation data or HRV-FP progressions, in particular significant increases and / or decreases in the HRV-FP progression compared to temporally preceding and / or subsequent sections of the HRV-FP progression; Upon detection of a defined number of coincidental, ie substantially simultaneously occurring primary significances ( 1 -N) in the HFI course and HFI-induced secondary significances ( 1' -N ') in the HRV-FP course, a positive assessment of the presence of a human biological relevant influence on the vegetative regulatory mechanisms of the test subject takes place. Method for analyzing the interaction of electromagnetic high frequency electromagnetic emissions (HFI) with vegetative regulatory mechanisms of a test subject, comprising the following method steps: determining the variance of the time interval of successive heart beats detected by means of electrocardiogram measuring method (ECG) and the course thereof determined with the Heart rate variability (HRV) of associated vegetative function parameters (HRV-FP) of the test subject over a defined period, - Comparison of the currently determined HRV-FP progression with characteristic, HFI-induced secondary significances or HRV-FP reference data respectively control progress, respectively were determined from the current experimental design or from at least one of these preceding experimental design with the current test subject or third test subjects and now in the form of digital or analog data, preferably in the form of graphic gradients, alphanumeric or al gorithmic data, functions or function graphs are kept on a storage device; Upon detection of a defined, at least partially present agreement of the currently determined HRV-FP profile with characteristic, HFI-induced secondary significances ( 1' -N ') or in the case of a deviation of the currently determined HRV-FP profile from the HRV-FP reference data, a positive assessment of the presence of a human biological relevant influencing of the vegetative regulatory mechanisms of the test subject takes place. Method according to one of Claims 1 or 2, characterized in that a heart rate variability (HRV) frequency information data set which can be represented as a "spectrogram" is analyzed as the vegetative functional parameter (HRV-FP), which frequency over a defined frequency bandwidth of substantially 0 to 0.5 Hz reaching activation in each case corresponds to a specific frequency range associated with vegetative organ functions of the test subject, in particular: 0.04 to 0.15 Hz: Low Frequency (LF) with correspondence: predominantly sympathetic activity, to a lesser extent also vagal activity, in particular the blood pressure and circulation rhythm , 0.15 to 0.40 Hz: High Frequency (HF) with correspondence: vagal activity; Assignment of, in particular, respiratory functions, preferably the respiratory sinus arrhythmia (RSA) reflecting the modulation of the cardiac rhythm by respiration, and activating respective frequency ranges or vegetative organ functions from the HRV frequency information record in the form of amplitude magnitudes preferably visualized by color coding, wherein HFI-induced secondary significance ( 1' -N ') is detected when a spontaneous activation of previously not yet or in negligible intensity activated frequency ranges or vegetative organ functions over a bandwidth of at least 0.05 Hz, preferably of at least 0.1 Hz, more preferably over a bandwidth of more than 0.2 Hz. Method according to claim 3, characterized in that it is used as a secondary significance ( 1' -N ') is detected when a short-term, preferably not more than ten minutes, more preferably not lasting more than one minute activation of previously not or negligible activated frequency ranges or vegetative organ functions, which in the case of a graphical Visualization of the HRV frequency information data set as an approximately needle-shaped, orthogonal to the time axis survey, wherein in a preferred process variant as a sufficient criterion for detection as a secondary significance ( 1' -N ') there must be a simultaneous activation of more than 50%, preferably more than 70% of the total, substantially 0 to 0.5 Hz frequency bandwidth of the HRV frequency information data set or spectrogram. Method according to claim 3 or 4, characterized in that it acts as activation or as secondary significance ( 1' -N ') is evaluated, if the amplitude strength of respective frequency ranges or vegetative organ functions increases by more than 20%, preferably by more than 50% and the analysis method can also include the possibility of weighting detected secondary significances ( 1' -N ') on the basis of respectively determined - in the case of a graphic visualization color coded - amplitude strengths and / or on the basis of the size of the respectively activated frequency bandwidth to make, with events or secondary significance ( 1' -N ') in which high amplitude strengths or activations reaching over broad ranges of the frequency bandwidth of the HRV frequency information data set have been determined, are exposed to a larger factor for assessing the presence of the presence of human biological relevant influence on the vegetative test subject regulation mechanisms Events or secondary significances ( 1' -N '), in which lower amplitude strengths or over a smaller ranges of the frequency bandwidth of the HRV frequency information record reaching activations were determined. Method according to one of Claims 1 or 2, characterized in that a HRV frequency information data set which can be represented as a "spectrogram" is analyzed as the vegetative function parameter (HRV-FP), which activates the activation over a defined frequency bandwidth of substantially 0 to 0.5 Hz each representing a specific frequency range associated vegetative organ functions of the test subject, wherein a lying in the frequency range of substantially 0.2 to 0.3 Hz of the HRV frequency information data set temporal course of autonomic organ functions is analyzed, which reflects the breathing-related modulation of the heart rhythm respiratory sinus arrhythmia (RSA), wherein a curve that is substantially convex toward the upper end of the frequency bandwidth is used as secondary significance ( 1' -N '), wherein the convexly curved course preferably extends over a time interval between 30 and 160 minutes, particularly preferably between 60 and 120 minutes. Method according to one of claims 1 or 2, characterized in that the temporal course of the heart rate of the test subject is analyzed as a vegetative function parameter (HRV-FP) and in this case the occurrence of short-term arrhythmias in relation to the temporally preceding and subsequent heart rate course as secondary significance ( 1' -N ') is detected. Method according to Claim 7, characterized in that the arrhythmia of the heart rate and thus secondary significance ( 1' -N ') the occurrence of extrasystoles is detected, whereby an extrasystole in the course of the heart rate measured in the unit: [number of heartbeats per unit of time, preferably per minute] as a short-term, erratic increase in the heart rate over the chronologically preceding and subsequent heart rates And thus as a significant shortening of the interval duration between two consecutive heartbeats, wherein in a preferred variant of the method sudden increases in the heart rate are then recognized as extrasystoles, if these elevations are in each case more than 30%, preferably more than 50%, particularly preferably more than 100 % of the immediately preceding or following heart rate or the average heart rate determined in the respective measurement period. Method according to one of Claims 1 or 2, characterized in that the temporal progression of respiratory sinus arrhythmia (RSA), which reflects the respiration-related modulation of the cardiac rhythm, is analyzed as a vegetative functional parameter (HRV-FP), which is preferably converted to logRSA by means of a decadic logarithm. Method according to one of claims 1 or 2, characterized in that as a vegetative function parameter (HRV-FP) analyzes the time course of the pulse-breath quotient (QPA) of the test subject preferably those events as secondary significances ( 1' -N ') are detected, in which the QPA curve deviates from a ratio pulse / breath = 4: 1 by more than 20% and / or in which temporary peaks or maximum values of the QPA curve occur. Method according to one of claims 1 or 2, characterized in that as a vegetative function parameter (HRV-FP) the time course of the vegetative quotient (VQ) of the test subject is analyzed, which is the ratio of predominantly the sympathetic, in particular the blood pressure rhythm associated low-frequency , HRV frequency ranges (LF) extending substantially between 0.04-0.15 Hz to the parasympathetic system, in particular HRV frequency ranges (HF), which are assigned to respiratory modulation and substantially between 0.15 and 0.40 Hz, whereby as secondary significances ( 1' -N ') are detected: temporary increase of the vegetative quotient (VQ) by more than 20% (compared to temporally preceding VQ values or to a VQ average value determined in the reference time interval) and / or - exceedances or sympathetic- Dominance of the vegetative quotient of more than 3: 1, preferably more than 5: 1. Method according to one of claims 1 or 2, characterized in that as a vegetative function parameter (HRV-FP), the time course of the SDNN _RR (standard deviation of normal-to-normal intervals) or a SDNN corresponding statistical characteristic such. SDNNIDX / ASDNN of the test subject, ie an HRV-associated statistical spread around the mean of the heartbeat interval duration or its differences representing the change in substantially all frequency ranges of total variability of artifact-adjusted RR heartbeat interval series within a specified time interval , Method according to one of claims 1 to 12, characterized in that as secondary significances ( 1' -N ') in one of the above-mentioned HRV-FP curves all events or course sections are detected, which in graphical representation a slope k> 1 or a pitch angle α of more than 45 °, preferably more than 70 °, especially preferably of approximately 90 ° with respect to the time axis (slope k → ∞) or an approximately needle-shaped elevation of the HRV-FP curve, whereby in each case exceeding the average HRV-FP profile by at least 20% in the case of the aforementioned events or detection criteria. , preferably by at least 30%. Method according to one of claims 1 to 13, characterized in that from the time of detection of secondary significances ( 1' At least one sub-time interval is opened in each case as at least one further secondary significance occurring within the sub-time interval as a sufficient criterion for the positive evaluation of the presence of a human biological relevant influencing of the vegetative regulatory mechanisms of the test subject. 1' -N ') must be detected and wherein the opened sub-time interval is preferably a maximum of 5 minutes, more preferably a maximum of 1 hour. Method according to one of Claims 1 to 14, characterized in that as a sufficient condition for the positive evaluation (the presence of a relevant HFI-indicated influencing) the detection of a defined number of secondary significances ( 1' -N ') is required, preferably at least three, more preferably at least five per reference time interval, which is preferably between 0.5 and 12 h, more preferably between 5 and 30 min. Procedure ( 1 ) according to one of claims 1 and 3 to 15, characterized in that for the synchronization of the HFI and the HRV-FP curve for the reference time interval considered (T _1-n ) in each case an equal number of the two courses constituting HFI and HRV-FP measurement or calculation values is generated, preferably by interpolation and / or extrapolation and / or selection of detected HFI and HRV-FP measurement / calculation values, and time-corresponding HFI and HRV-FP measurement / calculation values each having the same time indices In a preferred embodiment, the reference time interval (T ₁ ) for both (HFI and HRV-FP) courses is subdivided into a multiplicity of sub-time intervals of a maximum of 30 seconds, particularly preferably of a maximum of 10 seconds, and In each of these sub-time intervals, an HFI measurement / calculation value and an HRV-FP measurement / calculation value are respectively selected, which with respect to each as a detection criterion for primary and secondary characteristic significance values are most significant, preferably minimum, maximum, median, average or delta values, and the HFI and HRV-FP measurement / calculation values selected in the respective sub-time interval in the sequence on their correlation be checked. Method according to one of claims 1 and 3 to 16, characterized in that for the positive evaluation (the presence of a relevant HFI-indicated influence) at least one, preferably at least three coincidental significances ( 1 -N) must be detected per hour, or that in the analyzed reference time interval more than 20%, preferably more than 30%, particularly preferably more than 50% of the primary significances detected in the HFI curve ( 1 -N) with coincident secondary significances ( 1' -N ') in the HRV-FP curve. Method according to one of claims 1 and 3 to 17, characterized in that as primary significances ( 1 -N) in the HFI curve, one or more of the following characteristics are detected in a preferably automated manner: - maximum values or power flow peaks, which in the case of a graphical representation are shown as approximately needle-shaped or jagged amplitude deflections, - approximately step-shaped course sections, in this case Preferably, the beginning and / or end portions of a respective approximately horizontal or a consistent power density indexing indicative course section of the staircase and / or ebendiesen approximately horizontal course section itself - several, preferably more than three, preferably within a time interval of a maximum of 10, 30 or 60 minutes successive increases and decreases in the course, - Exceeding a power flux density of more than 0.1 mW / m2, preferably of more than 0.05 mW / m2, more preferably of more than 0.01 mW / m2, - approximately constant power flux densities in the case of a graphical representation substantially parallel to the time axis extending portions of the HFI curve, - subsidence and / or increases in the HFI curve or the power density, which preferably compared to immediately preceding and / or subsequent HFI-course sections, a difference of more than 10th %, more preferably of more than 30%, wherein as 'immediately preceding' in this case a period of several, z. B. 1-30 minutes, preferably from 0-60 seconds, more preferably from 0-10 seconds can be defined. Method according to one of claims 1 and 3 to 18, characterized in that the interval duration, ie the time intervals of successive primary significances ( 1 -N) in the HFI curve and preferably also their intensity, ie the difference of the power density between immediately preceding and / or subsequent HFI waveform sections are subjected to a frequency analysis and in this case statistical characteristic values are generated for which respective threshold values or maxima and / or minima wherein exceeding or undershooting of these threshold values is recognized as the primary meta-significance, and the occurrence of such a primary meta-significance is checked for temporal correlation with secondary significances detected in the HRV-FP curve, the existence of such a correlation is regarded as an indication of the presence of a human biologically relevant influencing of the vegetative regulatory mechanisms of the test subject, and wherein as a statistical parameter preferably the interval of successive primary significances ( 1 -N) SDNN (standard deviation of normal-to-normal intervals) is used. Procedure ( 1 ) according to any one of claims 1 and 3 to 19, characterized in that a targeted initiation of a high frequency immission acting on the test subject by one or more specially provided test high frequency source (s) made and thus one or more timed primary Significances ( 1 -N), wherein the simultaneously determined HRV-FP course of the test subject reaches the primary significance (s) ( 1 -N) coincide secondary significances ( 1' And wherein the initiation of the high-frequency immission is preferably in a (on external, ie not related by the test high-frequency source high frequency immissions related) HFI history area with approximately constant exposure altitude over a defined period of time or at Existence of external high-frequency immissions, which are below a defined threshold value, preferably below a power flux density of 0.4 mW / m2. Procedure ( 1 ) according to any one of claims 1 and 3 to 20, characterized in that the test subject during one or more time intervals by a preferably on all sides surrounding the test subject shielding device such. B. shielded by electrically conductive metal fabric of external high-frequency immissions of the environment and the shielding either open for a defined period of time and thus the test subject is exposed to the external high-frequency immissions or that with the shielding device closed in the time interval of the shield at least one within the shielding activated test RF source is activated one or more times for a defined period of time, wherein the simultaneously determined HRV-FP curve of the test subject to the / by the test RF source generated primary significance (s) ( 1 -N) coincide secondary significances ( 1' -n ') or on during the period of the opening of the shielding occurring secondary significances ( 1' -N ') is checked. Method according to one of Claims 1 and 3 to 21, characterized in that a plurality of HFI profiles in different frequency band ranges are determined from the high-frequency immissions acting on the test subject, such frequency-selective HFI progressions each being detected by at least one simultaneously detected HRV-FP. Course and coincident significances ( 1 -n, 1' -N ') are detected. Method according to one of claims 1 and 3 to 22, characterized in that the HFI course and the HRV-FP course constituent measurement or calculation values are each converted into a gradient sequence (G _HRV-FP and G _HFI ), which consists of a plurality time-sequential gradients, ie, forming difference formations between in each case two temporally successive HFI or HRV-FP measurement or calculation values, thus being depicted as a bar graph in the case of a graphical representation, the gradient sequences (G _HRV-FP and G _HFI ) being considered in accordance with the invention their correlation are analyzed. A method according to claim 23, characterized in that the gradient sequences (G _HRV-FP and G _HFI ) corresponding graphical profiles are created, wherein the HRV-FP course corresponding gradient sequence (G _HRV-FP ) and the HFI gradient corresponding gradient sequence ( G _HFI ) are superimposed on each other or side by side, in this case preferably mirror-inverted to a parallel or congruent with the time axis mirror axis and wherein at least one of the gradient sequences (G _HRV-FP and G _HFI ) may be scaled in such a way that orthogonal to the time axis running average heights or elevations of the gradients are approximately equal in both graphs. Method according to Claim 24, characterized in that envelope curves are applied to the graphic sequences of the gradient sequences (G _HRV-FP , G _HFI ) which are to be compared with one another. Method according to one of claims 23 to 25, characterized in that between the HRV-FP course corresponding gradient sequence (G _HRV-FP ) and the HFI gradient corresponding gradient sequence (G _HFI ) a preferably in percent specified correlation value is determined. Method according to one of claims 1 to 26, characterized in that as a verification of the analysis, a comparison of the analyzed HRV-FP curve with at least one, preferably with at least two of the recited in the claims 3 to 11 HRV-FP curves with respect to the presence of substantially simultaneous or causally correlated secondary significances ( 1' Or in at least two, preferably at least three, of the HRV-FP courses or types of analysis listed in claims 3 to 11, each having a defined number of secondary significances ( 1' -N ') must be detected. Method according to one of Claims 1 to 27, characterized in that, depending on the determined frequency and / or standard deviation (size) of the secondary significances ( 1' -N ') a gradual, z. B. in percent specified the probability of the presence of an incompatibility of high frequency immissions occurs or / and a gradual, z. B. Percentable evaluation of the vegetative regulatory ability of the test subject to secondary significance ( 1' -N ') is carried out on the basis of a comparison with HRV-FP comparison data, this comparison data being based on current or past measurements on the test subject itself or on empirical values of external HRV-FP test series or / and a gradual, e.g. B. Percentable evaluation on the basis of the speed recorded in the HRV-FP course, with which the organism of the test subject after detection of secondary significances ( 1' -N ') is again carried out in the HRV-FP profile to a standardized or individually calculated HRV-FP control profile, that is to say returns to defined target values. Arrangement for analyzing the interaction of electromagnetic high-frequency immissions (HFI) with vegetative regulatory mechanisms of a test subject according to claim 1, comprising a measuring device for measuring the intensity of high frequency immissions (HFI), preferably determined as power flux density, and an electrocardiogram (ECG) measuring device including measuring electrodes for determining the temporal variation of successive heart rate intervals and the course of determined therefrom associated with the heart rate variability (HRV) vegetative function parameters (HRV-FP) of the test subject and a processor-controlled evaluation together with this in data-connected memory device, on which one or more evaluation algorithms for performing a Analysis method according to one of claims 3 to 28 are deposited, wherein preferably the ECG measuring device and / or the evaluation device and / or the HFI measuring device are combined in a device applicable to the test subject or the evaluation is positioned at an external location and by means of ECG and Data sets determined by HFI measuring devices can be transmitted to this external, preferably server-based evaluation device for carrying out the analysis method according to the invention. Arrangement for analyzing the interaction of radio frequency electromagnetic emissions (HFI) with vegetative regulatory mechanisms of a test subject according to claim 2, comprising an electrocardiogram (ECG) measuring device including measuring electrodes for determining the temporal variation of successive heart rate intervals and the course thereof determined with heart rate variability (HRV ) associated vegetative function parameter (HRV-FP) of the test subject and a processor-controlled evaluation together with this in data-connected memory device on which characteristic, HFI-induced secondary significances or history characteristics in the form of digital or analog data are stored and one or more evaluation algorithms for implementation An analysis method according to one of claims 3 to 28, wherein preferably the ECG measuring device and / or the evaluation device combine in a device that can be applied to the test subject t or the evaluation device is positioned at an external location and data sets determined by means of the EKG and HFI measuring devices can be transmitted to this external, preferably server-based evaluation device for carrying out the analysis method according to the invention or for comparison with the characteristic, HFI-induced secondary significances.

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