{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T10:12:46Z","timestamp":1789985566913,"version":"4.0.1"},"reference-count":25,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation (\u0394KR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes.<\/jats:p>","DOI":"10.3390\/bios16020082","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T10:56:34Z","timestamp":1769684194000},"page":"82","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Denoising Non-Invasive Electroespinography Signals by Different Cardiac Artifact Removal Algorithms"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8855-1151","authenticated-orcid":false,"given":"Desir\u00e9e I.","family":"Gracia","sequence":"first","affiliation":[{"name":"Brain-Machine Interface Systems Lab, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"},{"name":"Engineering Research Institute of Elche\u2014I3E, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8057-5952","authenticated-orcid":false,"given":"Eduardo","family":"I\u00e1\u00f1ez","sequence":"additional","affiliation":[{"name":"Brain-Machine Interface Systems Lab, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"},{"name":"Engineering Research Institute of Elche\u2014I3E, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4269-1554","authenticated-orcid":false,"given":"Mario","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Brain-Machine Interface Systems Lab, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"},{"name":"Engineering Research Institute of Elche\u2014I3E, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5548-9657","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Azor\u00edn","sequence":"additional","affiliation":[{"name":"Brain-Machine Interface Systems Lab, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"},{"name":"Engineering Research Institute of Elche\u2014I3E, Miguel Hern\u00e1ndez University of Elche, 03202 Elche, Spain"},{"name":"Valencian Graduated School and Research Network of Artificial Intelligence\u2014ValGRAI, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cadotte, D.W., Bosma, R.L., Mikulis, D.J., Nugaeva, N., Smith, K., Pokrupa, R., Islam, O., Stroman, P.W., and Fehlings, M.G. 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