Frontiers in Signal Processing | Biomedical Signal Processing section | New and Recent Articles https://www.frontiersin.org/journals/signal-processing/sections/biomedical-signal-processing RSS Feed for Biomedical Signal Processing section in the Frontiers in Signal Processing journal | New and Recent Articles en-us Frontiers Feed Generator,version:1 2026-05-04T04:23:19.378+00:00 60 https://www.frontiersin.org/articles/10.3389/frsip.2026.1728615 https://www.frontiersin.org/articles/10.3389/frsip.2026.1728615 <![CDATA[Temporal convolutional network architectures: a novel simultaneous spatio-temporal model for comparative analysis]]> 2026-04-17T00:00:00Z Original Research Milad JabbariEisa AghchehliChenfei MaKianoush Nazarpour https://www.frontiersin.org/articles/10.3389/frsip.2026.1776807 https://www.frontiersin.org/articles/10.3389/frsip.2026.1776807 <![CDATA[Self-face viewing attenuates cardiac modulation of corticospinal excitability]]> 2026-04-07T00:00:00Z Original Research Milana MakarovaNikita FedosovIrina MikhailovaMaria NikolaevaAlexei OssadtchiAlexey TumyalisMaria Volodina https://www.frontiersin.org/articles/10.3389/frsip.2026.1715921 https://www.frontiersin.org/articles/10.3389/frsip.2026.1715921 <![CDATA[The role of signal preprocessing on the discriminability of canonical time-series characteristics and classification among individuals with and without Parkinson’s disease during serious game interaction]]> 2026-03-24T00:00:00Z Original Research Maria Fernanda Soares de AlmeidaAriana Moura CabralLeandro Rodrigues da Silva SouzaMila Figueira NozellaCamille Marques AlvesPedro Henrique Bernardes Caetano MilkenMaria Olivia Domingos Rezio RamosLuanne Cardoso MendesAdriano de Oliveira Andrade https://www.frontiersin.org/articles/10.3389/frsip.2026.1691777 https://www.frontiersin.org/articles/10.3389/frsip.2026.1691777 <![CDATA[EEG-based cognitive load estimation during the use of a virtual wheelchair simulator]]> 2026-03-16T00:00:00Z Original Research Débora Pereira SalgadoFelipe Roque MartinsAngela Abreu Rosa de SáRonan FlynnnNiall MurrayEduardo Lázaro Martins Naves https://www.frontiersin.org/articles/10.3389/frsip.2026.1745291 https://www.frontiersin.org/articles/10.3389/frsip.2026.1745291 <![CDATA[Equation-level parameterized fusion reformulation for multimodal epileptic seizure detection using interaction control and data-quality screening]]> 2026-03-06T00:00:00Z Original Research Abdul-Mumin KhalidMusah SulemanaIddrisu Wahab Abdul https://www.frontiersin.org/articles/10.3389/frsip.2026.1680796 https://www.frontiersin.org/articles/10.3389/frsip.2026.1680796 <![CDATA[Alzheimer’s detection using discrete wavelet transform based image fusion and vision information transformer]]> 2026-03-04T00:00:00Z Original Research Amar A. DumKshama V. KulhalliPriyanka Singh https://www.frontiersin.org/articles/10.3389/frsip.2025.1715540 https://www.frontiersin.org/articles/10.3389/frsip.2025.1715540 <![CDATA[Singular spectrum analysis of near-infrared spectroscopy signal classification for mental arithmetic and rest state]]> 2026-02-11T00:00:00Z Original Research Kanwardeep Singh GahlotAnukul PandeySachin TaranRahul Thakur https://www.frontiersin.org/articles/10.3389/frsip.2026.1724468 https://www.frontiersin.org/articles/10.3389/frsip.2026.1724468 <![CDATA[Research on heart rate estimation algorithm based on dynamic PPG]]> 2026-02-02T00:00:00Z Original Research Jiawei GuoShiyuan ChenTing LanRuochen LiLichao WangYunchong WuJun ZhongWei Zhu https://www.frontiersin.org/articles/10.3389/frsip.2025.1700044 https://www.frontiersin.org/articles/10.3389/frsip.2025.1700044 <![CDATA[Comparing compressive sensing and downsampling for COVID-19 diagnosis from cough and speech audio signals]]> 2026-01-22T00:00:00Z Original Research Leticia SilvaAlan FlorianoCarlos ValadãoEliete CaldeiraSridhar KrishnanTeodiano Bastos Filho https://www.frontiersin.org/articles/10.3389/frsip.2025.1707422 https://www.frontiersin.org/articles/10.3389/frsip.2025.1707422 <![CDATA[Unraveling cardiac arrhythmia frequency: comparative analysis using time and frequency domain algorithms]]> 2026-01-12T00:00:00Z Original Research Laura Diaz-MaueAnnette WittLina Elshareif Holger Nobach https://www.frontiersin.org/articles/10.3389/frsip.2025.1679555 https://www.frontiersin.org/articles/10.3389/frsip.2025.1679555 <![CDATA[A computational approach for prediction of exons using static encoding methods, digital filter and windowing technique]]> 2025-11-27T00:00:00Z Original Research Shaik BenarjeeVaegae Naveen Kumar https://www.frontiersin.org/articles/10.3389/frsip.2025.1555876 https://www.frontiersin.org/articles/10.3389/frsip.2025.1555876 <![CDATA[Editorial: Smart biomedical signal analysis with machine intelligence]]> 2025-01-31T00:00:00Z Editorial Tilendra ChoudharyPulakesh UpadhyayaMohammad Zavid ParvezShaik Rafi Ahamed https://www.frontiersin.org/articles/10.3389/frsip.2024.1479244 https://www.frontiersin.org/articles/10.3389/frsip.2024.1479244 <![CDATA[Markerless vision-based knee osteoarthritis classification using machine learning and gait videos]]> 2024-11-21T00:00:00Z Original Research Slim Ben HassineAla BaltiSabeur AbidMohamed Moncef Ben KhelifaMounir Sayadi https://www.frontiersin.org/articles/10.3389/frsip.2024.1496320 https://www.frontiersin.org/articles/10.3389/frsip.2024.1496320 <![CDATA[Corrigendum: Editorial: Physiological signal processing for wellness]]> 2024-10-15T00:00:00Z Correction Rakesh Chandra JoshiNavchetan AwasthiPriyadarsan ParidaManob Jyoti Saikia https://www.frontiersin.org/articles/10.3389/frsip.2024.1384744 https://www.frontiersin.org/articles/10.3389/frsip.2024.1384744 <![CDATA[CovRoot: COVID-19 detection based on chest radiology imaging techniques using deep learning]]> 2024-09-20T00:00:00Z Original Research Ahashan Habib NiloyS. M. Farah Al FahimMohammad Zavid ParvezShammi Akhter ShibaFaizun Nahar FariaMd. Jamilur RahmanEmtiaz HussainTasmi Tamanna https://www.frontiersin.org/articles/10.3389/frsip.2024.1396077 https://www.frontiersin.org/articles/10.3389/frsip.2024.1396077 <![CDATA[Enhancement of single-lead dry-electrode ECG through wavelet denoising]]> 2024-05-28T00:00:00Z Original Research Abdelrahman AbdouSridhar Krishnan https://www.frontiersin.org/articles/10.3389/frsip.2024.1362754 https://www.frontiersin.org/articles/10.3389/frsip.2024.1362754 <![CDATA[Feasibility, functionality, and user experience with wearable technologies for acute exacerbation monitoring in patients with severe COPD]]> 2024-03-19T00:00:00Z Original Research Olivia C. IorioFelix-Antoine CoutuDany MalaebBryan A. Ross 40 with spirometry-confirmed COPD presenting with a current acute exacerbation (ECOPD) were recruited from a tertiary centre Day Hospital in this prespecified feasibility study. Heart rate, respiratory rate, oxygen saturation, skin temperature, and daily activity and overnight sleep quality parameters were collected remotely by a wearable biometric wristband and ring for 21 consecutive days. “Total ambulatory wear time” and “percent of useable data” for eligible vital sign parameters were calculated. Correlation and agreement between cardiorespiratory vital sign data were performed using Spearman’s correlation rho and the Bland-Altman test, respectively. User experience was measured with end-of-study System Usability Scale (SUS) questionnaires.Results: Nine participants (mean age 66.8 ± 8.4 years, 22% female, mean FEV1 1.4L (34.1% predicted), with “severe” (56%) or “very severe” (44%) COPD) experiencing a current ECOPD were included. Wear time was 94% (wristband) and 88.2% (ring) of the total ambulatory study period. Wristband-obtained data (every 1 min, artefact-free) revealed 99.2% and 98.6% of all heart rate and temperature data, respectively, was useable, whereas only 17.6% of all respiratory rate data was useable. Ring-obtained data (every 5 min, “average” and “good” quality) revealed 84.5% of all heart rate data was useable. Cross-sectional analyses with nurse-obtained vital signs revealed correlation coefficients of 0.56 (p = 0.11) and 0.81 (p = 0.0086) for wristband-obtained and ring-obtained heart rate, respectively, and only 0.15 (p = 0.74) for wristband-obtained respiratory rate, without evidence of systematic/proportional bias. Longitudinal heart rate and respiratory rate inter-device analyses demonstrated correlations of 0.86 (p < 0.001) and 0.65 (p < 0.001), respectively. Finally, end-of-study SUS scores were 86.4/100 (wristband) and 89.2/100 (ring).Conclusion: Older adults with severe/very severe COPD experiencing a current ECOPD were capable of autonomous physiological data collection/upload/transmission from their home environment over several weeks using sophisticated wearable biometric technology, with favourable user experiences. Cross-sectional and longitudinal comparative results call into question the paradigm of single sets of infrequent/interval vital sign checks as the current “gold-standard” in frontline clinical practice.]]> https://www.frontiersin.org/articles/10.3389/frsip.2024.1391335 https://www.frontiersin.org/articles/10.3389/frsip.2024.1391335 <![CDATA[Editorial: Physiological signal processing for wellness]]> 2024-03-05T00:00:00Z Editorial Rakesh Chandra JoshiNavchetan AwasthiPriyadarsan ParidaManob Jyoti Saikia https://www.frontiersin.org/articles/10.3389/frsip.2024.1322334 https://www.frontiersin.org/articles/10.3389/frsip.2024.1322334 <![CDATA[Comparative study of time-frequency transformation methods for ECG signal classification]]> 2024-01-29T00:00:00Z Original Research Min-Seo SongSeung-Bo Lee https://www.frontiersin.org/articles/10.3389/frsip.2024.1321861 https://www.frontiersin.org/articles/10.3389/frsip.2024.1321861 <![CDATA[The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning]]> 2024-01-25T00:00:00Z Original Research Sucheer Maddury