Machine learning for shock decision in implanted defibrillators

The discrimination of Ventricular Tachycardia (VT) from Supra-Ventricular Tachycardia (SVT) remains a major challenge for appropriate therapy delivery in Implantable Cardioverter Defibrillators (ICDs). Unlike SVT, VT is a life-threatening arrhythmia that may lead to sudden death unless an appropriate shock is delivered. The discrimination in ICDs is performed from endocardial measurements of the electrical activity of the heart (EGM). Historically, only time intervals extracted from EGMs were used for the diagnosis. In the last decade, an additional analysis of features extracted directly from the shape of a single EGM channel led to improved performances, especially in order to avoid inappropriate shocks, which are very painful and stressful for patients. A recent study shows that inappropriate shocks occurred in 11.5% of the prophylactic ICD patients and accounted for 31.2% of the total shock episodes [1].