WO1991019452A1 - Procedes de detection et d'evaluation de troubles cardiaques - Google Patents

Procedes de detection et d'evaluation de troubles cardiaques Download PDF

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Publication number
WO1991019452A1
WO1991019452A1 PCT/US1991/004000 US9104000W WO9119452A1 WO 1991019452 A1 WO1991019452 A1 WO 1991019452A1 US 9104000 W US9104000 W US 9104000W WO 9119452 A1 WO9119452 A1 WO 9119452A1
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WIPO (PCT)
Prior art keywords
determining
phase
patient
plot
plane plot
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PCT/US1991/004000
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English (en)
Inventor
Hrayr Sevag Karagueuzian
George Alexander Diamond
Steven Shahid Khan
Timothy Alan Denton
Steven Evans
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Cedars-Sinai Medical Center
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Priority to CA002064887A priority Critical patent/CA2064887A1/fr
Publication of WO1991019452A1 publication Critical patent/WO1991019452A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39—Heart defibrillators
    • A61N1/3925—Monitoring; Protecting

Definitions

  • This invention relates to heart disorders. More specifically, this invention relates to detecting and evaluating arrythmia, fibrillation and related disorders by manipulation of an electrocardiogram signal.
  • VF ventricular fibrillation .
  • a method of detecting and evaluating heart disorders would therefore find wide applicability and utility.
  • Patient monitoring devices may summon medical personnel if the patient is undergoing VF or onset of VF.
  • Automatic devices which attempt to counter VF e.g. automatic implantable cardiac defibrillators (AICDs) may vary their operation based on evaluation of the severity of the patient's condition. Methods for reliably evaluating the risk of VF may also have important utility in monitoring patients undergoing surgery or other critical therapy.
  • AICDs automatic implantable cardiac defibrillators
  • chaos theory may be valuable for describing certain natural processes, including electroencephalogram (EEG) and electrocardiogram (EKG) signals.
  • EEG electroencephalogram
  • EKG electrocardiogram
  • VF ventricular fibrillation
  • a first aspect of the invention provides a method for detecting a heart disorder, by examination of a phase- plane plot (PPP) of a patient electrocardiogram (EKG) .
  • PPP phase- plane plot
  • EKG patient electrocardiogram
  • a normal patient will have a PPP which is relatively smooth; a patient at risk of developing ventricular fibrillation (VF) onset will have a PPP which exhibits features of a chaotic process, such as multiple bands, "forbidden zones", periodicity with period-doubling and phase locking; a patient exhibiting VF will have a PPP which appears noisy and irregular. Differing PPPs may be readily recognized, thus detecting patients with heart disorders.
  • the PPP's degree of deterministic chaos may be measured by a processor, such as by graphic and numeric analysis.
  • the processor may measure a Lyapunov exponent or a fractal dimension of the PPP.
  • the processor may determine a Poincare section of the PPP and examine that Poincare section for indicators of deterministic chaos. Also, the processed PPP and Poincare sections may be reviewed by a human operator.
  • a second aspect of the invention provides a method for detecting a heart disorder, by examination of a frequency-domain transform (such as an FFT) of a patient EKG.
  • a frequency-domain transform such as an FFT
  • a normal patient will have an FFT with a discrete spectrum, while a patient exhibiting VF will have an FFT with a relatively continuous spectrum and a peak energy at a relatively low frequency (e.g., about 5-6 Hz).
  • a patient exhibiting VF which is difficult to revert with shock will have an FFT with a peak energy at a relatively high frequency (e.g., about 10 Hz or more).
  • an automatic defibrilia- ting device may comprise means for delivering a variable shock, the size of which is determined at least in part by the FFT's peak energy.
  • the defibrillating device may also comprise means for signalling an alarm if the FFT's peak energy is at a relatively high frequency.
  • a third aspect of the invention provides a method for detecting drug toxicity, based on particulars of an action-potential duration (APD) restitution curve, or an action-potential amplitude (APA) curve, which is con- structed for the patient, such as fitting an exponential relation to that curve or such as a parameter time constant for that curve.
  • the slope of the fitted curve will indicate the patient's possibility of predisposition to arrythmia. Differences in the parameters of the fitted curve allow one to distinguish between normal and abnormal patients, e.g. those at risk of arrythmia or ischemia.
  • a normal patient will have a relatively low parameter time constant; a patient who is exhibiting drug toxicity will have a relatively high parameter time constant.
  • a PPP of APD or APA data may also be generated, and the analytical techniques described herein may be utilized to interpret that PPP, to determine and evaluate drug toxicity.
  • Figure 1 shows a patient monitoring system.
  • Figure 2 shows a set of sample EKG signals.
  • Figure 3 shows a set of corresponding PPPs for the sample EKG signals of figure 2.
  • Figure 4 shows an example PPP and a corresponding Poincare section.
  • Figure 5 shows an example PPP and a corresponding time-lapse Poincare section.
  • Figure 6 shows a set of corresponding frequency- domain transforms, obtained by performing a fast Fourier transform (FFT) on the EKG signal.
  • FFT fast Fourier transform
  • Figure 7 shows an improved automatic implantable cardiac defibrillator ("AICD") . .
  • Figure 8 shows a signal response of an individual heart muscle cell to a stimulus, known in the art as "action potential”.
  • a first aspect of the invention relates to detection and evaluation of heart disorders by examination of a phase-plane plot (PPP) of a patient electrocardiogram (EKG) .
  • Figure 1 shows a patient monitoring system.
  • a patient 101 is coupled to an electrocardiogram (EKG) device 102, which acquires EKG signals and transmits them to a processor 103.
  • the processor 103 may display the EKG signals on a monitor 104 (as is well-known in the art) , or it may process the EKG signals and display any results of processing on the monitor 104.
  • EKG signals are well-known in the art, as are methods of acquiring them.
  • an EKG refers to a surface electrocardiogram, but other forms of electro ⁇ cardiogram would also work with the methods disclosed herein, and are within the scope and spirit of the invention.
  • the EKG shown herein may comprise a surface EKG, an epicardial EKG, an endocardial EKG, or another related signal (or set of signals) measured in or near the heart.
  • the signal which is manipulated may be a voltage signal, a current signal, or another related electromagnetic values (or set of values) .
  • Figure 2 shows a set of sample EKG signals.
  • a first EKG signal 201 shows a normal patient.
  • a second EKG signal 202 shows a patient in transition to VF.
  • a third EKG signal 203 shows a patient with VF.
  • the processor 103 may construct a phase-plane plot (PPP) from the EKG signal.
  • a first type of PPP comprises a plot of an EKG variable against its first derivative.
  • the EKG variable is voltage, v (itself a function of time) ; its first derivative is dv/dt (also a function of time) .
  • variable chosen for the PPP may be any one of a variety of different parameters, including EKG voltage, current, or another signal value.
  • the chosen variable (v) may be plotted against its first time derivative (dv/dt) , its second time derivative d 2 v/dt 2 , or another time derivative d n v/dt n .
  • an Mth derivative may be plotted against an Nth derivative.
  • PPP may comprise a plot of an EKG variable (or an Nth derivative thereof) against a time delayed version of itself, (e.g. v(t) versus v(t-6 * t)).
  • This type of PPP is sometimes also called a "return map”.
  • This type of PPP is less sensitive to EKG signal noise.
  • PPP may comprise a plot of three EKG variables (or Nth derivatives thereof) simultaneously (e.g., v, dv/dt, and d 2 v/dt 2 ) .
  • Such a PPP would be 3- dimensional.
  • the PPP may be displayed stereoscopically, or a 2-dimensional plane "cut" of the 3-dimensional display may be displayed on a 2- dimensional display. It would be clear to one of ordinary skill in the art, that all of these choices described herein, or combinations thereof, would be workable, and are within the scope and spirit of the invention.
  • Figure 3 shows a set of corresponding PPPs for the sample EKG signals of figure 2.
  • a first PPP 301 corresponds to the first EKG signal 201.
  • a second PPP 302 corresponds to the second EKG signal 202.
  • a third PPP 303 corresponds to the third EKG signal 203.
  • Part of this aspect of the invention is the discovery that a normal patient will have a PPP which exhibits the regularity and smoothness of an EKG signal from that normal patient, while a patient undergoing VF will have a PPP which exhibits the irregularity and complexity of an EKG signal which might be deterministic chaos (e.g., a periodicity, banding and "forbidden zones") . Moreover, a patient in transition from normal into VF (i.e., in VF onset) exhibits a PPP which is consistent with an assessment that the EKG signal for the patient is in transition to deterministic chaos.
  • deterministic chaos e.g., a periodicity, banding and "forbidden zones
  • a normal patient has a relatively regular beat-to- beat EKG signal.
  • the patient's EKG signal at first shows oscillations between pairs of alterant regular beat-to-beat signals.
  • the patient's EKG signal shows oscillations between greater and greater numbers of alterant regular signals (e.g., four possible alternants, eight possible alternants, etc.), until it is no longer possible to identify alterant regular signals and the EKG signal is irregular and highly complex. At that point, the patient is generally said to be exhibiting VF.
  • the patient's PPP will transition from a smooth single-banded display, through a multi- banded display (showing multiple alternants) and finally to an irregular and highly complex display.
  • the display change in the PPP is so striking that even a relatively untrained person can see the difference. This is in contrast with display changes in the EKG, which generally requires a skilled cardiologist to evaluate.
  • VF cardiovascular disease
  • drugs overdose especially overdose with an anti-arrhythmic which has a pro-arrhythmic effect in overdosage, e.g., quinidine intoxication
  • excessive electrical stimulation e.g., hypothermia, ischemia, and stress.
  • a patient monitor may examine the patient's PPP so as to determine if the patient is in transition from normal to VF; this could indicate that one of these pro-arrhythmic factors is excessively present.
  • the processor 103 may further process the PPP so as to measure the PPP's degree of deterministic chaos. Several techniques may be applied for this purpose:
  • the processor 103 may measure a Lyapunov exponent of the PPP.
  • the Lyapunov exponent of the PPP is a measure of the degree to which nearby paths of the PPP diverge.
  • the Lyapunov exponent is well-known in chaos theory and may be measured with available software. See, e.g.. Wolf et al., "Determining Lyapunov exponents from a time series", Physica D 1985;16:285-317.
  • the processor 103 may measure a fractal dimension of the PPP.
  • the fractal dimension of the PPP is a measure of the degree to which the PPP forms a "space ⁇ filling" curve.
  • the fractal dimension is well-known in chaos theory and may be measured with several techniques (e.g. correlation dimension or box-counting methods) , for example as shown below:
  • the constant k is a measure of the fractal dimension of the PPP. A value of k between about 3 and about 7, especially with a fractional component, implies that the
  • PPP is likely to represent a process based on deterministic chaos, and therefore a patient who is close to (or actually in) VF.
  • the processor 103 may determine a Poincare section of the PPP and examine that Poincare section for indicators of deterministic chaos, as described herein.
  • the processed PPP and Poincare sections may also be displayed for review by a human operator, whereupon any visible structure will be readily recognized.
  • FIG. 4 shows an example PPP 401 and a corresponding Poincare section 402.
  • a Poincare section may comprise a line segment drawn across a part of the PPP. In general, such a line segment will be close to perpendicular to the trajectories of the PPP in a region of interest.
  • the processor 103 may acquire the data points in each Poincare section or PPP and compute a statistical measure of anisotropy or inhomogeneity of those data points.
  • One such measure is based on the mean and standard deviation of those data points (these may be computed by statistical methods which are well-known in the art) .
  • the ratio r (standard deviation) / (expected value) (403) is a measure of the degree of clumping in the Poincare section.
  • a greater value for r implies that the PPP is more likely to represent a process based on deterministic chaos, and therefore a patient who is close to (or actually in) VF.
  • the value for r may be displayed for review by a human operator in comparison with a value for r for a normal patient, together with a set of confidence bands, as is well-known in the art, for indicating a degree of variation from a normal patient.
  • the processor 103 may also compute other statistical measures of the Poincare section.
  • the processor 103 may also determine a "time-lapse" Poincare section of the PPP.
  • Figure 5 shows an example PPP 501 and a corresponding time-lapse Poincare section 502.
  • a time-lapse Poincare section may comprise a set of data points selected from the PPP by selecting one data point every t seconds.
  • the time-lapse Poincare section may be analyzed in like manner as the other Poincare section disclosed herein.
  • a second aspect of the invention relates to detection and evaluation of heart disorders based on a frequency- domain transform of a patient EKG.
  • Figure 6 shows a set of corresponding frequency- domain transforms, obtained by performing an FFT on the EKG signal.
  • a first transform 601 corresponds to a first EKG signal (not shown) .
  • a second transform 602 corresponds to a second EKG signal (not shown) .
  • the frequency spectrum shows that the energy of the corresponding EKG signal occurs primarily at a discrete set of frequencies.
  • the frequency spectrum shows that the energy of the corresponding EKG signal has a continuous spectrum of frequencies, and has an energy peak 603.
  • Part of this aspect of the invention is the use of both visual and mathematical techniques for analyzing frequency domain transforms, including for example calculation of a harmonic magnitude ratio (HMR) .
  • HMR harmonic magnitude ratio
  • a major peak or a central region of energy distribution in a spectrum of a frequency domain transform such as an FFT
  • the HMR calculated as follows: A magnitude of the transform in the region of the identified point is determined (e.g., by summing the magnitude of the transform at the identified point and at surrounding points) , and is summed with the corresponding magnitude in the region of harmonic values of the frequency for the identified point. This sum is divided by a total magnitude of the transform for the entire signal; the ratio is defined as the HMR.
  • One method which is known for bringing a patient out of VF is to administer an electric shock across the patient's heart.
  • This electric shock must generally have a substantial energy, e.g. 10-20 joules, and may often cause tissue damage to the patient even if it is successful in defibrillating the patient.
  • Multiple shocks may be required, generally of increasing energy. Accordingly, it would be advantageous to use a larger shock only when necessary, and it would be advantageous to use as few shocks as possible.
  • Part of this aspect of the invention is the discovery that when the energy peak 603 of the frequency-domain transform 602 is at a relatively low frequency, a relatively low energy shock will generally suffice to defibrillate the patient.
  • the energy peak 603 of the frequency-domain transform 602 is at a relatively high frequency (also, when a secondary energy peak 604 appears in the frequency-domain transform 602 at a relatively high frequency) , it will require a relatively high energy shock to defibrillate the patient, if it is possible to defibrillate the patient by means of an electric shock at all.
  • AICDs automated implanted cardiac defibrillators
  • FIG. 7 shows an improved AICD 701.
  • a patient 702 is coupled to an AICD EKG 703, which acquires EKG signals and transmits them to an AICD processor 704, which controls a shock device 705 for administering a defibrillating shock to the patient 702.
  • the improved AICD 701 also comprises (e.g., as part of the AICD processor 704) software for determining an FFT of the EKG signal and for determining the energy peak in that FFT. If the energy peak in that FFT is relatively low, the AICD processor 704 controls the shock device 705 to administer a relatively small shock to the patient. If the energy peak in that FFT is relatively high, the AICD processor 704 controls the shock device 705 to administer a relatively large shock to the patient, and may also signal an alarm 706 or other indicator that defibrillation may not be successful.
  • software for determining an FFT of the EKG signal and for determining the energy peak in that FFT. If the energy peak in that FFT is relatively low, the AICD processor 704 controls the shock device 705 to administer a relatively small shock to the patient. If the energy peak in that FFT is relatively high, the AICD processor 704 controls the shock device 705 to administer a relatively large shock to the patient, and may also signal an alarm 706 or other indicator that defibrillation
  • a third aspect of the invention relates to detection and evaluation of drug toxicity based on a parameter time constant for an action-potential duration (APD) restitution curve or an action-potential amplitude (APA) curve which is constructed for the patient.
  • APD action-potential duration
  • APA action-potential amplitude
  • Figure 8 shows a signal response of an individual heart muscle cell to a stimulus. This individual cell response is known in the art as "action potential”.
  • a time duration for recovery 801 of an individual cell depends on factors including a resting period 802 which the cell has had prior to stimulus. It is also well-known in the art that an APD restitution curve can be constructed for a human patient with the use of an intracardiac catheter. However, the complete relation between the actual time duration for recovery 801 based on the resting period 802 is not known.
  • APD APD ⁇ - A * exp(-DI/tau) (803) where APD . is the plateau APD, A is a proportionality constant, DI is the diastolic interval, and tau is the parameter time constant
  • the nonlinear nature of the APD restitution curve may promote deterministic chaos in response to excessive stimulus of the heart muscle cells.
  • the APD restitution curve is steeper (i.e., the parameter time constant tau is larger) , there is accordingly a greater predilection for the heart to enter VF.
  • Another part of this aspect of the invention is the discovery that a normal patient will have a relatively low APD restitution parameter time constant, while a patient who is exhibiting drug toxicity (e.g., quinidine intoxication) will have a relatively high APD restitution parameter time constant.
  • the restitution parameter time constant may also be used in monitoring cardiac stability, and in evaluating efficacy of anti-arrhythmic drugs. Experimental verification of the present invention has been achieved by the inventors.
  • PPPs were useful in distinguishing among all three classes of signals.
  • Periodic signals showed clear, widely separated trajectories; chaotic signals showed banding, forbidden zones and sensitive dependence on initial conditions; random signals showed no clear internal structure.
  • the only major difference between the PPPs and the appropriately lagged return map was a 45 degree rotation.
  • Poincare sections were also able to distinguish among the three classes of signals: periodic signals showed isolated points; chaotic signals showed ordered areas of apparent self-similarity; random signals showed a Gaussian distribution of points. Correlation dimension was more able to distinguish between chaotic and random signals than between chaotic and periodic signals.
  • HMRs of periodic signals were greater than 97%; HMRs of chaotic signals varied between 17 and 80%; HMRs of random signals were approximately 40%.
  • PPPs were greatly affected by noise, return maps were less affected, while spectral analysis was relatively immune to noise. It was concluded that PPPs, return maps, Poincare sections, correlation dimension and spectral analysis are all useful determinatives of chaotic systems.
  • Stimulus-response latency remained constant at 6-9 msec.
  • PPPs of the APDs during the irregular dynamics showed sensitive dependence on initial conditions and forbidden zones consistent with chaos theory.
  • a preferred embodiment of the present invention may include a combination of the aspects of the invention described herein.
  • One preferred embodiment may comprise multiphasic analysis of a PPP (e.g., visually with a display, graphically with Poincare sections, and numerically with Lyapunov exponents and correlation dimension) , frequency spectral analysis, and mathematical analysis of an APD restitution curve.
  • embodiments of the invention may comprise means for continuous monitoring of drug toxicity, atrial fibrillation, ischemia or other heart conditions, such as during surgery or patient recovery from surgery.
  • embodiments of the invention may comprise means for indicating heart conditions which are detected to attending medical personnel or to the patient.
  • means may be provided for directing the patient (when a heart disorder is detected) to contact a physician or to proceed to a nearby hospital for treatment.

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  • Health & Medical Sciences (AREA)
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Abstract

Des signaux d'ECG sont reçus d'un patient (102), traités (103) afin de déterminer des écarts par rapport à la normale et affichés (104) soit sous forme d'un signal transformé en fréquence soit sous forme d'un graphique du signal contre un de ses dérivés dans un tracé de plan de phase. Selon le signal et les circonstances on peut interpréter les résultats afin d'obtenir des informations relatives à des troubles cardiaques, le degré de toxicité médicamenteuse ou l'efficacité d'un médicament particulier. De plus, un défibrilateur automatique utilise le signal d'ECG reçu (703) du patient et des unités de traitement des signaux (704) afin de déterminer la quantité d'énergie à décharger sur le c÷ur à l'aide du dispositif d'administration de chocs (705).
PCT/US1991/004000 1990-06-20 1991-06-06 Procedes de detection et d'evaluation de troubles cardiaques WO1991019452A1 (fr)

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Application Number Priority Date Filing Date Title
CA002064887A CA2064887A1 (fr) 1990-06-20 1991-06-06 Methode de detection et d'evaluation des troubles cardiaques

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US701,753 1985-02-14
US54188190A 1990-06-20 1990-06-20
US541,881 1990-06-20
US70175391A 1991-05-17 1991-05-17

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JP (1) JP3338049B2 (fr)
AU (1) AU8102391A (fr)
CA (1) CA2064887A1 (fr)
WO (1) WO1991019452A1 (fr)

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WO2011077294A1 (fr) 2009-12-21 2011-06-30 Koninklijke Philips Electronics N.V. Procédé et appareil de traitement de signaux de photo-pléthysmographe
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US10271744B2 (en) 2013-03-15 2019-04-30 The Regents Of The University Of California System and method to identify sources associated with biological rhythm disorders
US10271786B2 (en) 2011-05-02 2019-04-30 The Regents Of The University Of California System and method for reconstructing cardiac activation information
US10398326B2 (en) 2013-03-15 2019-09-03 The Regents Of The University Of California System and method of identifying sources associated with biological rhythm disorders
US10485438B2 (en) 2011-05-02 2019-11-26 The Regents Of The University Of California System and method for targeting heart rhythm disorders using shaped ablation
US10856760B2 (en) 2010-04-08 2020-12-08 The Regents Of The University Of California Method and system for detection of biological rhythm disorders
CN113499081A (zh) * 2021-07-06 2021-10-15 中山大学 基于双通道深度神经网络的驾驶员房颤检测方法及系统
US11147462B2 (en) 2008-10-09 2021-10-19 The Regents Of The University Of California Method for analysis of complex rhythm disorders
US11446506B2 (en) 2013-03-15 2022-09-20 The Regents Of The University Of California System and method of identifying sources associated with biological rhythm disorders

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JP6616960B2 (ja) * 2015-04-20 2019-12-04 フクダ電子株式会社 生体信号処理装置およびその制御方法

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AU8102391A (en) 1992-01-07
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JP3338049B2 (ja) 2002-10-28
EP0487706A1 (fr) 1992-06-03
JPH05502816A (ja) 1993-05-20

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