{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T20:47:18Z","timestamp":1777927638649,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006469","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["0071\/2023\/RIB3"],"award-info":[{"award-number":["0071\/2023\/RIB3"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["2019A1515110304"],"award-info":[{"award-number":["2019A1515110304"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["2020A1515110461"],"award-info":[{"award-number":["2020A1515110461"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["2022ZDZX3093"],"award-info":[{"award-number":["2022ZDZX3093"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["GD21CJY08"],"award-info":[{"award-number":["GD21CJY08"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["2022JDR0039"],"award-info":[{"award-number":["2022JDR0039"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong Province","award":["0071\/2023\/RIB3"],"award-info":[{"award-number":["0071\/2023\/RIB3"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong Province","award":["2019A1515110304"],"award-info":[{"award-number":["2019A1515110304"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong Province","award":["2020A1515110461"],"award-info":[{"award-number":["2020A1515110461"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong Province","award":["2022ZDZX3093"],"award-info":[{"award-number":["2022ZDZX3093"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong Province","award":["GD21CJY08"],"award-info":[{"award-number":["GD21CJY08"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong Province","award":["2022JDR0039"],"award-info":[{"award-number":["2022JDR0039"]}]},{"name":"Special Project in Key Fields of Universities in Guangdong Province","award":["0071\/2023\/RIB3"],"award-info":[{"award-number":["0071\/2023\/RIB3"]}]},{"name":"Special Project in Key Fields of Universities in Guangdong Province","award":["2019A1515110304"],"award-info":[{"award-number":["2019A1515110304"]}]},{"name":"Special Project in Key Fields of Universities in Guangdong Province","award":["2020A1515110461"],"award-info":[{"award-number":["2020A1515110461"]}]},{"name":"Special Project in Key Fields of Universities in Guangdong Province","award":["2022ZDZX3093"],"award-info":[{"award-number":["2022ZDZX3093"]}]},{"name":"Special Project in Key Fields of Universities in Guangdong Province","award":["GD21CJY08"],"award-info":[{"award-number":["GD21CJY08"]}]},{"name":"Special Project in Key Fields of Universities in Guangdong Province","award":["2022JDR0039"],"award-info":[{"award-number":["2022JDR0039"]}]},{"name":"2021 General Project of the 14th Five-Year Plan of Philosophy and Social Sciences of Guangdong Province of China","award":["0071\/2023\/RIB3"],"award-info":[{"award-number":["0071\/2023\/RIB3"]}]},{"name":"2021 General Project of the 14th Five-Year Plan of Philosophy and Social Sciences of Guangdong Province of China","award":["2019A1515110304"],"award-info":[{"award-number":["2019A1515110304"]}]},{"name":"2021 General Project of the 14th Five-Year Plan of Philosophy and Social Sciences of Guangdong Province of China","award":["2020A1515110461"],"award-info":[{"award-number":["2020A1515110461"]}]},{"name":"2021 General Project of the 14th Five-Year Plan of Philosophy and Social Sciences of Guangdong Province of China","award":["2022ZDZX3093"],"award-info":[{"award-number":["2022ZDZX3093"]}]},{"name":"2021 General Project of the 14th Five-Year Plan of Philosophy and Social Sciences of Guangdong Province of China","award":["GD21CJY08"],"award-info":[{"award-number":["GD21CJY08"]}]},{"name":"2021 General Project of the 14th Five-Year Plan of Philosophy and Social Sciences of Guangdong Province of China","award":["2022JDR0039"],"award-info":[{"award-number":["2022JDR0039"]}]},{"name":"Science and Technology Program of Sichuan Province","award":["0071\/2023\/RIB3"],"award-info":[{"award-number":["0071\/2023\/RIB3"]}]},{"name":"Science and Technology Program of Sichuan Province","award":["2019A1515110304"],"award-info":[{"award-number":["2019A1515110304"]}]},{"name":"Science and Technology Program of Sichuan Province","award":["2020A1515110461"],"award-info":[{"award-number":["2020A1515110461"]}]},{"name":"Science and Technology Program of Sichuan Province","award":["2022ZDZX3093"],"award-info":[{"award-number":["2022ZDZX3093"]}]},{"name":"Science and Technology Program of Sichuan Province","award":["GD21CJY08"],"award-info":[{"award-number":["GD21CJY08"]}]},{"name":"Science and Technology Program of Sichuan Province","award":["2022JDR0039"],"award-info":[{"award-number":["2022JDR0039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.<\/jats:p>","DOI":"10.3390\/s24165243","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T03:46:36Z","timestamp":1723607196000},"page":"5243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhifeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China"},{"name":"Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China"}]},{"given":"Jinwei","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China"},{"name":"Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6616-1892","authenticated-orcid":false,"given":"Yi","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Education, City University of Macau, Macau 999078, China"}]},{"given":"Huannan","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China"},{"name":"Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China"}]},{"given":"Peizhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Cosmetic Dermatology Department, Dermatology Hospital of Southern Medical University, Guangzhou 510091, China"}]},{"given":"Haichu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China"},{"name":"Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China"}]},{"given":"Zetao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China"},{"name":"Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1177\/17474930211065917","article-title":"World Stroke Organization (WSO): Global stroke fact sheet 2022","volume":"17","author":"Feigin","year":"2022","journal-title":"Int. J. Stroke"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, G., Li, L., and Tao, H. (2021). Bioinformatics identification of ferroptosis-related biomarkers and therapeutic compounds in ischemic stroke. Front. Neurol., 12.","DOI":"10.3389\/fneur.2021.745240"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2650","DOI":"10.1109\/JBHI.2024.3360952","article-title":"Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms","volume":"28","author":"Ding","year":"2024","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1161\/STROKEAHA.123.043672","article-title":"Differences in stroke recurrence risk between atrial fibrillation detected on ECG and 14-day cardiac monitoring","volume":"54","author":"Ayan","year":"2023","journal-title":"Stroke"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e20362","DOI":"10.1161\/circ.148.suppl_1.17046","article-title":"Accuracy of Apple Watch for Detection of Atrial Fibrillation: A Systematic Review and Meta-Analysis","volume":"148","author":"Yasmin","year":"2023","journal-title":"Circulation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1093\/europace\/euad011","article-title":"Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation","volume":"25","author":"Hermans","year":"2023","journal-title":"Europace"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4587","DOI":"10.1109\/JBHI.2022.3193117","article-title":"Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks","volume":"26","author":"Sun","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lueken, M., Gramlich, M., Leonhardt, S., Marx, N., and Zink, M.D. (2023). Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation. Sensors, 23.","DOI":"10.3390\/s23125618"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Velraeds, A., Strik, M., van der Zande, J., Fontagne, L., Haissaguerre, M., Ploux, S., Wang, Y., and Bordachar, P. (2023). Improving Automatic Smartwatch Electrocardiogram Diagnosis of Atrial Fibrillation by Identifying Regularity within Irregularity. Sensors, 23.","DOI":"10.3390\/s23229283"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1136\/heartjnl-2018-314186","article-title":"External continuous ECG versus loop recording for atrial fibrillation detection in patients who had a stroke","volume":"105","author":"Sejr","year":"2019","journal-title":"Heart"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108325","DOI":"10.1016\/j.engappai.2024.108325","article-title":"Exploring the power of photoplethysmogram matrix for atrial fibrillation detection with integrated explainability","volume":"133","author":"Camara","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tonko, J.B., and Wright, M.J. (2021). Review of the 2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation\u2014What Has Changed and How Does This Affect Daily Practice. J. Clin. Med., 10.","DOI":"10.3390\/jcm10173922"},{"key":"ref_13","first-page":"815","article-title":"Arrhythmia discrimination using a smart phone","volume":"19","author":"Chong","year":"2015","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"El-Hajj, C., and Kyriacou, P.A. (2020). A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure. Biomed. Signal Process. Control, 58.","DOI":"10.1016\/j.bspc.2020.101870"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jiang, F., Zhou, Y., Ling, T., Zhang, Y., and Zhu, Z. (2021). Recent research for unobtrusive atrial fibrillation detection methods based on cardiac dynamics signals: A survey. Sensors, 21.","DOI":"10.3390\/s21113814"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e10448","DOI":"10.7717\/peerj.10448","article-title":"Prediction of state anxiety by machine learning applied to photoplethysmography data","volume":"9","author":"Perpetuini","year":"2021","journal-title":"PeerJ"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ouzar, Y., Djeldjli, D., Bousefsaf, F., and Maaoui, C. (2023). X-iPPGNet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation. Comput. Biol. Med., 154.","DOI":"10.1016\/j.compbiomed.2023.106592"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aldughayfiq, B., Ashfaq, F., Jhanjhi, N.Z., and Humayun, M. (2023). A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG. Diagnostics, 13.","DOI":"10.3390\/diagnostics13142442"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, Y., Tang, Q., Zhan, W., Li, S., and Chen, Z. (2024). Res-BiANet: A Hybrid Deep Learning Model for Arrhythmia Detection Based on PPG Signal. Electronics, 13.","DOI":"10.3390\/electronics13030665"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41746-019-0207-9","article-title":"Photoplethysmography based atrial fibrillation detection: A review","volume":"3","author":"Pereira","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s12265-023-10462-x","article-title":"A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG)","volume":"17","author":"Alam","year":"2023","journal-title":"J. Cardiovasc. Transl. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.hrthm.2020.01.034","article-title":"A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation","volume":"17","author":"Chen","year":"2020","journal-title":"Heart Rhythm."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Saarinen, H.J., Joutsen, A., Korpi, K., Halkola, T., Nurmi, M., Hernesniemi, J., and Vehkaoja, A. (2023). Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1100127"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kotlyarov, S., and Lyubavin, A. (2024). Early Detection of Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients. Medicina, 60.","DOI":"10.3390\/medicina60030352"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1109\/JBHI.2022.3172705","article-title":"Motion-Robust Atrial Fibrillation Detection Based on Remote-Photoplethysmography","volume":"27","author":"Wu","year":"2023","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tutuko, B., Nurmaini, S., Tondas, A.E., Rachmatullah, M.N., Darmawahyuni, A., Esafri, R., Firdaus, F., and Sapitri, A.I. (2021). AFibNet: An implementation of atrial fibrillation detection with convolutional neural network. BMC Med. Inf. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01571-1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"\u015eent\u00fcrk, \u00dc., Y\u00fcceda\u011f, \u0130., and Polat, K. (2018, January 19\u201321). Repetitive Neural Network (RNN) Based Blood Pressure Estimation Using PPG and ECG Signals. Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey.","DOI":"10.1109\/ISMSIT.2018.8567071"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xu, K., Jiang, X., Ren, H., Liu, X., and Chen, W. (2019, January 17\u201319). Deep Recurrent Neural Network for Extracting Pulse Rate Variability from Photoplethysmography During Strenuous Physical Exercise. Proceedings of the 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara, Japan.","DOI":"10.1109\/BIOCAS.2019.8918711"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kumar, A.K., Ritam, M., Han, L., Guo, S., and Chandra, R. (2022). Deep learning for predicting respiratory rate from biosignals. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105338"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/S2213-2600(18)30425-9","article-title":"Opening the black box of machine learning","volume":"6","year":"2018","journal-title":"Lancet Respir. Med."},{"key":"ref_31","unstructured":"Moody, B., Moody, G., Villarroel, M., Clifford, G., and Silva, I. (2020). Mimic-iii Waveform Database, Version 1.0, PhysioNet. Available online: https:\/\/physionet.org."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"euae065","DOI":"10.1093\/europace\/euae065","article-title":"Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation","volume":"26","author":"Gruwez","year":"2024","journal-title":"Europace"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45644","DOI":"10.1038\/srep45644","article-title":"Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram","volume":"7","author":"Tang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1109\/TASLP.2016.2545928","article-title":"Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition","volume":"24","author":"Kim","year":"2016","journal-title":"IEEE-ACM Trans. Audio Speech Lang. Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6731","DOI":"10.1038\/s41598-022-10808-6","article-title":"Research on recognition and classification of pulse signal features based on EPNCC","volume":"12","author":"Chen","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"123008","DOI":"10.1016\/j.eswa.2023.123008","article-title":"Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2.5 concentration long-term prediction","volume":"245","author":"Zheng","year":"2024","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:36:14Z","timestamp":1760110574000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,14]]},"references-count":36,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165243"],"URL":"https:\/\/doi.org\/10.3390\/s24165243","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,14]]}}}