EP1196862A1 - Indicateur de qualite de signaux de mesure, en particulier de signaux de mesure en medecine, par ex. pour la mesure de la saturation en oxygene - Google Patents

Indicateur de qualite de signaux de mesure, en particulier de signaux de mesure en medecine, par ex. pour la mesure de la saturation en oxygene

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Publication number
EP1196862A1
EP1196862A1 EP99929166A EP99929166A EP1196862A1 EP 1196862 A1 EP1196862 A1 EP 1196862A1 EP 99929166 A EP99929166 A EP 99929166A EP 99929166 A EP99929166 A EP 99929166A EP 1196862 A1 EP1196862 A1 EP 1196862A1
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EP
European Patent Office
Prior art keywords
signal
quality indicator
quality
measurement
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP99929166A
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German (de)
English (en)
Inventor
Siegfried Kästle
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
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Koninklijke Philips Electronics NV
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Application filed by Koninklijke Philips Electronics NV filed Critical Koninklijke Philips Electronics NV
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7221—Determining signal validity, reliability or quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to the determination of a quantitative statement about the quality of a measurement signal, preferably a medical measurement signal as in pulse oximetry.
  • the measurement of signals usually consists of a multi-stage process, typically with the steps of signal acquisition, signal processing and signal evaluation.
  • the mere signal acquisition is sufficient, but depending on the application, signal processing and / or evaluation is still required on a regular basis
  • signals representing the quantity to be measured are recorded as raw signal values, e.g. by a sensor or another suitable receiving device.
  • signals representing the quantity to be measured are recorded as raw signal values, e.g. by a sensor or another suitable receiving device.
  • electrocardiography e.g., electrocardiography
  • the raw signals determined represent the desired measured variable only indirectly, and signal evaluation is still required in order to derive the desired measured variable from the raw signals.
  • the raw signals (both for those directly representing the measured variable and indirectly representing the measured variable) require signal processing, ie the raw signals must be processed accordingly. eg by improving the signal quality, such as the signal-to-noise ratio, or by filtering or suppression undesired measuring influences.
  • Pulse oximetry is about the non-invasive, continuous determination of the oxygen content of the blood (oximetry). based on the analysis of the photospectrometrically measured pulse. For this it is necessary that a pulse waveform (Pletnysmogramm) is present at multiple wavelengths. Practically all devices work with only two wavelengths, making them inexpensive. compact solutions are possible.
  • the principle of photometry is based on the fact that the amount of light absorbed is determined by the degree of absorption of a substance and by the wavelength.
  • the signal quality of the pulse oximetry measurement means that more and more difficult cases are still used to derive measured values, there is still no meaningful indicator for the clinical user that allows the reliability and quality of the measured values to be reliably assessed.
  • Such an assessment is important, however, since the Maisoximeter with its plethysmographic raw signals have too little information to always make a very certain decision in borderline cases whether a measured value may be displayed.
  • the doctor for example, regularly has considerably more information available to help him decide whether his patient's oxygen supply is really critical or whether there is merely an artifact from the pulse oximeter.
  • the previous aids which are given to the user of the pulse oximetric measurement regularly, are the display of a raw signal as a curve (plethysmogram) or the one-dimensional variant as a pulsating Baiken display. There are also warnings such as “Motion " . "Noise”. "Low signal” through text fields on the displays / flashing displays (cf. Morris et. Al., A Comparison of fifteen pulse oximeters. Anesth. Intens. Care, Vol. 17. pp. 62-82, 1989).
  • a first approach in the direction of a quality indicator for the pulse oximetry measurement can be found in EP-A-587009.
  • This system is certainly suitable for an unusable measuring location. for example because the received light intensity is too low. to show.
  • the AC component (perfusion) and the interference ratio are not taken into account, so that a display with a high value of the quality display does not guarantee reliable measurement.
  • the user can get some indication of the quality of the signal.
  • the meaningfulness is limited.
  • One problem is e.g. that only a raw signal is regularly displayed as a plethysmogram, but the Inclusmeter evaluates two raw curves.
  • the N-400 Fetal Oxygen Saturation Monitor from Nellcor Puritan Bennett has a triangular bar display on the front as a multi-parameter reliability display of the medium signal quality.
  • Signal quality indicator shows the quality of the signal that is used to calculate the SpO : value. If the signal quality drops below a required threshold value, an acoustic alarm signal is triggered if the signal is lost.
  • Periodicity of the signal is used, for example, while the amplitude of the
  • a quantitative statement about the quality of a measurement signal is determined by determining factors that are preferably related to signal processing, signal processing and / or signal evaluation. These factors are determined by combination methods, in particular by a fuzzy logic such as fuzzy logic. to a quality indicator linked to describe quantitatively the quality of the measured value determined.
  • factors relevant to signal recording are, for example, factors affecting the measurement location. Describe the measurement time, the measurement sensor or the like.
  • Factors relevant to signal processing can be, for example, the signaling signal distance. Parameters of a possible noise suppression or signal compensation. or the like.
  • Factors relevant to the signal evaluation can be determined in particular by the measurement algorithm or algorithms used. Consideration of all raw signals or only parts of them
  • the "quality" of the quality indicator can be improved.
  • the links can only be limited to a few selected factors.
  • the quality indicator is preferably visually represented by a corresponding display, for example between a minimum value and a maximum value.
  • the maximum value results, which indicates maximum reliability.
  • the quality indicator drops to the lower limit of the minimum value. least reliability indexed. Then the signal is so weak or so badly disturbed that the guided measurement values are very likely to have larger errors and should therefore preferably no longer be displayed or should only be displayed with the appropriate information
  • the high quality indicator thus provides a certain measure of this to a certain extent. with which reliability the measured values can be calculated. It is however, it is clear that an exact forecast of display errors cannot be guaranteed, especially not in individual cases. However, the user is sensitized to smaller values of the quality indicator (low reliability). that the readings may be uncertain. He can then check or try to achieve a better signal quality and thus a more reliable measurement using alternative means, for example by choosing a different measuring location or using a different sensor.
  • the quality indicator according to the invention provides the corresponding clues.
  • the quality indicator according to the invention is indicated by a trend statement in the sense of “relatively better / worse or“ absolutely good / bad ”, e.g. thanks to a quasi-analog display as a bar with variable length. This allows a sufficient and sufficiently intuitive representation of the quality indicator according to the invention.
  • a further visualization of the quality indicator according to the invention takes place in such a way that when the quality indicator value is exceeded via one or more predetermined threshold values, the manner of display for the measured variable which the quality indicator describes changes in each case Flashing display (if necessary with varying Flashing frequency depending on the quality indicator value), by changing the display color (for example, red for low quality indicator values or in the sense of a traffic light: green for high, yellow for medium and red for low quality indicator value), or by inverting the colors.
  • Flashing display if necessary with varying Flashing frequency depending on the quality indicator value
  • the display color for example, red for low quality indicator values or in the sense of a traffic light: green for high, yellow for medium and red for low quality indicator value
  • an alarm function is controlled (e.g. if the measurement signal deviates from a predetermined value or range) depending on the quality indicator.
  • Such control is preferably carried out by changing an alarm delay time (i.e. the time between when an alarm triggering criterion is reached and when the alarm is actually triggered) depending on the value of the quality indicator.
  • the time course of the quality indicator is evaluated in order to identify trends and to be able to make error forecasts, for example.
  • a trend display is preferably carried out, for example by displaying an arrow pointing upwards to improve the signal quality.
  • an error forecast is possible over the course of the quality indicator trend, so that an alarm can be issued, for example, if the quality indicator value continuously drops over a predetermined period of time, even if the absolute value of the quality indicator is still in a tolerable range.
  • the values of the quality indicator determined and / or the trend of the quality indicator are preferably also logged so that they can be taken into account in a subsequent evaluation of the measurement. For example, measured events can later be classified as artifacts or real events.
  • the invention is preferably used for medical measurements monitoring, e.g. in pulse oximetry, but is not limited to this and can also be used for other purposes.
  • Fig. 1 A and 1 B show the energy factor ⁇ e as an example for an undisturbed and for a disturbed episode
  • Fig. 3B and 3C exemplify a possible graphic
  • Fig. 4A-4C show rule bases of the preliminary stages.
  • 4D shows the control surface as a function of the family strength "contiStrength" and the correlation of the time signals "correl".
  • 4E shows the control surface as a function of the energy fluctuation ..energy “ and the SpO : spread” spreadSp02 " .
  • the first-mentioned application EP-A-870466 (.. pincushion algorithm ") is based on the selection of the pulse oximetric signal according to the physiological relevance of the frequency components.
  • the raw signal values in a current time window are transformed into the frequency domain by Founer transformation (here: Fast Fou ⁇ er Transformation - FFT). Ratios of the coefficients of the amplitude spectrum are formed for all frequency points from the transformed raw signals.
  • Founer transformation here: Fast Fou ⁇ er Transformation - FFT
  • a pin cushion algorithm is first used to determine one from the complex amplitudes of the red and infrared spectra.
  • the distance spectrum describes the distance of each individual base point in the needle diagram from the origin.
  • the individual needles are determined from this range of distances by considering the maxima and the associated base points. Only those needles that meet a number of given criteria are retained for further consideration. The selection of needles that is reduced in this way is subjected to a further classification.
  • Needles that represent the useful signal must fulfill the criteria that: the peaks fit well in a harmonic frequency series, as many as possible harmonic waves are present, the needles are as thin as possible and that Frequency of the fundamental wave as well as the saturation value, the perfusion and the pulse rate are in physiological ranges. This is done a Trubewertu ⁇ g for je ⁇ e needle ⁇ urchumblevergaoe or KO criterion them for each of these criteria.
  • the needle that receives the most points, in other words that best meets the criteria and has at least a minimum number of points. is used to determine the output value for the pulse oximetric measured value.
  • a comparison with previous output values can be used as a plausibility check, and if there are significant deviations from the previous output values, the newly determined output value is rejected and no new value is displayed.
  • a transformation e.g. FFT
  • filtering the measurement signal can precede the transformation or join it.
  • Such filtering is preferably carried out e.g. by reducing the DC component finsoeson ⁇ ere as brushed in EP-A-870466 or EP-A-870465) and / or by suppressing transient disturbances (in particular as described in the international patent application of the applicants from the same filing date with the internal file number: 20-99-0010 ).
  • frequency peaks are identified in the transformed time window of the measurement signal with the aid of an abano spectrum mentioned above.
  • identified frequency peaks of the current time window are assigned to temporal courses (or also called threads) of identified frequency peaks of an earlier or more recent time window. insofar as identified frequency peaks already exist. This “chaining of
  • Needling to threads is done by initialization e.g. on restart, taking the first set of needles obtained to establish a set of threads. This is followed by a continuous affiliation of suitable needles, whereby a needle is considered to be suitable if the last link in the
  • Linking given criteria is carried out by fuzzy logic. If a new needle cannot be assigned to an existing thread, a gap remains and the thread is either terminated or replaced by a new thread.
  • the temporal profiles are assigned to one or more families, each of which consists of a fundamental wave and one or more harmonics.
  • Such assignment or chaining of the threads to harmonics takes place by examining the extent to which certain characteristic features exist between the threads, which jointly indicate that the threads belong to the same useful signal.
  • Such an investigation is carried out by linking suitable criteria such as harmonic frequency relationship, expected decrease in amplitude of the upper wave series and / or proportionate trend development of the frequencies and / or amplitudes.
  • the criteria are also linked using fuzzy logic.
  • a family is then selected as that which is to represent the useful signal, the selection being considered in the sense of the highest probability.
  • the family is selected by linking predetermined criteria such as the existence of a fundamental wave. first harmonic and second harmonic, average accuracy of fit of the threads, number of valid needles in one thread (ie the length of the thread), continuity or "holes" of a thread, and quality of relationship between fundamental and first Harmonic. Fundamental wave and second harmonic as well as first harmonic to the second harmonic.
  • the criteria are also linked by fuzzy logic.
  • the selection of a family can also or additionally be carried out by a plausibility check of the family against previous output values, the most plausible family being selected.
  • a frequency peak (a needle) of the current time window is selected from the selected family as the one which is to represent the measured value of the useful signal in this time window, the selection also being considered here in the sense of the highest probability.
  • the current measured value of the useful signal can then be calculated or otherwise determined from this selected frequency peak insofar as this does not already correspond to the measured value.
  • the frequency peak representing the current measured value of the useful signal is selected by linking predetermined criteria using fuzzy logic. Criteria are used as criteria which are based on a plausibility of the current measured value in relation to previous measured values and / or in relation to expected or sensible values.
  • a plausibility check can be carried out to check whether the selected frequency peak actually corresponds to an expected measured value of the useful signal and whether a measured value derived from the selected frequency peak is to be output, or whether for this time window no measured value should be output at all. Such a plausibility check is carried out by comparing the current measured value with previous measured values and / or with expected or useful values.
  • a number of input factors are used to control the quality indicator, each of which, individually and in combination, has a relevant relationship to the quality of the signal and the quality of the calculated display values.
  • the factors can be determined on the one hand by elements which can be determined continuously from the raw signals and which are independent of the actual pulse oximetry algorithm.
  • elements can be represented that come directly from the pincushion algorithm or the FNA and are matched to their performance.
  • the combination of the elements into a summary quality factor is preferably carried out by a fuzzy operation
  • energy values e (t) are preferably determined every second of the algorithm.
  • a continuous trend is formed from the secondarily calculated energy values e (t), which preferably extends up to 20s in the past. As an indicator of Disruptions are considered a sudden increase in energy. Waste is not taken into account.
  • a rank function rank D is used as a measure of the basic level.
  • a ranking function determines the value from a sample that is at a certain ranking in the ordered sample, analogous to a median filter, which gives the average value of the ordered row.
  • a parameter o (with values between 0 and 1) indicates the rank.
  • a rank parameter ⁇ 0.2 has proven itself for the purposes of the invention. It has the effect that an energy value between the minimum and the mean (median) is taken as the reference measure. It makes sense to get close to the minimum, because continuous strong "energy outbreaks" do not erroneously raise the reference value. It is therefore not advisable to go to the bare minimum, because there can also be moments when the useful signal and interference partially cancel each other out and lead to an energy dip that would not be the correct baseline reference point.
  • the energy fluctuation factor for the calculation of the quality indicator is preferably determined using the formula:
  • the maximum function limits the smallest possible value to 0, since dips should not be recorded.
  • the logarithmic measure (dB) for the energy e is preferably used in order to master the large dynamics with stronger disturbances. 1 shows the energy factor ⁇ e as an example for an undisturbed (FIG. 1A) and for a disturbed (FIG. 1B) episode. Fluctuations up to about + 5dB can be regarded as normal: Artifacts can also be assumed.
  • Factor 3 fall in the spectrum
  • a pulse signal always shows a typical pattern of the amplitude spectra: a dominating fundamental wave with more or less steeply falling harmonics.
  • the spectrum drops regularly with more than about -20% / Hz. Values above -10% / Hz. Flat drop or even rise can only be observed in the case of striking faults, especially if they are high-frequency.
  • the variability of the coefficients within the spectrum also represents a measure of the "purity".
  • a restless situation with many peaks indicates disturbances.
  • the scatter factor is ⁇ . in the range 2 to 3. there is a large spread: the majority of the spectral coefficients are either significantly above the best-fit line (peaks) or well below it (background, close to 0). Values ⁇ of the scattering factor ⁇ r below 2 indicate a strong back and forth in the spectrum hm; the background between the harmonics of the Inc wave is filled with disturbances.
  • This factor is determined far ahead in the algorithm. It is the determination of the total area of all peaks detected in the spectrum in relation to the total area that the spectrum spans. Experiments have shown that this measure sensibly complements the previous factor (variability of the spectral coefficients) in some situations. Values above 20% are good, below 5% the background is dominantly high due to disturbances.
  • a SpO : value is assigned to each needle.
  • the harmonics of the pulse are present as needles.
  • Your SpO : value differs only slightly; the difference is of the order of 1%. Faults usually introduce new components in the form of needles, or they overlap the peaks and falsify their SpO : value. Every wrong - ie not belonging to the useful sign - represents a potential risk. Because it could get into a thread one time and thus make an error contribution, this thread should be issued
  • Fig. 2 shows an example of the SpO. Scattering of the needles, the other course an undisturbed base episoo. namely, the volunteer’s breath hold mover.
  • the lower course shows a severely disturbed episode, the same episode as in FIG. 2 above, but overlaid with disturbances, so that the needles sometimes deviate considerably from the target value.
  • the strongly deviating needles are often weak and are only of limited importance in the weighting of the spreading dimension. This increases the spread within the needles of an FNA cycle.
  • a decisive factor in the FNA which also accounts for the largest part in the fuzzy operations (set out below) for linking all quality-determining parameters, is the strength of the family spent. It already summarizes many characteristics of the previous algorithm steps. As the name "Strength” suggests, the value provides information about the quality of the output values.
  • the strength (contiStrength) is preferably in a value range from 0 to 100 points and contains the qualitative properties of algorithm-relevant factors.
  • a quality indicator (QI) value (instQI) is obtained with each clock cycle. In the case of more disturbed signals, this value can fluctuate considerably from cycle to cycle.
  • correl factor 1: r, correlation of the time signals red with infrared
  • slopeFFT factor 3: rr, decrease in the (distance) spectrum in% / Hz
  • PeakFrac factor 5: area share of the peaks in the total area of the spectrum in%
  • FIG. 3B an example of the variable “contiStrength” is shown in FIG. 3B and an example of the variable “.spreadSp02” is shown in FIG. 3C.
  • the profiles of the other variables can be displayed accordingly.
  • the properties of the input variables can be represented as follows:
  • purityFFT The pre-stage “purityFFT ': Here the 3 inputs, which have to do with the spectral properties of the signal, are evaluated jointly and linked to form a kind of" purity factor "of the spectrum.
  • the table below shows the input and output interface of the control block “purityFFT”:
  • 4A shows the rule base of the preliminary stage “spectral purity” (purityFFT). The poor characteristic of a parameter is sufficient to pull the result down.
  • the preamplifier "puritySignal” Here are 3 further inputs, which are also connected to the
  • 4B shows the rule base of the preliminary stage “purity of the signal” (puritySignal).
  • the awkward characteristic of a single parameter is sufficient to pull the result down (see. Don’t care ”fields).
  • 4C shows the rule base of the preliminary stage ..instQI " . Only good pairings allow a very good quality indicator.
  • 4D shows the control surface as a function of the family strength »ContiStrength« and the correlation of the time signals »correl « All other parameters were set to their optimal values. The influence of the correlation is clearly recognizable, but it is still quite weak compared to the family strength.
  • a delay time for responding to an alarm after triggering an alarm condition is set to a maximum, since there is the highest probability of possible false alarms.
  • a high quality indicator value high reliability
  • the alarm delay time is set to a lower value. The delay time can be continuously adjusted automatically between a minimum and a maximum delay value depending on the value of the quality indicator
  • FIG. 5 shows an example of a course of the delay time over the value of Quality indicator Ql. the course being carried out towards the extreme values of the delay time. However, the course can also be adapted linearly or otherwise to the respective circumstances.
  • the minimum and the maximum delay time can preferably be configured by the user and / or the course of the delay time can be configured using the quality indicator. eg adjustable by selecting predefined courses.
  • the quality indicator value is displayed as a trend together with the SpO. Value and the pulse rate and is simultaneously recorded. This allows SpO : and pulse rate events to be qualified as artifacts or real events when viewed retrospectively. It is also possible to forecast expected measurement problems via the course of the quality indicator trend. A deterioration in the perfusion or an increase in movements (eg cold tremors in the recovery room) can be detected earlier. Clinical staff can therefore react to this before a (false) alarm is generated.

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Abstract

L'invention concerne la détermination d'une information quantitative relative à la qualité d'un signal de mesure, par ex. un signal de mesure médical en pulsoximétrie. Ladite invention consiste à déterminer des facteurs relatifs au signal de mesure, concernant en particulier l'enregistrement du signal, le traitement du signal et/ou l'exploitation du signal, et à établir un lien entre les facteurs déterminés par logique floue, de préférence par </= fuzzy logic >/= , afin de générer un indicateur de qualité décrivant la qualité de la valeur de mesure de façon quantitative. De préférence, l'invention concerne également une commande de fonction d'alarme dépendant de l'indicateur de qualité, qui intervient de préférence lorsque le signal de mesure dévie d'une valeur ou d'un domaine seuil prédéfini(e).
EP99929166A 1999-06-10 1999-06-10 Indicateur de qualite de signaux de mesure, en particulier de signaux de mesure en medecine, par ex. pour la mesure de la saturation en oxygene Ceased EP1196862A1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP1999/003994 WO2000077659A1 (fr) 1999-06-10 1999-06-10 Indicateur de qualite de signaux de mesure, en particulier de signaux de mesure en medecine, par ex. pour la mesure de la saturation en oxygene

Publications (1)

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EP1196862A1 true EP1196862A1 (fr) 2002-04-17

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US (1) US6725074B1 (fr)
EP (1) EP1196862A1 (fr)
JP (1) JP4495378B2 (fr)
WO (1) WO2000077659A1 (fr)

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