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Separation of Superimposed Electrocardiographic and Electromyographic Signals

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EMBEC & NBC 2017 (EMBEC 2017, NBC 2017)

Abstract

Electrocardiography (ECG) and surface electromyography (SEMG) are two non-invasive tests to evaluate cardiac and muscular functionality, respectively. They are both acquired by placing electrodes on the body surface so they become one the interference of the other. Typically, linear filters are used for ECG and SEMG separation: high-pass filters with cutoff at 20 Hz to attenuate ECG interference in SEMG, and low-pass filters with cut-off at 50 Hz to attenuate SEMG interference in ECG. In spite of that, linear filtering is not adequate due to the presence of a 20-50 Hz frequency-band in which the two signal spectra overlap. The aim of the present study was to evaluate the ability of the Segmented-Beat Modulation Method (SBMM) for ECG and SEMG separation and by accurately maintaining signals characteristics. SBMM is a template-based technique for ECG denoising: under the hypothesis of ECG and SEMG linearly superimposed, it first provides an ECG estimation, and then an SEMG estimation by subtraction. In order to test the method under several conditions, SBMM was applied to simulated as well as clinical recordings with superimposed ECG and SEMG. SBMM was able to accurately estimate both ECG and SEMG in all cases. Indeed, ECG and SEMG were estimated by maintain their features such as amplitude (estimation errors <6%), heart rate and heart-rate variability. Moreover, estimated ECG was always characterized by a spectrum mostly (76.4-100.0%) included in the 0-50 Hz frequency-band, whereas estimated SEMG was always characterized by a spectrum mostly (80.9-95.6%) included in the 20-450 Hz frequency-band. Such results confirm the existence of a 20-50 Hz frequency-band in which ECG and SEMG spectral components are overlapped. Thus, SBMM is a robust filtering procedure to separate superimposed ECG and SEMG.

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Correspondence to Laura Burattini .

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Sbrollini, A. et al. (2018). Separation of Superimposed Electrocardiographic and Electromyographic Signals. In: Eskola, H., Väisänen, O., Viik, J., Hyttinen, J. (eds) EMBEC & NBC 2017. EMBEC NBC 2017 2017. IFMBE Proceedings, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-10-5122-7_130

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  • DOI: https://doi.org/10.1007/978-981-10-5122-7_130

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