A low-complexity intracardiac electrogram compression algorithm

Implantable cardioverter defibrillators (ICD's) detect, diagnose and treat the potentially fatal heart arrhythmias known as bradycardia, ventricular t achycardia (VT), and ventricular fibrillation (VF) in cases where these arr hythmias are resistant to surgical and drug-based treatments by direct sens ing and electrical stimulation of the heart muscle. Since the ICD is implan ted, power consumption, reliability, and size are severe design constraints . This paper targets the problems associated with increasing the signal rec ording capabilities of an ICD. A data-compression algorithm is described wh ich has; been optimized for low power consumption and high reliability impl ementation. Reliance on a patients morphology or that of a population of pa tients is avoided by adapting to the intracardiac electrogram (ICEG) amplit ude and phase variations and by using adaptive scalar quantization, The alg orithm is compared to alternative compression algorithms which are also pat ient independent using a subset of VT arrhythmias from a data base of 146 p atients. At low distortion the algorithm is closest to the Shannon lower ho und achieving an average of 3.5 b/sample at 5% root mean square distortion for a 250-Hz sample rate. At higher distortion vector quantization and Karh unen-Loeve Transform approaches are superior but at the cost of considerabl e additional computational complexity.