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 tachycardia (VT), and ventricular fibrillation (VF) in cases where these arrhythmias are resistant to surgical and drug-based treatments by direct sensing and electrical stimulation of the heart muscle. Since the ICD is implanted, power consumption, reliability, and size are severe design constraints. This paper targets the problems associated with increasing the signal recording capabilities of an ICD. A data-compression algorithm is described which has been optimized for low power consumption and high reliability implementation. Reliance on a patient´s morphology or that of a population of patients is avoided by adapting to the intracardiac electrogram (ICEG) amplitude and phase variations and by using adaptive scalar quantization. The algorithm is compared to alternative compression algorithms which are also patient independent using a subset of VT arrhythmias from a data base of 146 patients. At low distortion the algorithm is closest to the Shannon lower bound 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 Karhunen-Loeve Transform approaches are superior but at the cost of considerable additional computational complexity.