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

TL;DR

This paper introduces a simple, effective shock advice algorithm for AEDs using K-nearest neighbors and optimized ECG features, achieving high detection accuracy for sudden cardiac arrest rhythms.

Contribution

It presents a novel AED shock advice algorithm combining KNN with an optimal feature set derived from modified variational mode decomposition, improving simplicity and performance.

Findings

MVMD significantly enhances SCA detection.

Proposed algorithm achieves high detection accuracy.

Feature selection improves classifier performance.

Abstract

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.