Key result
A proposed algorithm using Phase Rectified Signal Average (PRSA) and Support Vector Machine classification achieved good performance for detecting atrial fibrillation and flutter from ECGs.
A novel PRSA-based algorithm without conventional filtering can effectively detect AF and AFL from ECG signals.
Supports PRSA-SVM for filtering-free AF detection; leaves open prospective validation before clinical use.
Non-invasive detection of Atrial Fibrillation (AF) and Atrial Flutter (AFL) from ECG at the time of their onset can prevent forthcoming dangers for patients. In most of the previous detection algorithms, one of the steps includes filtering of the signal to remove noise and artefacts present in the signal. In this paper, a method of AF and AFL detection is proposed from ECG without the conventional filtering stage. Here Phase Rectified Signal Average (PRSA) technique is used with a novel optimized windowing method to achieve an averaged signal without quasi-periodicities. Both time domain and statistical features are extracted from a novel SQ concatenated section of the signal for non-linear Support Vector Machine (SVM) based classification. The performance of the proposed algorithm is tested with the MIT-BIH Arrhythmia database and good performance parameters are obtained, as indicated in the result section.
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Maji et al. (2015) studied Atrial Fibrillation and Atrial Flutter. Phase Rectified Signal Average (PRSA) technique with SVM classification was evaluated on Detection of AF and AFL. A proposed algorithm using Phase Rectified Signal Average (PRSA) and Support Vector Machine classification achieved good performance for detecting atrial fibrillation and flutter from ECGs.
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