An Improved SSA-Based Technique for EMG Removal from ECG - 28/01/19
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Abstract |
Background |
The 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.
Methods |
In 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.
Results |
Performance of the proposed method is investigated using several experiments. Two sub-sets from Physionet MIT-BIH arrhythmia database are used for this purpose.
Conclusion |
The experimental results demonstrate effectiveness of the proposed method in comparison with other SSA-based techniques.
Le texte complet de cet article est disponible en PDF.Graphical abstract |
Highlights |
• | EMG removal from contaminated ECG signals is addressed. |
• | Singular spectrum analysis (SSA) is employed as a powerful tool for this purpose. |
• | A new criterion is proposed in grouping step of SSA. |
• | The proposed method outperforms existing techniques. |
Keywords : De-noising, Electrocardiogram, Electromyogram, Singular spectrum analysis
Plan
Vol 40 - N° 1
P. 62-68 - février 2019 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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