{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:36:00Z","timestamp":1783074960046,"version":"3.54.6"},"reference-count":22,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T00:00:00Z","timestamp":1607299200000},"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":["62076177"],"award-info":[{"award-number":["62076177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772358"],"award-info":[{"award-number":["61772358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Association (NYHA) functional classification system. Furthermore, in order to make the reported results more realistic, we used an inter-patient paradigm to divide the dataset, and segmented the ECG signals into two different intervals. The experimental results show that the proposed multi-scale ResNet-34 has achieved an average positive predictive value, sensitivity and accuracy of 93.49%, 93.44% and 93.60% respectively for two seconds of ECG segments. We have also obtained an average positive predictive value, sensitivity and accuracy of 94.16%, 93.79% and 94.29% respectively for five seconds of ECG segments. The proposed method can be used as an auxiliary tool to help doctors to classify CHF.<\/jats:p>","DOI":"10.3390\/sym12122019","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T22:15:36Z","timestamp":1607638536000},"page":"2019","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Classification of Congestive Heart Failure from ECG Segments with a Multi-Scale Residual Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8310-7684","authenticated-orcid":false,"given":"Dengao","family":"Li","sequence":"first","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Jinzhong 030024, China"},{"name":"Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Tao","sequence":"additional","affiliation":[{"name":"Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong 030024, China"},{"name":"College of Information and Computer, Taiyuan University of Technology, Jinzhong 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jumin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong 030024, China"},{"name":"College of Information and Computer, Taiyuan University of Technology, Jinzhong 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Wu","sequence":"additional","affiliation":[{"name":"Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong 030024, China"},{"name":"College of Information and Computer, Taiyuan University of Technology, Jinzhong 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.csbj.2016.11.001","article-title":"Heart failure: Diagnosis, severity estimation and prediction of adverse events through machine learning techniques","volume":"15","author":"Tripoliti","year":"2017","journal-title":"Comput. 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