{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:26:55Z","timestamp":1771489615239,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T00:00:00Z","timestamp":1583971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61773110"],"award-info":[{"award-number":["No.61773110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Provincial Natural Science Foundation Project","award":["20170540320"],"award-info":[{"award-number":["20170540320"]}]},{"name":"Liaoning Provincial Doctor Initiation Fund","award":["20170520358"],"award-info":[{"award-number":["20170520358"]}]},{"name":"Central University Basic Scientific Research Operational Expenses Project","award":["N172415005-2"],"award-info":[{"award-number":["N172415005-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.<\/jats:p>","DOI":"10.3390\/s20061579","type":"journal-article","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T12:22:51Z","timestamp":1584015771000},"page":"1579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal"],"prefix":"10.3390","volume":"20","author":[{"given":"Dongqi","family":"Wang","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Meng","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongming","family":"Chen","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hupo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8360-3605","authenticated-orcid":false,"given":"Lisheng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,12]]},"reference":[{"key":"ref_1","unstructured":"(2020, February 13). Cardiovascular Diseases (CVDs). Available online: https:\/\/www.who.int\/en\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1378\/chest.125.4.1561","article-title":"The clinical value of the ECG in noncardiac conditions","volume":"125","author":"Sabbe","year":"2004","journal-title":"Chest"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1161\/01.CIR.85.3.1221","article-title":"Guidelines for electrocardiography. A report of the American College of Cardiology\/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Committee on Electrocardiography)","volume":"85","author":"Schlant","year":"1992","journal-title":"Circulation"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.cmpb.2015.12.008","article-title":"ECG-based heartbeat classification for arrhythmia detection: A survey","volume":"127","author":"Luz","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2930","DOI":"10.1109\/TBME.2012.2213253","article-title":"Heartbeat classification using morphological and dynamic features of ECG signals","volume":"59","author":"Ye","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Acharya, U.R., Fujita, H., Adam, M., Lih, O.S., Hong, T.J., Sudarshan, V.K., and Koh, J.E. (2016, January 9\u201312). Automated Characterization of Arrhythmias Using Nonlinear Features from Tachycardia ECG Beats. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844294"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1640005","DOI":"10.1142\/S0219519416400054","article-title":"Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers","volume":"16","author":"Desai","year":"2016","journal-title":"J. Mech. Med. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., and Sun, J. (2015, January 13\u201316). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.neucom.2015.03.017","article-title":"Subset based deep learning for RGB-D object recognition","volume":"165","author":"Bai","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 22\u201325). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_11","unstructured":"Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., and Chen, G. (2016, January 19\u201324). Deep speech 2: End-to-end Speech Recognition in English and Mandarin. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_12","unstructured":"Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., and Coates, A. (2014). Deep speech: Scaling up end-to-end speech recognition. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"key":"ref_16","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":"Tan","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.eswa.2018.07.030","article-title":"ECG classification using three-level fusion of different feature descriptors","volume":"114","author":"Golrizkhatami","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guanglong, M., Xiangqing, W., and Junsheng, Y. (2019). ECG Signal Classification Algorithm Based on Fusion Features, IOP Publishing.","DOI":"10.1088\/1742-6596\/1207\/1\/012003"},{"key":"ref_19","unstructured":"Cakaloglu, T., and Xu, X. (2019). A multi-resolution word embedding for document retrieval from large unstructured knowledge bases. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8\u201310). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_21","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegaas, NV, USA."},{"key":"ref_22","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegaas, NV, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.bspc.2013.01.005","article-title":"ECG beat classification using PCA, LDA, ICA and discrete wavelet transform","volume":"8","author":"Martis","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1161\/01.CIR.93.5.1043","article-title":"Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology","volume":"93","author":"Camm","year":"1996","journal-title":"Circulation"},{"key":"ref_27","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1007\/s13534-014-0158-7","article-title":"Heartbeat classification using decision level fusion","volume":"4","author":"Zhang","year":"2014","journal-title":"Biomed. Eng. Lett."},{"key":"ref_29","unstructured":"(2020, February 13). Experiment Dataset (ECG Signals). Available online: http:\/\/tianchi-competition.oss-cn-hangzhou.aliyuncs.com\/231754\/round2\/hf_round2_train.zip."},{"key":"ref_30","unstructured":"(2020, February 13). Experiment Dataset (Label). Available online: http:\/\/tianchi-competition.oss-cn-hangzhou.aliyuncs.com\/231754\/round2\/hf_round2_train.txt."},{"key":"ref_31","unstructured":"(2020, February 13). Experiment Dataset (Arrythmia). Available online: http:\/\/tianchi-competition.oss-cn-hangzhou.aliyuncs.com\/231754\/round2\/hf_round2_arrythmia.txt."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_33","unstructured":"DeVries, T., and Taylor, G.W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv."},{"key":"ref_34","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_35","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1579\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:06:18Z","timestamp":1760173578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,12]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20061579"],"URL":"https:\/\/doi.org\/10.3390\/s20061579","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,12]]}}}