JP3530169B2 - ECG signal analyzer - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an electrocardiogram signal analyzer, and more particularly, to an electrocardiogram signal analyzer capable of detecting heart disease, particularly arrhythmia, fibrillation and related diseases.

[0002]

BACKGROUND OF THE INVENTION Despite significant advances in the diagnosis and treatment of ischemic heart disease over the past decade, a significant number of patients encounter sudden cardiac death due to ventricular fibrillation (VF) each year. To date, no reliable predictive method or preventive measure has been developed. Externally, VF appears to be a highly complex and accidental phenomenon. The same is true of other related heart diseases, including a stage (a sign of VF) in cardiac function, especially preceding VF.
Therefore, it is difficult to detect with certainty that the VF of the patient or the manifestation of VF is detected by the automatic device. Moreover, the signs of VF are difficult to reliably determine even for skilled medical personnel.

[0003]

Accordingly, methods for detecting and assessing heart disease have wide applicability and utility. The patient monitoring device may call medical personnel when the patient presents with VF or signs of VF. Automated devices that face the VF, such as implantable automatic defibrillators (AICDs), may alter their behavior based on an assessment of the severity of the patient's condition. Also, methods of reliably assessing risk of VF can have significant utility in monitoring patients undergoing surgery or other significant treatment.

It has been found that certain antiarrhythmic drugs have proarrhythmic effects at excess concentrations. Quinidine, for example, is known to have such toxicity. The methods of detecting and assessing heart disease also have broad applicability and utility in determining whether a patient is being administered a toxic (or semi-toxic) dose of a drug associated with a cardiac condition.

The theory of chaos is a recently developed field of phenomena that is highly complex, seemingly random, but which can be described as a deterministic consequence of relatively simple systems. Chaos theory has potentially broad applications in ecosystems and other systems, including ambiguity and ambiguity.
For example, chaos theory has been speculated to be valuable in describing certain natural processes involving electroencephalogram (EEG) and electrocardiogram (EKG) signals. Techniques for detecting and evaluating various aspects of deterministic chaos are known in the field of chaos theory, but have few application examples in the medical field.

Accordingly, there is a need for improved methods and apparatus for detecting and assessing heart disease, including ventricular fibrillation (VF) and signs of VF.

[0007]

The first feature of the present invention is:
Patient electrocardiogram (EKG) as a means of detecting heart disease
The present invention provides an electrocardiogram signal analyzer for analyzing a phase-plane plot (PPP) of the above. Normal patients have PPPs that are relatively smooth. Ventricular fibrillation (V
Patients at risk of developing the symptoms of F) have features of chaotic processes, such as multiple swaths, “forbidden zones”, PPP with periodicity including doubling and constant phase, and patients with VF are noisy. It has a PPP that looks awkward and irregular. PPP
Are easily recognized and thus can detect patients with heart disease.

In a preferred embodiment, the deterministic chaos PP
The degree of P can be measured by a processor, such as graph analysis and numerical analysis. (1) The processor may measure the Lyapunov exponent or fractal dimension of PPP.
(2) The processor determines the Poincare section of PPP,
Poincaré sections may be examined for deterministic chaos markers. Also, the processed PPP and Poincaré sections may be inspected by a human operator.

A second feature of the present invention is that the frequency domain transform (freque) of the EKG of a patient is used as a means for detecting heart disease.
The present invention provides an electrocardiogram signal analysis device for performing ncy-domain transform (for example, FFT) inspection. Normal patients have FFTs with discontinuous spectra, while VF
Patients exhibiting an FFT have a relatively continuous spectrum and an FFT with peak energies at relatively low frequencies (eg, about 5-6 Hz). Patients with VF that are difficult to recover from shock have FFTs that have peak energies at relatively high frequencies (eg, about 10 Hz or higher).

In a preferred embodiment, the automatic defibrillator may include means for providing a variable shock whose magnitude is determined at least to some extent by the FFT peak energy. The defibrillator may also include means for sending an alarm when the FFT peak energy is at a relatively high frequency.

A third aspect of the present invention is the characteristic of the action potential duration (APD) recovery curve or action potential amplitude (APA) curve made with respect to the patient , eg the index to said curve.
Based on conformity of functional relation or parameter time constant of the curve
Then, a method for detecting drug toxicity is provided. The slope of the fitted curve indicates the possible arrhythmic predisposition of the patient. The difference in the parameters of the fitted curve makes it possible to distinguish between normal and abnormal patients, for example as to whether they are at risk of arrhythmia or ischemia. Normal patients have relatively low parameter time constants, and patients with drug toxicity have relatively high parameter time constants. The analytical techniques described herein may be used to generate a PPP of APD or APA data, interpret the PPP, and detect and assess drug toxicity.

[0012]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The first aspect of the present invention relates to the detection and evaluation of heart disease by examining the phase diagram (PPP) of patient electrocardiogram (EKG).

FIG. 1 shows a patient monitor device. Patient 10
1 is connected to an electrocardiogram (EKG) device 102,
The G device 102 acquires the EKG signals and sends them to the processor 103. The processor 103 is EK to the monitor 104 as is well known in the art.
The G signal may be displayed, or the EKG signal may be processed,
The processing result may be displayed on the monitor 104.

EKG signals, as well as how to obtain them, are well known in the art. As used herein, EKG refers to surface electrocardiogram, although other forms of electrocardiogram can be used with the methods disclosed herein and are also within the scope and spirit of the invention. For example, the EKG referred to here includes surface EKG, epicardial EKG, and endocardial EK.
G, or other relevant signal (or set of signals) measured in or near the heart. The signals further treated may be voltage signals, current signals or other relevant electromagnetic values (or series of values).

FIG. 2 shows a series of sample EKG signals. The first EKG signal 201 indicates a normal patient. Second EKG signal 2
02 indicates a patient in transition to VF. Third EKG signal 2
03 indicates a patient suffering from VF.

The processor 103 may construct a phase diagram (PPP) from the EKG signal. The first type of PPP is a plot of the EKG variable against its first derivative. In the preferred embodiment, the EKG variable is voltage v (a function of time) and its first derivative is dv / dt (also a function of time). However, upon perusal of the specification, drawings and claims, it will be apparent to one of ordinary skill in the art that the permissible range of PPP interpretation is broad. P
The variable chosen for PP can be a variety of different parameters including EKG voltage, current, or other signal value.
The selected variable (v) is its first derivative (dv / dt), its second derivative d ² v / dt ² or another nth derivative d ⁿ v /
It can be plotted against dt ⁿ . Alternatively, the Mth derivative may be plotted against the Nth derivative.

Another form of PPP is an EKG variable (or
N-th derivative) its own time delay value of (time delay
ed version ) plotted against (eg v (t) vs.
v (t−δt)). This type of PPP is sometimes called "return diagram".
(Hereinafter, referred to as a return map.) ”. This type of PPP is not sensitive to EKG signal noise. Other types of PPP have three EKG variables (or their Nth derivative) at the same time (eg v, dv / dt and d ² v / dt ² ).
Can include a plot of Such a PPP is three-dimensional. If the PPP is three-dimensional, it may be displayed stereoscopically, and the two-dimensional "cut" of the three-dimensional display may be displayed on the two-dimensional display. It will be apparent to those skilled in the art that all these choices or combinations thereof described herein are feasible and within the scope and spirit of the invention.

FIG. 3 shows corresponding PPP groups for the sample EKG signal of FIG. The first PPP 301 corresponds to the first EKG signal 201. The second PPP 302 corresponds to the second EKG signal 202. The third PPP 303 corresponds to the third EKG signal 203.

Part of this aspect of the invention is that a normal patient has a PPP that indicates the regularity and smoothness of the EKG signal from that normal patient, while the patient undergoing VF has a deterministic chaos (eg, P, which indicates the irregularity and complexity of the EKG signal which is periodic, banding and "forbidden zones")
The discovery of having PP. In addition, patients who are in the transition from normal to VF (ie, during the onset of VF) show PPP consistent with their assessment that their EKG signal is in the transition to deterministic chaos.

Normal patients have a relatively regular beat-to-beat EKG signal. When the patient switches to VF,
The patient's EKG signal initially exhibits variations between pairs of regular beat-to-beat signal alternations. If conversion continues, patient's EK
The G signal shows fluctuations between regular signals with an increasing number of fluctuations (eg, 4 alternating beats, 8 alternating beats, etc.), and at the end it is no longer possible to see the alternating regular signals and the EKG signal Is irregular and extremely complicated. At this point, the patient is V
It is commonly referred to as indicating F. Similarly, the patient's PP
P transitions from a smooth single band display to a multiple band display (indicating multiple alternating beats) and finally to an irregular, highly complex display. The display changes in PPP are so remarkable that even a relatively untrained person can see the difference. This is significantly different from the display changes in EKG which generally require a skilled cardiologist to evaluate.

There are several possible factors that transform a patient from normal to VF. These factors include drug overdose (especially overdose with antiarrhythmic drugs that have proarrhythmic effects at overdose, such as quinidine poisoning), excessive electrical stimulation,
It may include cooling, ischemia and stress. In a preferred embodiment, the patient monitor may examine a patient's PPP to determine if the patient has a change from normal to VF. This indicates that these proarrhythmic factors are in excess.

Processor 103 may further process the PPP to determine the degree of PPP in deterministic chaos. Several techniques can be applied for this purpose.

(1) The processor 103 can measure the Lyapunov exponent of PPP. Lyapunov exponent of PPP is P
It is a measure of the degree to which the road near PP diverges. The Lyapunov exponent is well known in chaos theory and can be measured with available software. For example, Wolff et al., "Determining Lyapunov Exponents From A Time Series", Fijika Day 1985;
16: 285-317.

(2) The processor 103 can measure the fractal dimension of PPP. The fractal dimension of PPP is PP
It is a measure of the extent to which P forms a "space filling" curve. Fractal dimension is well known in chaos theory, and can be measured by several techniques (eg, correlation dimension or box calculation method) as shown below.

To measure the fractal dimension of PPP,
The processor 103 burns a linear grid (consisting of a series of boxes) onto the PPP and counts the number of boxes cut by the PPP trace. The processor 103 changes the size of the grid and records the size of each grid and each total. The processor 103 then calculates the constant k in the following relationship.

Ln (# (number) of box cuts) = k * ln (# (number) of boxes in grid 304) ln (# of box cut cuts) = k * ln (# of boxes in grid 304) constant k is a measure of the fractal dimension of PPP. About 3
To about 7, especially the value of k with fractional part is PP
It is believed to mean that P represents a deterministic chaos-based process, and thus a patient close to VF (ie, a patient who is substantially VF).

(3) The processor 103 may measure the Poincaré cross section of the PPP and inspect the Poincaré cross section for an indicator of deterministic chaos as described herein. The processed PPP and Poincaré sections are also shown for inspection by a human operator so that all visible structures are easily recognizable.

FIG. 4 shows a sample PPP 401 and corresponding Poincare section 402. The Poincaré cross section includes a line segment drawn across a portion of PPP. Generally, such a line segment is substantially perpendicular to the PPP trajectory in the target area.

The processor 103 acquires data points at each Poincare section or PPP and calculates a statistical measure of the anisotropic or non-uniformity of these data points. Such indicators are based on the mean and standard deviation of these data points (which can be calculated by statistical methods well known in the art). The ratio γ is γ = (standard deviation) / (expected value) (403), which is a measure of the degree of aggregation in the Poincare section.

Larger values of γ are more likely to represent processes in which PPP is based on deterministic chaos, and thus patients near (or in fact VF). The value of γ is
As is well known in the art, it is displayed for human operator observation in comparison to the value of γ for normal patients, with confidence bands to indicate the extent of change from normal patients.

The processor 103 may also calculate other statistical measures of Poincare section. Processor 103 may also inspect the "aged" Poincare section of PPP.

FIG. 5 shows an example of the PPP 501 and its correspondence.
3 shows a Poincaré cross-section 502 over time. Time Poincare section is more PPP to select one data point every t seconds
May include a data point group selected from Aged Poincaré sections can be analyzed in a similar manner as other Poincaré sections disclosed herein.

A second aspect of the invention relates to the detection and evaluation of heart disease based on the frequency domain transform of patient EKG.

FIG. 6 shows a corresponding frequency domain transform group obtained by performing FFT on the EKG signal. The first transform 601 corresponds to the first EKG signal (not shown). The second transform 602 corresponds to the second EKG signal (not shown).

In the first transform 601, which represents a normal patient, the frequency spectrum shows that the energy of the corresponding EKG signal occurs predominantly at the frequencies of the discontinuities. In the second transform 602, which represents a patient exhibiting VF, the frequency spectrum shows that the energy of the corresponding EKG signal has a continuous spectrum of frequencies and has an energy peak 603.

The part of this aspect of the invention, for example, harmonic vibration
Use of visual and mathematical methods to analyze frequency domain transformations, including calculation of width ratio (HMR). To determine the HMR, the main peak or central region of the energy distribution in the spectrum of the frequency domain transform (eg FFT) was identified and the HMR was calculated as follows. The magnitude of the transform (magnitude) in the region of the specific point is determined, for example, by summing the magnitudes of the transforms in the particular point and the surrounding points, and in the region of the harmonic value of the frequency for the particular point,
Summed with the corresponding size. Dividing this sum by the total magnitude of the transform for the entire signal. The ratio is defined as HMR.

One known method for withdrawing VF from a patient ("defibrillation") is to apply electrical stimulation to the patient's heart. This electrical stimulus generally has substantial energy, eg, 10-20 Joules, and often causes tissue damage to the patient, even if successful in defibrillating the patient. Multiple stimuli that generally increase energy are required. Therefore, it is advantageous to use large stimuli only when needed and to use as few stimuli as possible. Part of this feature of the invention is the discovery that relatively low energy stimuli are generally sufficient to defibrillate a patient when the energy peaks 603 of the frequency domain transform 602 are at relatively low frequencies. When the energy peak 603 of the frequency domain transform 602 is at a relatively high frequency (and when the second energy peak 604 appears at the frequency domain transform 602 at a relatively high frequency), the electrical stimulation can defibrillate the patient anyway. Defibrillating the patient if possible requires relatively high energy stimulation.

One application of this finding is to automatically detect VF and attempt to automatically apply a stimulus to defibrillate the patient. An Implantable Cardiac Defibrillator (AIC).
In D).

FIG. 7 shows an improved AICD 701. The AICD EKG 703 connected to the patient 702 is
The signal is acquired and sent to an AICD processor 704 which regulates a stimulator 705 that provides defibrillation stimulation to the patient 702.

The improved AICD 701 (for example, A
Includes software to measure the FFT of the EKG signal (as part of ICD processor 704) and the energy peak at that FFT. When the energy peak in the FFT is relatively low, the AICD processor 704 adjusts the stimulator 705 to provide a relatively small stimulus to the patient. If the energy peak in the FFT is relatively high, the AICD processor 704 will cause the stimulator 7
05 may be adjusted to provide a greater stimulus to the patient, but may also signal an alarm 706 or other indicator that defibrillation may not be successful.

[0041] A third feature of the present invention, created with the patient
And evaluation of drug toxicity based on the parameter time constant of the action potential duration (APD) recovery curve or action potential amplitude (APA) curve performed.

FIG. 8 shows the signal response to stimulation of individual heart muscle cells. This individual cellular response is known in the art as the "action potential."

It is well known in the art that the recovery period 801 of an individual cell depends on factors including the rest period 802 that the cell has before stimulation. For human patients
To the APD restitution curve of Te can structure <br/> formed by the use of intracardiac catheter it is also well known in the art. However, the complete relationship between the rest period 802 and the actual recovery period 801 is not known.

Part of this feature of the invention is that the recovery period 80
When 1 is plotted against the rest period 802 (diastolic interval), the curve is based on the finding that it follows an exponential relationship.

APD = APD _pl −A * exp (−DI / tau) (803) where APD _pl is Prato-APD, A is a proportional constant, and DI.
Is the diastolic interval and tau is the parameter time constant.

The non-linearity of the APD recovery curve promotes the development of deterministic chaos in response to cardiomyocyte hyperstimulation .
It If the APD recovery curve becomes steep (ie
If a large meter time constant), heart V accordingly
The tendency to become F becomes large. Therefore, another feature of the present invention is that normal patients have relatively low APD recovery curve parameters.
Patients with a time constant but drug toxicity (eg, quinidine intoxication) have parameters with a relatively high APD recovery curve.
The discovery of having a data time constant . The parameter time constant is also used to monitor cardiac stability.
It can also be used to assess the efficacy of antiarrhythmic drugs.

The experimental transformation of the present invention is accomplished by the inventors.

Experiment 1To distinguish periodic signals, chaotic signals and arbitrary signals,
Mathematical researchToPPPs,Return map(Return ma
p), Poincaré cross section, correlation dimensionAndSpectral analysis
Was used. PPPsOf all three types of signalsUseful in distinguishing
It was The periodic signal showed obvious widely separated orbits.
Chaotic signal banding,Ban zone& Keen
The initial condition dependence was shown. Arbitrary signal has clear internal structure
Was not shown. Suitable for PPPs, except for noise effects
LateReturn mapThe big difference between45 degrees ( de
gree ) Rotation onlyMet. Poincare section3 kinds of belief
IssueI was able to distinguish. The periodic signal showed a separation point. chaos
The signal is clearly self-similar and ordered ar
ea). The arbitrary signal has a Gaussian distribution of points. phase
Function dimensions are more chaotic and deterministic than between chaotic and periodic signals.
A better distinction was made between signals. FFTs andHarmonic amplitude ratio
Spectral analysis using (HMR) divides periodic signals into
It was possible to separate, but between arbitrary and chaotic signals
Could not be distinguished. HMRs of periodic signals
Was greater than 97%.

HMRs of chaotic signals are between 17 and 80%
In it has changed. The HMRs of arbitrary signals were about 40%.
PPP was significantly affected by noise. Return map
Flop was not affected most. On the other hand, spectral analysis was relatively immune to noise. PPPs, return map
It was concluded that Pu , Poincaré cross section, correlation dimension and spectral analysis are all useful determinants of chaotic systems.

Experiment 2 Mathematical studies have particularly focused on the possibility of spectral analysis to distinguish chaos from arbitrary signals. In this experimental example,
Two series of arbitrary signals were made. The first sequence contains 5000 pseudo-random numbers simplified using the method of least squares approximation. The second series contained white noise obtained from an analog-to-digital converter board. Spectral analysis was performed by applying FFT to the data and examining the change from a broad band spectrum or from a narrow band to a broad band estimated to be a judgment of chaos.

Spectral analysis alone is not sufficient to clearly distinguish chaotic signals from arbitrary signals,
And to distinguish between them, additional tests such as PPP and Rita
It was concluded that a map of maps is needed.

[0052] distinguish between normal curved rhythm and VF in experiment 3 experiment dog park
Spectral analysis, visualization of PPPs and correlation dimension analysis were examined for utility in combination . Ischemia and reperfusion was used as a stress factor of closed-chest anesthetized dogs. Spectral analysis of dogs with a normal bending rhythm showed that the bending ratio and 50 Hz were exceeded.
It shows a narrow band spectrum including the fundamental frequency
It was PPPs are consistent with periodic dynamics, and dimensional analysis
It showed a low dimension (1-2 . 5) . On the other hand , VF
Spectral analysis of the dogs with, there is most energy to 6 Hz, energy across all frequencies 1～25H z
It shows a wide band characteristic that shows a ghee . PPP was forced
Shows the non-periodic, dimensional analysis showed a high level (4-6) than the dimension observed in dogs normal curvature rhythm. Obey
Thus , all three techniques have been found to be useful in distinguishing normal bending rhythms from VF .

[0053] Experiment 4 Experiments scan <br/> spectrum analysis the usefulness of checking the VF human visualization of PPPs, visualization of return maps, and examined the correlation dimension. These analysis techniques
It was applied to data from 8 hypothermic patients undergoing spontaneous VF and data from 3 normal temperature patients with VF induced during electrophysiology. All patients
It had a wide band frequency spectrum (0-12 Hz), low dimensions (range 2-5) and banding and forbidden zones in the PPPs and return maps . V for spectral analysis, visualization of PPPs, visualization of return map and correlation dimension analysis
It was concluded to be useful in detecting and assessing F.

[0054] Experiment 5 Experiment distinguishes between normal curvature rhythm and VF in humans Field
Spectral analysis, visualization of PPPs and correlation dimension analysis were examined for utility in combination . VF was studied in eight hypothermic human patients undergoing open heart surgery. In all patients, the first and secondary PPP is prohibited areas
Banding and banding were shown, and the FFT showed a relatively continuous power spectrum at all frequencies from 0 to 25 Hz, with a power of often less than 12 Hz. Symmetrically, in all cases, the correlation dimension was less than 4. It was concluded that a multi-dimensional analysis of data (= spectral analysis + correlation dimension analysis) is preferable rather than relying on a single analysis technique such as correlation dimension.

Experiment 6 The experiment used spectral analysis and visualization of PPPs to explain the heterogeneous nature of atrial fibrillation. In the experiment, the inspector caused acute fibrillation by a rapid procedure of stimulation of the atria of 7 closed-chest dogs. PPP based on EKG data
s often fills well-defined structures, and digitized EKG FFTs show peaks below 15Hz, which were distinct from the apparent harmonic components or had a continuous spectrum that varied in a time and place-dependent manner. Indicated. It was concluded that PPP spectral analysis and visualization is a useful technique for analyzing atrial and ventricular fibrillation.

Experiment 7 In an experiment, visual analysis of PPPs and the slope of the APD recovery curve were found to be useful in detecting and assessing quinidine-attracting VF in the in vivo heart. Quinidine is
90-100 mg / kg in total or until ventricular tachycardia or VF, whichever comes first, at 30 minute intervals for 5 hours. PPP of quinidine poisoned cells
s indicates a sharp dependence on the initial condition and the existence of a prohibited zone ,
The corresponding FFTs showed a continuous spectrum. In contrast, the PPPs of cells in control dogs were uniform and dense and the corresponding FFTs showed distinct spectra.

The initial slope of the APD recovery curve of quinidine-excited cells was, at least in magnitude, much steeper than that of normal cells. It was concluded that quinidine toxicity was associated with the slope of the APD recovery curve.

Experiment 8 The experiment compared the slopes of the APD recovery curve and the APA curve with quinidine poisoning. Quinidine was administered to 8 dogs over 5 hours (90-100 mg / kg). Three untreated dogs served as controls. Ventricular and Purkinje cells from treated and untreated dogs were then 900-600 mse
It was attached to an electric stick in the cycle of c. Cycle length reduction to 600 msec includes APD including electrical alternating beats and branches
And APA random dynamics. AP
The slope of the D-recovery curve was calculated and found to be steeper in quinidine-excitable cells to Purkinje fibers and ventricular muscle cells than during quinidine efflux or in normal untreated cells. The curves were fitted by the exponential equation given here. APA changes almost always correlated with APD changes. In three normal tissue preparations,
Neither ventricular muscle cells nor Purkinje cells showed a divergent response with respect to APD or AA. Quinidine toxicity, and possibly other pre-drug-induced arrhythmic effects, were determined by APD recovery curves and A
It was concluded that there is a correlation with the slope of the PA curve, respectively .

Experiment 9 In the experiment, quinidine-induced ventricular palpitations and VF in dogs were treated with action potential duration (APD = action potential).
duration) and action potential amplitude (APA) data generated PPPs. Both PPPs showed a forbidden zone and a sharp initial condition dependence implying chaos.
It was concluded that PPPs based on APD or APA are useful in detecting and assessing quinidine toxicity.

Experiment 10 In the experiment, EKGs of quinidine-excited dogs were analyzed by frequency spectrum, phase diagram, Poincare section, return map and Lyapunov index. In control conditions and at therapeutic doses, PPPs were uniformly thick and showed no gaps indicating that cycle-to-cycle changes were due to normal biological "noise."

However, the quinidine dose was at an intermediate level (40-
50 mg / kg), the PPPs showed a non-uniform thickness showing a sharp initial condition dependence, and also marked bands (dark solid areas separated by boundaries or gaps). At these intermediate doses the Lyapunov index was positive and the Poincaré section and return map showed non-arbitrary chaos. Higher doses made PPPs more complex. In the two dogs that showed VF (and not the other), there was a clear change in the last prefibrillation dose in PPP, a "funnel" development, and a standard mechanism of chaos.
The frequency spectra for all prefibrillation doses were distinct with peaks at the fundamental frequency and polyharmonic. It was concluded that chaotic doses occur during progressive quinidine intoxication, and PPPs and PPPs-based graphical and numerical analyzes are better indicators of chaos than frequency spectra.

Experiment 11 In an experiment, quinidine poisoning in dogs was tested for APA and A
Analysis was performed using PPPs generated from PD data. EK
G records show various transmission rates (dri) from 1000 to 560 msec.
ving rate). The increase in transmission rate from 1000 to 500 msec caused a progressive appearance of high order periodicity (periods 3 and 4). Phase-locking was seen in the cyclically repeated stimulus (S) response (R) pattern for all four preparations with S: R ratios of 2: 1, 5: 3, 3: 2. At faster transmission rates, aperiodic changes were seen in APA and APD. Many intermediate steps that are predictive of chaos were found in quinidine excitable fibers. These results further demonstrate the utility of the method of the present invention for detecting quinidine poisoning and precursor stages of poisoning.

Experiment 12 In an experiment, quinidine toxicity in dogs was tested for APA and A
Analysis was performed using PPPs generated from PD data. Electrical stimulation was used to move the heart tissue at various rates from 2000 to 300 msec. Each of these stimuli is 10
8 ± 36 msec and 12 ± 9 millivolt APD and A
Stable alternating beats (branches) occurred in PA.
The more increase in transmission rate caused the irregular driving force. This transition was preceded by 50 consecutive beats with various repeated stimulus-response ratios (phase-fixed). Such a driving force is
It was not attracted in the three untreated (control) tissues. The APD recovery curve had a significantly (P <0.05) steeper slope than the 6 control fibers. The stimulus-response time was 6-9 msec and remained constant. PPPs of APDs between random dynamics showed a sharp initial condition dependence and a forbidden zone consistent with chaos theory. These results demonstrate the utility of the method of the invention for detecting quinidine poisoning and precursors to poisoning.

Experiment 13 The experiment used spectral analysis, PPPs, Poincare section, Lyapunov exponent and dimension analysis to analyze computer simulated experimental waveforms including sine wave, modulated sine wave, square wave, sawtooth wave, and triangular wave. I was there. The inspector added arbitrary noise to the waveform at 1%, 10% and 20%. The experiments further show that VF has five different implications: EKG data from anesthetized dogs caused by quinidine poisoning, quinidine poisoning followed by premature electrical stimulation, coronary occlusion, acute ischemic myocardial reperfusion and global hypothermia. The same analysis technique was used. Preliminary results showed that PPPs and Poincaré sections in dogs undergoing ventricular fibrillation were consistent with chaos, while spectral analysis did not suggest chaos. The investigators conclude, in part, that VF can be described as a chaotic electrophysiological function, but a single method of analysis is not sufficient to detect such function.

One conclusion that can be drawn from the tests cited herein is that the analytical value of each of the features of the invention is enhanced in combination with one or more of the other features of the invention.

Preferred embodiments of the invention may include combinations of the inventive features described herein. One preferred embodiment is a multi-faceted analysis of PPP (eg, visually on the display, graphically in Poincaré section and numerically in Lyapunov exponent and correlation dimension), frequency spectrum analysis and mathematical analysis of APD recovery curves. Can be included.

Other Embodiments While the preferred embodiment has been described herein, many variations are possible and are within the concept and scope of the invention, which variations are described in the specification, drawings and claims. It will be obvious to those skilled in the art if the range is read carefully.

It is also possible that embodiments of the present invention may include means for continuous monitoring of drug toxicity, atrial fibrillation, ischemia or other cardiac conditions such as surgery or patient recovery from surgery. It will be apparent to those skilled in the art. In addition, embodiments of the present invention may include means for indicating a detected cardiac condition to the attending medical personnel or patient. In a preferred embodiment of the invention, the patient is provided with a means of contacting a doctor (when a heart disease is detected) or going to a nearby hospital for treatment.

[Brief description of drawings]

FIG. 1 is a diagram showing a patient monitor device.

FIG. 2 is a diagram showing a series of sample EKG signals.

3 is a series of correspondences P to the sample EKG signal of FIG.
Diagram showing PP

FIG. 4 is a view showing a cross section of a sample PPP and a corresponding Poincare.

FIG. 5 is a view showing a cross section of a sample PPP and corresponding Poincare aging.

FIG. 6 shows a series of corresponding frequency domain transforms obtained by performing a Fast Fourier Transform (FFT) on an EKG signal.

FIG. 7: Improved Implantable Cardiac Defibrillator (AIC)
Diagram showing D)

FIG. 8 shows the signal response to stimulation of individual cardiac muscle cells, known in the art as action potentials.

[Explanation of symbols] 102 ... Electrocardiogram (EKG) device 103 ... Processor 104 ... Monitor 703 ... Automatic implant cardiac defibrillator 704 ... Processor 705 ... Stimulator

─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor George Alexander Diamond 2408, Wild Oak Drive, Los Angeles, California 90068 United States 90068 (72) Inventor Stephen Shade Can United States 90064 Los Angeles, CA, USA Manning Avenue 2241 (72) Inventor Timoti Alan Denton United States 90048 California, Los Angeles, Norwich Drive 513 (72) Inventor Stephen Evans United States 20022 New York, New York, East Fifty Second Street No. 325 (56) Reference JP 63-203133 (JP, A) JP 59 189831 (JP, A) JP Akira 59-216282 (JP, A) JP Akira 47-28778 (JP, A) (58 ) investigated the field (Int.Cl. ^7, DB name) A61B 5/04 - 5 / 0472

Claims (6)

(57) [Claims] 1. A means for receiving an electrocardiogram signal, a signal processing means for processing the electrocardiogram signal to determine a phase diagram of the electrocardiogram signal, and a display means for displaying the phase diagram.
From the multidimensional diagram of at least three variables
An electrocardiogram signal analysis device characterized in that 2. The variables are voltage and current of an electrocardiogram signal,
Other relevant electromagnetic values or series of values and their derivatives
The electrocardiogram signal analyzer according to claim 1, wherein the variables are at least three variables selected from the above . 3. The variable is the voltage of the electrocardiogram signal and its
The electrocardiogram signal analysis device according to claim 1 or 2, comprising a derivative of 4. The derivative is the voltage of the electrocardiogram signal,
The electrocardiogram signal analysis device according to claim 3 , wherein the electrocardiogram signal analysis device has a first derivative and a second derivative . 5. A means for receiving an electrocardiogram signal from an electrocardiogram device, the composed and the electrocardiogram signal signal processing means for processing, since the electrocardiogram signal to determine whether to instruct heart disease, said signal processing means , a plurality of the following, namely, (a) the multi analysis of phase plane view of the electrocardiogram signal; (b) spectral analysis of the ECG signal, and (c) action potential duration which is calculated in accordance with the electrocardiogram signal Time
An electrocardiogram signal analysis device comprising means for performing an inter- restoration curve analysis. Wherein said signal processing means comprising comprises means for calculating a harmonic amplitude ratio for performing the spectrum analysis according to claim 5
The electrocardiographic signal analysis device according to 1.