Abstract
BackgroundThe electrocardiogram (ECG) signals provide important information about the heart electrical activities in medical and diagnostic applications. This signal may be contaminated by different types of noises. One of the noise types which has a considerable overlap with the ECG signals in frequency domain is electromyogram (EMG). Among the exciting approaches for de-noising the ECG signals, those based on singular spectrum analysis (SSA) are popular. MethodsIn this paper, we propose a method based on SSA to separate the ECG signals from EMG noises. In general, SSA contains four steps as: embedding, singular value decomposition, grouping, and diagonal averaging. Among these steps, grouping step contains parameter (indices) which can be adjusted to achieve the desirable results. Indeed, grouping is one of the important steps of SSA as the ECG and EMG signals are separated in this step. Hence, in the proposed method, a new criterion is presented to select the indices in grouping step to separate the ECG from EMG signal with higher accuracy. ResultsPerformance of the proposed method is investigated using several experiments. Two sub-sets from Physionet MIT-BIH arrhythmia database are used for this purpose. ConclusionThe experimental results demonstrate effectiveness of the proposed method in comparison with other SSA-based techniques.
Published Version
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