WO2013170232A1 - Changement d'entropie d'activation ventriculaire (rr) comme facteur de prédiction d'une mort subite cardiaque chez des patients en thérapie de resynchronisation cardiaque - Google Patents

Changement d'entropie d'activation ventriculaire (rr) comme facteur de prédiction d'une mort subite cardiaque chez des patients en thérapie de resynchronisation cardiaque Download PDF

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Publication number
WO2013170232A1
WO2013170232A1 PCT/US2013/040677 US2013040677W WO2013170232A1 WO 2013170232 A1 WO2013170232 A1 WO 2013170232A1 US 2013040677 W US2013040677 W US 2013040677W WO 2013170232 A1 WO2013170232 A1 WO 2013170232A1
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WO
WIPO (PCT)
Prior art keywords
entropy
cardiac rhythm
interval
measurement
segment
Prior art date
Application number
PCT/US2013/040677
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English (en)
Inventor
J. Randall Moorman
Douglas E. Lake
Gordon F. TOMASELLI
Deeptankar DEMAZUMDER
Original Assignee
University Of Virginia Patent Foundation
The Johns Hopkins University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by University Of Virginia Patent Foundation, The Johns Hopkins University filed Critical University Of Virginia Patent Foundation
Priority to EP13788256.9A priority Critical patent/EP2846685A4/fr
Priority to US14/400,408 priority patent/US9839364B2/en
Publication of WO2013170232A1 publication Critical patent/WO2013170232A1/fr

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    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • 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
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/364—Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • RR Ventricular Activation
  • the present invention relates generally to cardiology. More particularly, the present invention relates to the dynamic analysis of cardiac rhythm to predict morbidity and mortality.
  • Electrocardiograms have long been studied in order to analyze cardiac function and predict health, disease and mortality.
  • linear methods in the time and frequency domains are used to analyze the information from the electrocardiogram.
  • One such linear method is referred to as heart rate variability (HRV).
  • HRV heart rate variability
  • time domain analysis a range of normal values for HRV analyzed in the time domain, frequency domain and geometrically are established based on 24-hour ambulatory recordings. Similar metrics, particularly in the time domain, are not universally accepted for short- term recording so stratification of continuous data can be used.
  • the irregularity in the time-sampled intervals of electrocardiographic ventricular activation can be accounted for in frequency domain analyses in order to calculate an estimate of the power spectrum density (PSD).
  • PSD power spectrum density
  • PSD estimations are performed as a method of cardiac assessment using the FFT (Welch's periodogram) and the parametric maximum-entropy "forward-backward linear least squares" autoregressive (AR) methods.
  • FFT Frequency-to-frequency transform
  • AR autoregressive
  • spectrum powers are calculated by integrating the spectrum over the frequency bands.
  • the parametric AR method models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases, and fits a function of frequency with a predefined number of poles (frequencies of infinite density) to the spectrum.
  • the AR method asserts that the position and shape of a spectral peak is determined by the corresponding complex frequency and that the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.
  • the spectrum is divided into components and the band powers are obtained as powers of these components.
  • Nonlinear dynamic analyses are an alternate approach for understanding the complexity of biological systems.
  • a nonlinear system has an output that is simply "not linear,” i.e., any information that fails criteria for linearity, i.e., output is proportional to input (e.g., Ohm's law), and superposition (behavior predicted by dissecting out individual input/output relationships of sub-components).
  • FIG. 1A illustrates fractal temporal processes of a healthy RR.
  • FIG. 1 B illustrates wavelet analysis of healthy RR time series of >1500 beats (x-axis is time, y-axis is wavelet scale (5 to 300 sees).
  • FIG. 1 C illustrates the wavelet amplitudes.
  • FIGS. 2A-2E It is quite common for the output of nonlinearly coupled control systems to generate behaviors that defy explanation based on conventional linear models, as illustrated in FIGS. 2A-2E.
  • Characteristic behaviors of nonlinear systems include self- sustained, periodic waves (e.g., ventricular tachycardia), abrupt changes in output (e.g., sudden onset of ventricular fibrillation) and, possibly, chaos.
  • FIGS. 2A-2E illustrate an RR time series demonstrating quantifiable nonlinear dynamics that are distinct within patients with OSA, as illustrated in FIGS. 2A-2C, and also distinct within healthy individuals at high altitude, as illustrated in FIGS. 2D-2E.
  • nonlinear systems that appear to be very different in their specific details may exhibit certain common output patterns, a characteristic referred to as universality.
  • outputs may change in a sudden, discontinuous fashion (e.g., bifurcation), often resulting from a very small change in one of the control modules.
  • the same system may produce a wildly irregular output that becomes highly periodic or vice versa (e.g., electrical alternans, ST-T wave alternans preceding ventricular fibrillation, pulsus alternans during congestive heart failure)
  • the Poincare plot is a graphical representation of the correlation between successive RR intervals, i.e. plot of RRn+1 as a function of RRn.
  • the significance of this plot is that it is the two-dimensional reconstructed phase space (i.e., the projection of the system attractor that describes the dynamics of the time series).
  • DFA Detrended fluctuation analysis
  • CM short-term
  • ⁇ 3 ⁇ 4, range 16 ⁇ n ⁇ 64 long- term fluctuation
  • 0 ⁇ a ⁇ 0.5 indicates a large value is followed by a small value and vice versa
  • 0.5 ⁇ a ⁇ 1 .0 indicates a large value is likely to be followed by a large value.
  • An a value of 0.5, 1 .0, >1 .0, or >1 .5 indicates white noise, 1/f noise, different kinds of noise, or brown noise (integral of white noise), respectively.
  • thermodynamic entropy the information entropy can be calculated for any probability distribution (i.e., occurrence of an event that had a probability of occurring out of the space of possible events).
  • the infornnation entropy quantifies the amount of information needed to define the detailed microscopic state of a system, given its macroscopic description, and can be converted into its thermodynamic counterpart based on the Boltzmann distribution. Recent experimental evidence supports this method of conversion.
  • ShanEn measures information as the decrease of uncertainty at a receiver (or physiological process).
  • ShanEn of the line length distribution is defined as where is the number of length / lines such that
  • the reduced AG would be equal to the minimum number of yes/no questions (using log 2 ) that needed to be answered in order to fully specify the microscopic state, given the macroscopic state.
  • An increase in Shannon entropy indicates loss of information.
  • ApEn approximate entropy
  • SampEn is the conditional probability that that two short templates of length m that match within an arbitrary tolerance r will continue to match at the next point m + 1 .
  • SampEn is calculated by first forming a set of vectors u s of length m where m represents the embedding dimension and N is the number of measured RR intervals. The distance between these vectors is defined as the maximum absolute difference between the corresponding elements
  • COSEn an optimized form of the SampEn measure, was originally designed and developed at the University of Virginia for the specific purpose of discriminating atrial fibrillation (AF) from normal sinus rhythm (NSR) at all heart rates using very short time series of RR intervals from surface ECGs (i.e., as few as 12 heart beats).
  • AF atrial fibrillation
  • NSR normal sinus rhythm
  • COSEn smaller values indicate a greater likelihood that similar patterns of RR fluctuation will be followed by additional similar measurements. If the time series is highly irregular, the occurrence of similar patterns will not be predictive for the following RR fluctuations and the COSEn value will be relatively large.
  • a method of nonlinearly determining health and mortality includes obtaining a ventricular activation (RR) time series from a subject for multiple temporal intervals.
  • the method also includes calculating a cardiac entropy in the RR time series over the temporal intervals using coefficient of sample entropy (COSEn).
  • COSEn coefficient of sample entropy
  • the method includes comparing the cardiac entropy between the intervals to determine health and mortality.
  • the absolute and relative changes in entropy over a patient's follow up period provide dynamic information regarding health and mortality risk.
  • the determination of health and mortality can then be used to create a treatment plan for the subject.
  • the treatment plan created can include monitoring the subject's cardiac rhythms and other physiological signals, including but not limited to respiration, blood pressure, oxygen saturation, temperature and electroencephalogram.
  • the subject can further be one selected from the group consisting of primates, dogs, guinea pigs, rabbits, horses, cats and other organisms.
  • FIG. 1 A illustrates fractal temporal processes of a healthy RR according to an embodiment of the present invention.
  • FIG. 1 B illustrates wavelet analysis of healthy RR time series of >1500 beats (x-axis is time, y-axis is wavelet scale (5 to 300 sees) according to an embodiment of the present invention.
  • FIG. 1 C illustrates the wavelet amplitudes according to an embodiment of the present invention.
  • FIGS. 2A-2E illustrate an RR time series demonstrating quantifiable nonlinear dynamics that are distinct within patients with OSA, as illustrated in FIGS. 2A-2C, and also distinct within healthy individuals at high altitude, as illustrated in FIGS. 2D-2E according to an embodiment of the present invention.
  • Fig. 3 shows analysis of heart rate variability.
  • Fig. 4 shows graphs of calculated change in entropy over time for patients at risk of SCD, in accordance with an aspect of the invention.
  • Fig. 5 is a diagram illustrating vital demographics of patients in an observational study in accordance with the invention.
  • Fig. 6 shows Kaplan-Meier survival curves for the patients in Fig. 5.
  • Fig. 7 shows Hazard Ratios for multiple parameters of SCD patients.
  • Fig. 8 shows Hazard Ratios for multiple parameters of patient death in the study due to all causes.
  • FIG. 9 is a block diagram of an illustrative computer system capable of implementing the methods of the present invention.
  • a method allows for the nonlinear assessment of health and mortality.
  • ventricular activation (RR) time series from a subject for a temporal interval are obtained.
  • a first and second cardiac entropy in the RR time series over the temporal interval are determined.
  • the first and second cardiac entropy are compared, to determine health and mortality. This information can then be used to determine a treatment plan for the subject, such as increased monitoring for pathophysiological states.
  • a method for assessing the risk of sudden cardiac death (SCD) by comparing cardiac RR interval rate of entropy change over a predefined time interval for a patient receiving Cardiac Resynchronization Therapy (CRT) to determine changes in entropy of normal sinus rhythm (NSR) and determining increased risk of SCD when the NSR entropy of the patient has increased.
  • CTR Cardiac Resynchronization Therapy
  • the coefficient of entropy is a calculation of an entropy rate (or entropy) of an RR interval series after it has been unit mean normalized (dividing each observation by the mean of the series). This is analogous to the coefficient of variation, which is the standard deviation after normalization by the mean.
  • the calculation of the coefficient of entropy is accomplished by subtracting the natural logarithm of the mean from the original entropy calculation.
  • the coefficient of entropy calculated for Q in this way is especially effective and we give it the name coefficient of sample entropy or COSEn for short and denote it by Q * .
  • the dynamics of cardiac rhythms can be quantified by entropy and entropy rate under the framework of continuous random variables and stochastic processes.
  • the entropy of a continuous random variable X with density f is
  • entropy rate is the entropy of the conditional distribution of the present observation given the past.
  • the entropy rate for i.i.d. sequences reduces to the entropy of the common distribution.
  • E_- ⁇ og(f(X x ,X 2 ,...,X ) can still be estimated empirically. These are the fundamental calculations in ApEn and SampEn.
  • the patients (age 51 ⁇ 12 yrs, male 66%, white 82%, DM 26%, HTN 46%, ICM 32%, EF 20 ⁇ 8%, NYHA class 2.3 ⁇ 0.8) were well treated medically for heart failure.
  • Entropy was measured using coefficient of sample entropy (COSEn), based on Kolmogorov-Sinai entropy with roots in chaos theory.
  • entropy change was measured as the slope of linear regression fit to values at baseline and subsequent clinic visits excluding shock.
  • ICD shocks or deaths from ventricular tachyarrhythmias were used as a specific surrogate for SCD.
  • entropy change was measured as ⁇ / ⁇ , the change in entropy over two routine 6 month clinical visits preceding an ICD shock (and excluding the clinical visit after the ICD shock).
  • the ⁇ / ⁇ for the time interval preceding an ICD shock was a strong predictor of increased risk of SCD (where ICD shock is used as a surrogate for SCD).
  • Fig. 5 is a graph illustrating the
  • Fig. 6 shows Kaplan-Meier curves for these patients, per quartile of ⁇ / ⁇ . As shown, patients in the 4 th quartile for ⁇ / ⁇ had the lowest survival probability.
  • Fig. 7 shows hazard ratios for SCD for multiple parameters, and Fig. 8 shows hazard ratios for all deaths for multiple parameters. As shown, ⁇ / ⁇ alone as a predictor had a confidence interval (CI) of 95%.
  • CI confidence interval
  • FIG. 9 is an illustrative block diagram for a computer system 100 for implementation of an exemplary embodiment or portion of an embodiment of present invention.
  • a method or system of an embodiment of the present invention may be implemented using hardware, software or a
  • the invention was implemented in software running on a general purpose computer 100 as illustrated in FIG. 1 .
  • the computer system 100 may include one or more processors, such as processor 104.
  • the Processor 104 is connected to a communication infrastructure 106 (e.g., a communications bus, cross-over bar, or network).
  • the computer system 100 may include a display interface 102 that forwards graphics, text, and other data from the communication infrastructure 106 (or from a frame buffer not shown) for display on the display unit 830.
  • the computer system 10 may also include a main memory 108, preferably random access memory (RAM), and may include a secondary memory 1 10.
  • the secondary memory 1 10 may include, for example, a hard disk drive 1 12 and/or a removable storage drive 1 14, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc.
  • the removable storage drive 1 14 reads from and/or writes to a removable storage unit 1 18 in a well-known manner.
  • Removable storage unit 1 18, represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 1 14.
  • removable storage unit 1 18 includes a computer usable storage medium having stored therein computer software and/or data.
  • secondary memory 1 10 may include other means for allowing computer programs or other instructions to be loaded into computer system 100.
  • Such means may include, for example, a removable storage unit 122 and an interface 120.
  • removable storage units/interfaces include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as a ROM, PROM, EPROM or EEPROM) and
  • removable storage units 122 and interfaces 120 which allow software and data to be transferred from the removable storage unit 122 to computer system 100.
  • the computer system 100 may also include a communications interface 124.
  • Communications interface 124 allows software and data to be transferred between computer system 100 and external devices.
  • Examples of communications interface 824 may include a modem, a network interface (such as an Ethernet card), a
  • communications interface 124 Software and data transferred via communications interface 124 are in the form of signals 828 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 124.
  • Signals 128 are provided to communications interface 124 via a communications path (i.e., channel) 126.
  • Channel 126 (or any other communication means or channel disclosed herein) carries signals 128 and may be implemented using wire or cable, fiber optics, blue tooth, a phone line, a cellular phone link, an RF link, an infrared link, wireless link or connection and other communications channels.
  • computer program medium and “computer usable medium” are used to generally refer to media or medium such as removable storage drive 1 14, a hard disk installed in hard disk drive 1 12, and signals 128.
  • These computer program products are means for providing software to computer system 100.
  • the computer program product may comprise a computer useable medium having computer program logic thereon.
  • the invention includes such computer program products.
  • the "computer program product” and “computer useable medium” may be any computer readable medium having computer logic thereon.
  • Computer programs may be stored in main memory 108 and/or secondary memory 1 10. Computer programs may also be received via communications interface 124. Such computer programs, when executed, enable computer system 100 to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable processor 104 to perform the functions of the present invention.
  • the software may be stored in a computer program product and loaded into computer system 100 using removable storage drive 1 14, hard drive 1 12 or communications interface 124.
  • the control logic when executed by the processor 104, causes the processor 104 to perform the functions of the invention as described herein.

Abstract

L'invention concerne un procédé de détermination de la santé et de la mortalité comprenant l'obtention d'une série de temps d'activation ventriculaire (RR) d'un sujet pendant des intervalles temporels multiples. Le procédé comprend également le calcul d'une entropie cardiaque dans la série de temps RR sur les intervalles temporels au moyen d'un coefficient d'entropie d'échantillon (COSEn). De plus, le procédé comprend la comparaison de l'entropie cardiaque entre les intervalles pour déterminer la santé et la mortalité. Les changements absolus et relatifs de l'entropie pendant une période de suivi d'un patient fournissent des informations dynamiques concernant la santé et le risque de mortalité. La détermination de la santé et de la mortalité peut alors être utilisée pour générer un plan de traitement pour le sujet.
PCT/US2013/040677 2009-10-06 2013-05-11 Changement d'entropie d'activation ventriculaire (rr) comme facteur de prédiction d'une mort subite cardiaque chez des patients en thérapie de resynchronisation cardiaque WO2013170232A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP13788256.9A EP2846685A4 (fr) 2012-05-11 2013-05-11 Changement d'entropie d'activation ventriculaire (rr) comme facteur de prédiction d'une mort subite cardiaque chez des patients en thérapie de resynchronisation cardiaque
US14/400,408 US9839364B2 (en) 2009-10-06 2013-05-11 Ventricular activation (RR) entropy change as a predictor of sudden cardiac death in cardiac resynchronization therapy patients

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US201261645830P 2012-05-11 2012-05-11
US61/645,830 2012-05-11

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