A proposed arrhythmia classification module using time and frequency domain features and a classification tree achieved an F-score of 0.82 for normal sinus rhythm on the training set.
May support ECG arrhythmia classifier development; leaves open prospective validation before clinical use.
Atrial fibrillation (AF) is associated with significant risk of heart failure and consequent death. Its episodic appearance, the wide variety of arrhythmias exhibiting irregular AF-like RR intervals and noises accompanying the ECG acquisition, impede the reliable AF detection. Therefore, the Computing in Cardiology Challenge 2017 organizers encourage the development of methods for classification of short, single-lead ECG as AF, normal sinus rhythm (NSR), other rhythm (OR), or noisy signal. The arrhythmia classification module presented in this paper involves procedures for QRS detection and classification, P-waves detection, feature calculation in the time and frequency domains. The applied decision rule is a classification tree. The scores over the training (test subset) [whole test] datasets are: F NSR =0.82(0.81);
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Jekova et al. (2017) studied this question.
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