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With the rapid development of artificial intelligence, intelligent diagnosis methods based on deep learning are gradually applied in the medical field in order to reduce the dependence of diagnosis results on medical experience. Deep learning has made remarkable achievements in the field of image processing, through which deeper information can be extracted than through time-series signals. Therefore, this paper proposes a method of 2DECG diagnosis based on Faster R-CNN (Faster Region-based Convolutional Neural Network). First, the time-series ECG signal is transformed into two-dimensional curve. Then, the Faster R-CNN model based on beat is obtained by using dataset training. Finally, three kinds of ECG diseases are diagnosed by the Faster R-CNN model. The test results show that compared with the effect of one-dimensional CNN, the method proposed in this paper has high diagnosis accuracy and can help doctors to diagnose diseases more intuitively. <\/jats:p>","DOI":"10.1142\/s0218001421590072","type":"journal-article","created":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T09:46:20Z","timestamp":1599644780000},"page":"2159007","source":"Crossref","is-referenced-by-count":1,"title":["Intelligent Diagnosis Method Based on 2DECG Model"],"prefix":"10.1142","volume":"35","author":[{"given":"Weibo","family":"Song","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, P. R. China"},{"name":"College of Information Engineering, Dalian Ocean University, Dalian, Liaoning, P. R. 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