{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:13:10Z","timestamp":1791393190360,"version":"4.3.3"},"reference-count":40,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2022,4,5]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been reported. Our study investigated the feasibility of using photoplethysmography signals and a deep convolutional neural network to classify multiclass arrhythmia types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods and Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">ECG and photoplethysmography signals were collected simultaneously from a group of patients who underwent radiofrequency ablation for arrhythmias. A deep convolutional neural network was developed to classify multiple rhythms based on 10\u2010second photoplethysmography waveforms. Classification performance was evaluated by calculating the area under the microaverage receiver operating characteristic curve, overall accuracy, sensitivity, specificity, and positive and negative predictive values against annotations on the rhythm of arrhythmias provided by 2 cardiologists consulting the ECG results. A total of 228 patients were included; 118\u00a0217 pairs of 10\u2010second photoplethysmography and ECG waveforms were used. When validated against an independent test data set (23\u00a0384 photoplethysmography waveforms from 45 patients), the DCNN achieved an overall accuracy of 85.0% for 6 rhythm types (sinus rhythm, premature ventricular contraction, premature atrial contraction, ventricular tachycardia, supraventricular tachycardia, and atrial fibrillation); the microaverage area under the microaverage receiver operating characteristic curve was 0.978; the average sensitivity, specificity, and positive and negative predictive values were 75.8%, 96.9%, 75.2%, and 97.0%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">This study demonstrated the feasibility of classifying multiclass arrhythmias from photoplethysmography signals using deep learning techniques. The approach is attractive for population\u2010based screening and may hold promise for the long\u2010term surveillance and management of arrhythmia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Registration<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      URL:\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.chictr.org.cn\">www.chictr.org.cn<\/jats:ext-link>\n                      . Identifier: ChiCTR2000031170.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.121.023555","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T18:43:02Z","timestamp":1648147382000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":59,"title":["Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network"],"prefix":"10.1161","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2878-5367","authenticated-orcid":false,"given":"Zengding","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China"},{"name":"University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Cardiology Laboratory of Heart Center Zhujiang HospitalSouthern Medical University Guangzhou China"},{"name":"Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China"},{"name":"Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6883-1770","authenticated-orcid":false,"given":"Min","family":"Tang","sequence":"additional","affiliation":[{"name":"Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3054-807X","authenticated-orcid":false,"given":"Fen","family":"Miao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.0000140263.20897.42"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.28.10.973"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.0000072767.89944.6E"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2007.06.023"},{"key":"e_1_3_2_6_2","first-page":"193","article-title":"The pathology and treatment of cardiac arrhythmias: focus on atrial fibrillation","volume":"7","author":"Schmidt C","year":"2011","unstructured":"Schmidt C, Kisselbach J, Schweizer PA, Katus HA, Thomas D. 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