{"_id":"hhvEhKnswek49Nr87","bibbaseid":"che-zhang-zhu-qu-jin-constrainedtransformernetworkforecgsignalprocessingandarrhythmiaclassification-2021","author_short":["Che, C.","Zhang, P.","Zhu, M.","Qu, Y.","Jin, B."],"bibdata":{"bibtype":"article","type":"article","title":"Constrained transformer network for ECG signal processing and arrhythmia classification","volume":"21","issn":"1472-6947","url":"https://doi.org/10.1186/s12911-021-01546-2","doi":"10.1186/s12911-021-01546-2","abstract":"Heart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower.","number":"1","urldate":"2023-03-12","journal":"BMC Medical Informatics and Decision Making","author":[{"propositions":[],"lastnames":["Che"],"firstnames":["Chao"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Peiliang"],"suffixes":[]},{"propositions":[],"lastnames":["Zhu"],"firstnames":["Min"],"suffixes":[]},{"propositions":[],"lastnames":["Qu"],"firstnames":["Yue"],"suffixes":[]},{"propositions":[],"lastnames":["Jin"],"firstnames":["Bo"],"suffixes":[]}],"month":"June","year":"2021","keywords":"CNNs, ECG signal, Link constraints, Transformer","pages":"184","bibtex":"@article{che_constrained_2021,\n\ttitle = {Constrained transformer network for {ECG} signal processing and arrhythmia classification},\n\tvolume = {21},\n\tissn = {1472-6947},\n\turl = {https://doi.org/10.1186/s12911-021-01546-2},\n\tdoi = {10.1186/s12911-021-01546-2},\n\tabstract = {Heart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower.},\n\tnumber = {1},\n\turldate = {2023-03-12},\n\tjournal = {BMC Medical Informatics and Decision Making},\n\tauthor = {Che, Chao and Zhang, Peiliang and Zhu, Min and Qu, Yue and Jin, Bo},\n\tmonth = jun,\n\tyear = {2021},\n\tkeywords = {CNNs, ECG signal, Link constraints, Transformer},\n\tpages = {184},\n}\n\n","author_short":["Che, C.","Zhang, P.","Zhu, M.","Qu, Y.","Jin, B."],"key":"che_constrained_2021","id":"che_constrained_2021","bibbaseid":"che-zhang-zhu-qu-jin-constrainedtransformernetworkforecgsignalprocessingandarrhythmiaclassification-2021","role":"author","urls":{"Paper":"https://doi.org/10.1186/s12911-021-01546-2"},"keyword":["CNNs","ECG signal","Link constraints","Transformer"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"article","biburl":"https://api.zotero.org/users/1528358/collections/VYQBMSWJ/items?key=ZpFELJbRRRzw7HareFKZ4Uix&format=bibtex&limit=100","dataSources":["ZX4taNS949XiyXgAT"],"keywords":["cnns","ecg signal","link constraints","transformer"],"search_terms":["constrained","transformer","network","ecg","signal","processing","arrhythmia","classification","che","zhang","zhu","qu","jin"],"title":"Constrained transformer network for ECG signal processing and arrhythmia classification","year":2021}