{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:29:22Z","timestamp":1763468962489,"version":"3.38.0"},"reference-count":30,"publisher":"Tech Science Press","issue":"1","license":[{"start":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T00:00:00Z","timestamp":1729382400000},"content-version":"vor","delay-in-days":293,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.053817","type":"journal-article","created":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T08:18:04Z","timestamp":1726301884000},"page":"971-983","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Extraction of Medical Latent Variables from ECG Signals Utilizing a Mutual Information-Based Technique and Capsular Neural Networks for Arrhythmia Detection"],"prefix":"10.32604","volume":"81","author":[{"given":"Abbas Ali","family":"Hassan","sequence":"first","affiliation":[]},{"given":"Fardin","family":"Abdali-Mohammadi","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","series-title":"IEEE Int. Conf. Bioinform. Biomed. (BIBM)","first-page":"1865","article-title":"Arrhythmia classification using CGAN-augmented ECG signals","author":"Adib","year":"2022"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.3390\/math11030562","article-title":"Classifying cardiac arrhythmia from ECG signal using 1D CNN deep learning model","volume":"11","author":"Ahmed","year":"2023","journal-title":"Mathematics"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"87387","DOI":"10.1109\/ACCESS.2022.3192390","article-title":"A review of classification techniques for arrhythmia patterns using convolutional neural networks and Internet of Things (IoT) devices","volume":"10","author":"Agyeman","year":"2022","journal-title":"IEEE Access"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2023.1246746","article-title":"Deep learning for ECG Arrhythmia detection and classification: An overview of progress for period 2017\u20132023","volume":"14","author":"Ansari","year":"2023","journal-title":"Front. Physiol."},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1177\/11779322221149600","article-title":"A literature review: ECG-based models for arrhythmia diagnosis using artificial intelligence techniques","volume":"17","author":"Boulif","year":"2023","journal-title":"Bioinform. Biol. Insights"},{"key":"ref6","series-title":"2023 2nd Int. Conf. Paradigm Shifts Commun. Embed. Syst., Mach. Learn. Signal Process. (PCEMS)","first-page":"1","article-title":"Capsule network for 1-D biomedical signals: A review","author":"Chaitanya","year":"2023"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ymeth.2021.04.021","article-title":"Automated ECG classification based on 1D deep learning network","volume":"202","author":"Chen","year":"2022","journal-title":"Methods"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s13198-021-01548-3","article-title":"A review of different ECG classification\/detection techniques for improved medical applications","volume":"13","author":"Gupta","year":"2022","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.3390\/s22239347","article-title":"Efficient lightweight multimodel deep fusion based on ECG for arrhythmia classification","volume":"22","author":"Hammad","year":"2022","journal-title":"Sensors"},{"key":"ref10","first-page":"5088","article-title":"Detection of arrhythmia in ECG signal using deep learning methods\u2013A exhaustive review & summary of the concepts & techniques","volume":"44","author":"Honnashamaiah","year":"2023","journal-title":"Tuijin Jishu\/J. Propuls. Technol."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.14704\/WEB\/V19I1\/WEB19078","article-title":"Classification and detection of ECG arrhythmia and myocardial infarction using deep learning: A review","volume":"19","author":"Rawi","year":"2022","journal-title":"Webology"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.3390\/bioengineering9040152","article-title":"A hybrid deep learning approach for ECG-based arrhythmia classification","volume":"9","author":"Madan","year":"2022","journal-title":"Bioengineering"},{"key":"ref13","first-page":"302","author":"Kusumoto","year":"2020","journal-title":"ECG Interpretation: From Pathophysiology to Clinical Application (no. 1)"},{"key":"ref14","article-title":"Learning explainable time-morphology patterns for automatic arrhythmia classification from short single-lead ECGs","volume":"21","author":"Lee","year":"2021","journal-title":"Sensors"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1007\/s12652-021-03275-w","article-title":"An effective arrhythmia classification via ECG signal subsampling and mutual information based subbands statistical features selection","volume":"14","author":"Mian Qaisar","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref16","article-title":"Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model","volume":"37","author":"Mohonta","year":"2022","journal-title":"Sens. Biosens. Res."},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102766","article-title":"A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN","volume":"68","author":"Sepahvand","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref18","first-page":"2281","article-title":"A DNN-based low power ECG co-processor architecture to classify cardiac arrhythmia for wearable devices","volume":"69","author":"Janveja","year":"2022","journal-title":"IEEE Trans. Circuits Syst. II: Express Briefs"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.3390\/app13084964","article-title":"Deep learning-based ECG arrhythmia classification: A systematic review","volume":"13","author":"Xiao","year":"2023","journal-title":"Appl. Sci."},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102262","article-title":"ECG arrhythmia classification by using a recurrence plot and convolutional neural network","volume":"64","author":"Mathunjwa","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104206","article-title":"Arrhythmia classification algorithm based on multi-head self-attention mechanism","volume":"79","author":"Wang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref22","article-title":"Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet","volume":"12","author":"Li","year":"2022","journal-title":"Sci. Rep."},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-020-0386-x","article-title":"A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients","volume":"7","author":"Zheng","year":"2020","journal-title":"Sci. Data"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","article-title":"Arrhythmia detection using deep convolutional neural network with long duration ECG signals","volume":"102","author":"Y\u0131ld\u0131r\u0131m","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref25","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.jelectrocard.2019.11.046","article-title":"Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram","volume":"58","author":"Li","year":"2020","journal-title":"J. Electrocardiol."},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1186\/s12874-024-02223-4","article-title":"Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads","volume":"24","author":"Andayeshgar","year":"2024","journal-title":"BMC Med. Res. Methodol."},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105740","article-title":"Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records","volume":"197","author":"Yildirim","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115031","article-title":"Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals","volume":"181","author":"Faust","year":"2021","journal-title":"Expert. Syst. Appl."},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105114","article-title":"Self-supervised representation learning from 12-lead ECG data","volume":"141","author":"Mehari","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref30","article-title":"A deep learning model for the classification of atrial fibrillation in critically ill patients","volume":"11","author":"Chen","year":"Jan. 13, 2023","journal-title":"Intensive Care Med. Exp."}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-81-1\/TSP_CMC_53817\/TSP_CMC_53817.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T02:30:59Z","timestamp":1741314659000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v81n1\/58305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.053817","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-05-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-15","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}