  Shockable rhythms, namely ventricular fibrillation and ventricular
tachycardia, are the main cause of sudden cardiac arrests, which can be
detected quickly by the automated external defibrillator (AED) devices. In this
paper, a simple but effective algorithm is proposed as the shock advice
algorithm 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 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 simpler while
remaining relatively high detection performance compared to previous
publications.
