{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:27:59Z","timestamp":1785421679174,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Guided Local Science and Technology Development Fund Project for Science and Technology Innovation Base Construction","award":["ZY24212046"],"award-info":[{"award-number":["ZY24212046"]}]},{"DOI":"10.13039\/501100011785","name":"Department of Science and Technology of Guangxi Zhuang Autonomous Region","doi-asserted-by":"crossref","award":["2024KY0227"],"award-info":[{"award-number":["2024KY0227"]}],"id":[{"id":"10.13039\/501100011785","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005372","name":"Department of Education of Guangxi Zhuang Autonomous Region","doi-asserted-by":"publisher","award":["2024YCXS124"],"award-info":[{"award-number":["2024YCXS124"]}],"id":[{"id":"10.13039\/501100005372","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2024YCXS124"],"award-info":[{"award-number":["2024YCXS124"]}],"id":[{"id":"https:\/\/ror.org\/00kx48s25","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005372","name":"Guilin University of Electronic Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005372","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/05arjae42","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly.<\/jats:p>","DOI":"10.3390\/bios16050235","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T17:04:59Z","timestamp":1776963899000},"page":"235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Class Arrhythmia Detection from PPG Signals Based on VGG-BiLSTM Hybrid Deep Learning Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-9259","authenticated-orcid":false,"given":"Shiyong","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaying","family":"Mo","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiating","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengguang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-1509","authenticated-orcid":false,"given":"Qunfeng","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhencheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101622","DOI":"10.1016\/j.cpcardiol.2023.101622","article-title":"Global cardiovascular diseases death rate prediction","volume":"48","author":"Gaidai","year":"2023","journal-title":"Curr. 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