{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:34Z","timestamp":1750309594124,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,1,10]]},"DOI":"10.1145\/3727353.3727377","type":"proceedings-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T16:28:15Z","timestamp":1748363295000},"page":"140-145","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Hybrid Model Based on Deep Learning for Myocardial Infarction Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4599-9898","authenticated-orcid":false,"given":"Mingxi","family":"Han","sequence":"first","affiliation":[{"name":"School of Software, Liaoning Technical University, Huludao, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0865-6640","authenticated-orcid":false,"given":"Chi","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1983-7206","authenticated-orcid":false,"given":"Siyuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, Liaoning Technical University, Huludao, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2344-1283","authenticated-orcid":false,"given":"Baoming","family":"Pu","sequence":"additional","affiliation":[{"name":"School of Electrical and Intelligent Control, Liaoning Institute of Science and Engineering, Jinzhou, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"e_1_3_3_1_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehy655"},{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Han Chuang and Li Shi. \"ML\u2013ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG.\"\u00a0Computer methods and programs in biomedicine\u00a0185 (2020): 105138.","DOI":"10.1016\/j.cmpb.2019.105138"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Xiong Peng et al. \"Localization of myocardial infarction with multi-lead ECG based on DenseNet.\"\u00a0Computer Methods and Programs in Biomedicine\u00a0203 (2021): 106024.","DOI":"10.1016\/j.cmpb.2021.106024"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Jahmunah Vicneswary et al. \"Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals.\"\u00a0Computers in Biology and Medicine\u00a0146 (2022): 105550.","DOI":"10.1016\/j.compbiomed.2022.105550"},{"key":"e_1_3_3_1_5_2","volume-title":"5366-5384","author":"Rai Hari Mohan","year":"2022","unstructured":"Rai, Hari Mohan, and Kalyan Chatterjee. \"Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data.\"\u00a0Applied Intelligence\u00a052.5 (2022): 5366-5384."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Hochreiter S. \"Long Short-term Memory.\"\u00a0Neural Computation MIT-Press\u00a0(1997).","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Liu Wenhan et al. \"MFB-CBRNN: A hybrid network for MI detection using 12-lead ECGs.\"\u00a0IEEE journal of biomedical and health informatics\u00a024.2 (2019): 503-514.","DOI":"10.1109\/JBHI.2019.2910082"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Schuster Mike and Kuldip K. Paliwal. \"Bidirectional recurrent neural networks.\"\u00a0IEEE transactions on Signal Processing\u00a045.11 (1997): 2673-2681.","DOI":"10.1109\/78.650093"},{"key":"e_1_3_3_1_9_2","first-page":"3","volume":"7","author":"Omarov Batyrkhan","year":"2023","unstructured":"Omarov, Batyrkhan, et al. \"Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms.\"\u00a0Computers, Materials & Continua\u00a076.3 (2023).","journal-title":"\"Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms.\"\u00a0Computers, Materials & Continua\u00a0"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Hu Jie Li Shen and Gang Sun. \"Squeeze-and-excitation networks.\"\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_3_1_11_2","unstructured":"Vaswani A. \"Attention is all you need.\"\u00a0Advances in Neural Information Processing Systems\u00a0(2017)."},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Goldberger Ary L. et al. \"PhysioBank PhysioToolkit and PhysioNet: components of a new research resource for complex physiologic signals.\"\u00a0circulation\u00a0101.23 (2000): e215-e220.","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.13026\/kfzx-aw45"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Wagner Patrick et al. \"PTB-XL a large publicly available electrocardiography dataset.\"\u00a0Scientific data\u00a07.1 (2020): 1-15.","DOI":"10.1038\/s41597-020-0495-6"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Pan Jiapu and Willis J. Tompkins. \"A real-time QRS detection algorithm.\" IEEE transactions on biomedical engineering 3 (1985): 230-236.","DOI":"10.1109\/TBME.1985.325532"},{"key":"e_1_3_3_1_16_2","first-page":"4","volume":"5","author":"Mart\u00ednez Juan Pablo","year":"2004","unstructured":"Mart\u00ednez, Juan Pablo, et al. \"A wavelet-based ECG delineator: evaluation on standard databases.\"\u00a0IEEE Transactions on biomedical engineering\u00a051.4 (2004): 570-581.","journal-title":"\"A wavelet-based ECG delineator: evaluation on standard databases.\"\u00a0IEEE Transactions on biomedical engineering\u00a0"},{"key":"e_1_3_3_1_17_2","volume-title":"Deep residual learning for image recognition.\"\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition","author":"He Kaiming","year":"2016","unstructured":"He, Kaiming, et al. \"Deep residual learning for image recognition.\"\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. 2016."},{"key":"e_1_3_3_1_18_2","unstructured":"Ross T-YLPG and G. K. H. P. Doll\u00e1r. \"Focal loss for dense object detection.\"\u00a0proceedings of the IEEE conference on computer vision and pattern recognition. 2017."},{"key":"e_1_3_3_1_19_2","volume-title":"IEEE","author":"Yu Jie","year":"2022","unstructured":"Yu, Jie, et al. \"QT-STNet: A Spatial and Temporal Network Combined with QT Segment for MI Detection and Location.\"\u00a02022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2022."},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Jing Junyuan et al. \"ECG\u2010Based Multiclass Arrhythmia Classification Using Beat\u2010Level Fusion Network.\"\u00a0Journal of Healthcare Engineering\u00a02023.1 (2023): 1755121.","DOI":"10.1155\/2023\/1755121"},{"key":"e_1_3_3_1_21_2","volume-title":"IEEE","author":"Garg Saloni","year":"2024","unstructured":"Garg, Saloni, et al. \"ECG-Lense: Benchmarking ML & DL Models on PTB-XL Dataset.\"\u00a02024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). IEEE, 2024."},{"key":"e_1_3_3_1_22_2","volume-title":"International Conference on Networking and Advanced Systems (ICNAS). IEEE","author":"Azzem Yousra Chahinez","year":"2023","unstructured":"Azzem, Yousra Chahinez Hadj, and Fouzi Harrag. \"Explainable Deep Learning Based-System for Multilabel Classification of 12-Lead ECG.\"\u00a02023 International Conference on Networking and Advanced Systems (ICNAS). IEEE, 2023."},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Chen Xiehui et al. \"Acute myocardial infarction detection using deep learning-enabled electrocardiograms.\"\u00a0Frontiers in cardiovascular medicine\u00a08 (2021): 654515.","DOI":"10.3389\/fcvm.2021.654515"},{"key":"e_1_3_3_1_24_2","unstructured":"Moorthi M. \"Detection and Classification of electrocardiography using hybrid deep learning models.\"\u00a0Hellenic Journal of Cardiology\u00a0(2024)."},{"key":"e_1_3_3_1_25_2","volume-title":"2024 International Joint Conference on Neural Networks (IJCNN). IEEE","author":"Xie Linhai","year":"2024","unstructured":"Xie, Linhai, Yilei Man, and Delong Shang. \"Multiscale Residual Network with Dynamic Depthwise Convolution for Multi-label 12-Lead ECG Classification.\" 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024."},{"key":"e_1_3_3_1_26_2","volume-title":"1121","author":"\u015amigiel Sandra","year":"2021","unstructured":"\u015amigiel, Sandra, Krzysztof Pa\u0142czy\u0144ski, and Damian Ledzi\u0144ski. \"ECG signal classification using deep learning techniques based on the PTB-XL dataset.\"\u00a0Entropy\u00a023.9 (2021): 1121."}],"event":{"name":"BDICN 2025: 2025 4th International Conference on Big Data, Information and Computer Network","acronym":"BDICN 2025","location":"Guangzhou China"},"container-title":["Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3727353.3727377","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3727353.3727377","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:56:56Z","timestamp":1750298216000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3727353.3727377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":26,"alternative-id":["10.1145\/3727353.3727377","10.1145\/3727353"],"URL":"https:\/\/doi.org\/10.1145\/3727353.3727377","relation":{},"subject":[],"published":{"date-parts":[[2025,1,10]]},"assertion":[{"value":"2025-05-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}