Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram
| العنوان: | Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram |
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| المؤلفون: | Christian Ciccarelli, Lin Xu, Massimo Mischi, Hongji Xu, Sebastiaan Overeem, Elisabetta Peri, Johannes P. van Dijk, Nele Vandenbussche, Xi Long |
| المساهمون: | Signal Processing Systems, Biomedical Diagnostics Lab, Center for Care & Cure Technology Eindhoven, Eindhoven MedTech Innovation Center, EAISI Health |
| المصدر: | Sensors, Vol 21, Iss 573, p 573 (2021) Sensors, 21(2):573. Multidisciplinary Digital Publishing Institute (MDPI) Sensors (Basel, Switzerland) Sensors Volume 21 Issue 2 |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Elektrokardiografie, Computer science, Physics::Medical Physics, 0206 medical engineering, 02 engineering and technology, Respiratory monitoring, Electromyography, Signal-To-Noise Ratio, lcsh:Chemical technology, Biochemistry, Article, Analytical Chemistry, quantitative assessment of performance, Electrocardiography, 03 medical and health sciences, 0302 clinical medicine, trunk electromyography, Singular value decomposition, medicine, Humans, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, Computer Science::Information Theory, electrocardiograph interference, Ground truth, medicine.diagnostic_test, business.industry, DDC 500 / Natural sciences & mathematics, singular value decomposition, Subtraction, Torso, Signal Processing, Computer-Assisted, Pattern recognition, Electrocardiographs, 020601 biomedical engineering, Trunk, Independent component analysis, Atomic and Molecular Physics, and Optics, Singulärwertzerlegung, ddc:500, Artificial intelligence, respiratory monitoring, business, Algorithms, 030217 neurology & neurosurgery |
| الوصف: | A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0&ndash 20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice. |
| وصف الملف: | application/pdf |
| تدمد: | 1424-8220 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f50170e6b8b38dc4000eef58062445e https://doi.org/10.3390/s21020573 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....1f50170e6b8b38dc4000eef58062445e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14248220 |
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