Jian-jun Yan, Guangyao Zhu, Rui Guo, Yiqin Wang, Haixia Yan
{"title":"基于tcn的腕脉波序列诊断冠状动脉粥样硬化性心脏病严重程度模型","authors":"Jian-jun Yan, Guangyao Zhu, Rui Guo, Yiqin Wang, Haixia Yan","doi":"10.1145/3469678.3469713","DOIUrl":null,"url":null,"abstract":"The pulse wave at the human radial artery is closely related to the health status of the cardiovascular system. In this paper, the morphological features of the pulse wave were used to establish a diagnostic model for the severity of coronary atherosclerotic heart disease (CAD). Features of waveform variations were extracted from pulse wave sequences by building a deep learning network, Temporal Convolutional Network (TCN), which mined more detailed waveform information and obtained more comprehensive features of waveform morphology than the classical time domain features extraction method, thus established a TCN-based CAD severity diagnostic model (TCSDM) with better performance. The 64 features extracted by TCN have shown significant differences between the three classes of CAD samples at the 0.05 level, which have provided additional basis for the model's classification decisions. The accuracy of TCSDM has reached 91.17%, an 11.93% improvement compared to the Random Forest-based diagnostic model using classical time domain features. The proposed method for the acquisition of pulse wave morphological features can effectively extract the differential features of different pulse waves. And this method has a great application value in the remote diagnosis of CAD severity because it's non-invasive, rapid and low-cost.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"94 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TCN-Based Diagnostic Model for the Severity of Coronary Atherosclerotic Heart Disease Using Wrist Pulse Wave Sequence\",\"authors\":\"Jian-jun Yan, Guangyao Zhu, Rui Guo, Yiqin Wang, Haixia Yan\",\"doi\":\"10.1145/3469678.3469713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The pulse wave at the human radial artery is closely related to the health status of the cardiovascular system. In this paper, the morphological features of the pulse wave were used to establish a diagnostic model for the severity of coronary atherosclerotic heart disease (CAD). Features of waveform variations were extracted from pulse wave sequences by building a deep learning network, Temporal Convolutional Network (TCN), which mined more detailed waveform information and obtained more comprehensive features of waveform morphology than the classical time domain features extraction method, thus established a TCN-based CAD severity diagnostic model (TCSDM) with better performance. The 64 features extracted by TCN have shown significant differences between the three classes of CAD samples at the 0.05 level, which have provided additional basis for the model's classification decisions. The accuracy of TCSDM has reached 91.17%, an 11.93% improvement compared to the Random Forest-based diagnostic model using classical time domain features. The proposed method for the acquisition of pulse wave morphological features can effectively extract the differential features of different pulse waves. And this method has a great application value in the remote diagnosis of CAD severity because it's non-invasive, rapid and low-cost.\",\"PeriodicalId\":22513,\"journal\":{\"name\":\"The Fifth International Conference on Biological Information and Biomedical Engineering\",\"volume\":\"94 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Fifth International Conference on Biological Information and Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3469678.3469713\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Fifth International Conference on Biological Information and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3469678.3469713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
TCN-Based Diagnostic Model for the Severity of Coronary Atherosclerotic Heart Disease Using Wrist Pulse Wave Sequence
The pulse wave at the human radial artery is closely related to the health status of the cardiovascular system. In this paper, the morphological features of the pulse wave were used to establish a diagnostic model for the severity of coronary atherosclerotic heart disease (CAD). Features of waveform variations were extracted from pulse wave sequences by building a deep learning network, Temporal Convolutional Network (TCN), which mined more detailed waveform information and obtained more comprehensive features of waveform morphology than the classical time domain features extraction method, thus established a TCN-based CAD severity diagnostic model (TCSDM) with better performance. The 64 features extracted by TCN have shown significant differences between the three classes of CAD samples at the 0.05 level, which have provided additional basis for the model's classification decisions. The accuracy of TCSDM has reached 91.17%, an 11.93% improvement compared to the Random Forest-based diagnostic model using classical time domain features. The proposed method for the acquisition of pulse wave morphological features can effectively extract the differential features of different pulse waves. And this method has a great application value in the remote diagnosis of CAD severity because it's non-invasive, rapid and low-cost.