Sparse Representation Based Anomalies Detection in Electrocardiography Signals

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In this article, we present the use of sparse representation of signal and dictionary learning method for solving the problem of anomaly detection. The analyzed signal was presented as a set of correct ECG structures and outliers (characterizing different types of disorders). In the course of learning we used the modified Method of Optimal Directions (MOD) to find a dictionary that would reflect correct structures of an ECG signal. The dictionary found this way became a basis for sparse representation of the analyzed ECG signal. In the process of anomaly detection based on decomposition of the analyzed signal onto correct values and outliers, there was used a modified Alternating Minimization Algorithm (AMA). Performance of the proposed method was tested using a widely available database of ECG signals - MIT–BIH Arrhythmia Database. The obtained experimental results confirmed the effectiveness of the method of anomaly detection in the analysed ECG signals.