Use of approximate entropy measurements to classify ventricular tachycardia and fibrillation

Abstract

Implantable cardiac devices which treat arrhythmias require automated detection to decide when to deliver therapy and, in some devices like the implantable cardioverter-defibrillator (ICD), to determine which therapy to deliver. This type of detection often requires the separation of fibrillatory chaotic rhythms from coherent tachycardias. In the ICD, multiple rate zones can be programmed for detection of ventricular tachycardia (VT) and ventricular fibrillation (VF) and for delivery of therapy: antitachycardia pacing and cardioversion for VT, and defibrillation for VF. Previous research determined that current technology is unable to uniquely classify VF. Analysis of typical settings of the ICD revealed that 40% to 80% of VTs were misdassified as VF (depending on device/setting). Improved detection of VF must be sought in order to capitalize on the cost effectiveness of low-energy VT therapies, potentially saving up to 20% of the life of the battery. In this study, a statistical measure of variability, called approximate entropy (ApEn), has been applied to separate fibrillatory and nonfibrillatory rhythms. Although standard deviation is often used as a measure of variability, it fails to capture the level of regularity or complexity. ApEn improves upon standard deviation by quantifying differences between random and regular signals. ApEn was tested on a small patient set containing VF, VT, and sinus rhythm (SR). Intracardiac recordings (bipolar, ventricular) were selected from the Ann Arbor Electrogram Library (Ann Arbor, MI) where filtering (1–500 Hz), amplification (∼1000), and digitization (1000 Hz) are tightly controlled. Results demonstrated that ApEn has the ability to quantify subtle differences between VF and other rhythms.