An Efficient Shock Advice Algorithm based on K-Nearest Neighbors for Automated External Defibrillators

Shockable rhythms, namely ventricular fibrillation, and ventricular tachycardia, are the main cause of sudden cardiac arrests (SCA), which can be detected quickly by the automated external defibrillator (AED) devices. In this paper, a simple and efficient shock advice algorithm is developed, and it can be practically applied in AED. The proposed algorithm consists of K-nearest neighbor classifier and an optimal set of 36 features, which are extracted from original ECG and shockable, non-shockable signals using modified variational mode decomposition (MVMD) technique. Cross validation procedure and sequential forward feature selection are carefully applied to select an optimal set from entire feature space. The performance results show that the MVMD is the key element for SCA detection performance, and the proposed algorithm is computationally efficient while featuring greater detection performance compared to previous publications.