Surgery – Diagnostic testing – Cardiovascular
Reexamination Certificate
2000-09-25
2002-12-03
Evanisko, George R. (Department: 3762)
Surgery
Diagnostic testing
Cardiovascular
C607S004000
Reexamination Certificate
active
06490478
ABSTRACT:
BACKGROUND OF THE INVENTION
A. Field of The Invention
The present invention relates generally to a new cardiac tachyarrhythmia detection system and method, which is suitable for use in cardiac therapy and monitoring equipment and other similar devices which incorporate techniques of ECG processing and analysis. These devices may include out-of-hospital or in-hospital, external automatic defibrillator (AED), implantable cardioverter defibrillator (ICD), pacemakers, and other similar systems. More particularly, the present invention relates to a system and method capable of discriminating ventricular fibrillation (VF), ventricular tachycardia (VT), non-shockable tachyarrhythmia, and high-frequency noise, using an ECG complexity measure CM.
B. Description of The Prior Art
Despite major advances in the diagnosis and treatment of heart disease over the past decades, a substantial number (350,000 in the USA) of patients each year suffer sudden cardiac arrest (SCA) due to, for example, ventricular tachycardia (VT) or ventricular fibrillation (VF). However, the national survival rate of SCA is merely about 5%. The standard therapy for SCA is early cardioversion/defibrillation either by implantable cardioverter defibrillators (ICD) or by automatic external defibrillator (AED). An important parameter that affects the reliability and accuracy of these therapies is the algorithm or technique used to detect shockable VT and VF and while avoiding unnecessary shocks possibly caused by non-shockable tachyarrhythmias (e.g. supraventricular tachycardia (SVT), atrial fibrillation (AF), etc.) and some high-frequency noise commonly encountered under practical situations.
Since electrical shocks always have adverse affects on the myocardium, another primary goal of all cardiac therapies is to minimize the number and energy level of electrical signals delivered to the patient. To this end, VT, which requires much lower energy levels for effective therapy, must be effectively differentiated from VF. Moreover, the safety of a device, as well as its ease of use, extent of automatic operation, and widespread acceptance also depend on the performance of the arrhythmia detection system and method.
All devices and systems monitoring the cardiac state of a patient and/or generating antitachyarrhythmia therapy rely on the analysis of the electrocardiogram (ECG) from the patient. The analyses proposed and used so far were based on manipulation of information in the time-domain, frequency-domain, time-frequency domain, and bispectral domain, and even nonlinear dynamics domain. However, all these manipulations have fundamental limitations associated with the linear nature, computational complexity, or difficulty in real-time implementation as well as low sensitivity and specificity. For this reason, currently, the percentage of patients with ICDs who are paced or shocked unnecessarily exceeds 40%. Similarly, AEDs are only 90% effective or sufficiently sensitive to detecting ventricular tachyarrhythmia and 90-95% accurate in detecting and correctly classifying other heart rhythms. Moreover, discrimination of VT from VF is still a difficult object to achieve using existing algorithms for ICD and AED. Therefore, a need still exists for a simple and effective arrhythmia detection system and method using sophisticated signal processing techniques.
It has been found that the electrical activity of the heart is best described as by a non-linear dynamical system. The theory used to describe such systems is known as non-linear dynamics theory, which can be used therefore to analyze the dynamic mechanisms underlying the cardiac activities. Dynamical systems such as the heart can exhibit both periodic and chaotic behaviors depending on certain system parameters. For instance, VF is a highly complex, seemingly random phenomenon, and can be described as chaotic cardiac behavior. Therefore, a diagnostic system with the ability to quantify abnormalities of a non-linear dynamic cardiac system would be expected to have an enhanced performance. In fact, methods have been described which were derived from nonlinear dynamics in ECG signal processing and arrhythmia prediction and detection. For example, Poincare map or return map of the ECG amplitude for cardiac fibrillation detection was disclosed in U.S. Pat. No. 5,439,004, issued to Duong-Van. U.S. Pat. No. 5,643,325, issued to Karagueuzian et al., disclosed the degree of deterministic chaos in phase-plane plot may indicate a propensity for fibrillation including both the risk of fibrillation and the actual onset of fibrillation. A method for detecting a heart disorder using correlation dimension (by Grassberger-Procaccia algorithm) was also disclosed in U.S. Pat. No. 5,643,325, incorporated herein by reference. A slope filtered point-wise correlation dimension algorithm is utilized to predict imminent fibrillation, as disclosed in U. S. Pat. No. 5,425,749, issued to Adams, and “Slope Filtered Pointwise Correlation Dimension Algorithm and Evaluation with Prefibrillation Heart Rate Data,” were disclosed in the
Journal of Electrocardiology
, Vol. 24, Supplement, pp. 97-101, authored by Kroll and Fulton. These non-linear dynamics derived methods are based on the phase space reconstruction, and the computational demand and complexity are considerable for current ICD and AED, therefore, they are still difficult to apply in the real world.
The cardiac electrical signal is the complex resultant of a plurality of spatial and temporal inputs and many non-linear dynamic features or characteristics should be expected in this signal, such as different spatio-temporal patterns manifested in the ECG. One such dynamic feature is ‘complexity.’ Different non-linear dynamic cardiac behavior is associated with different degrees of complexity. Therefore, the measure characterizing complexity can be used as an effective tool for detecting VT and VF. Correlation dimension and approximate entropy have been proposed as means of characterizing complexity, however, these approaches requires highly accurate calculations involving long data segments and are very time-consuming (See Caswell Schuckers S. A., “Approximate Entropy as a Measure of Morphologic Variability for Ventricular Tachycardia and Fibrillation”,
Computers in Cardiology
, 1998, 25:265-268). Hence, these approaches cannot be extended to real-time application in ICD and AED. Another method of complexity analysis was proposed by Lempel and Ziv in “On the Complexity of Finite Sequences”,
IEEE Trans. Information Theory
, 1976, IT-22: 75-81. Zhang et al. have disclosed some results of this kind of complexity analysis for normal sinus rhythm, VT, and VF, in “Detecting Ventricular Tachycardia and Fibrillation by Complexity Measure”,
IEEE Transactions on Biomedical Engineering
, 1999, 46: 548-555. All the above-mentioned references are incorporated herein by reference. However, none of these references mention the way to perform real-time complexity analysis in ICDs and AEDs. Moreover, none of these references discuss a method that can be used to avoid unnecessary therapy caused by SVT or high-frequency noise.
In view of the clinical importance of ventricular conditions, more emphasis should be put on the analysis and feature extraction of the ventricular electrical activity, manifested as QRS complex on the ECG. By an optimized threshold method, the “complexity” of ventricular activity patterns can be analyzed quantitatively by complexity analysis after transforming it into a character sequence. The cardiac tachyarrhythmia detection system and method of present invention as disclosed herein is simple, computationally efficient, effective, robust and reliable, and well suited for real-time implementation and, at the same time, it has immunity ability to noise and artifacts. Therefore, it offers all the desirable features for the practical application in AED and ICD.
OBJECTIVES AND SUMMARY OF THE INVENTION
The present invention fulfills the need in AEDs and ICDs by providing a cardiac arrhythmia detection system and method, which provides a clea
Lin Dong-ping
Zhang Xu-Sheng
Cardiac Science Inc.
Evanisko George R.
Gottlieb Rackman & Reisman P.C.
LandOfFree
System and method for complexity analysis-based cardiac... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with System and method for complexity analysis-based cardiac..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and System and method for complexity analysis-based cardiac... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2949084