A novel signal-adaptive multi-feature extraction algorithm for arrhythmia detection

Abstract

Abstract Background The significant features like an amplitude and intervals of electrocardiograph or P-QRS-T wave represent the functionality of the heart. Accurate extraction of these features helps in capturing characteristics of the signal helpful for the detection of cardiac abnormalities. In this paper, a novel signal folding-based algorithm is proposed to obtain detailed information about the complex morphology of signal. It explores the denoising and feature extraction of the specific ECG signals. Results The experimental study conducted using MIT-BIH Arrhythmia database ECG records with known conditions of left bundle branch block, right bundle branch block, Wolff-Parkinson-White syndrome beats has been considered. Heart rate values for selected ECG records from MIT-BIH dataset and synthetic signals from ECG simulator yielded the same values and thus validate our approach. Conclusion The proposed algorithm determines the heart rate, percentage leakage around the peak and is capable of folding a signal very efficiently based on detected R peaks and period-dependent gate(window).

Keywords

• ECG
• Arrhythmia
• Left bundle branch block (LBBB)
• Right bundle branch block (RBBB)
• Wolff-Parkinson-White syndrome (WPW)
• Signal folding