{"title":"ECG Signal Analysis for Patient with Metabolic Syndrome based on 1D-Convolution Neural Network","authors":"Chhayly Lim, Jung-Yeon Kim, Yunyoung Nam","doi":"10.1109/CSCI51800.2020.00134","DOIUrl":null,"url":null,"abstract":"Metabolic syndrome (MetS) is a cluster of metabolic disorders associated with medical conditions: abdominal obesity, high blood pressure, insulin resistance, etc. People with MetS have a higher risk of cardiovascular diseases and type 2 diabetes mellitus. Hence, early detection of MetS can be useful in the field of healthcare. In this paper, we propose a 1D-Convolution Neural Network (1D-CNN) model for classifying the electrocardiogram (ECG) signals of the GBBANet online database into two classes: a group of people with the medical condition (MetS [n=15]) and a control group (CG [n=10]). The dataset consists of 5 ECG recordings per person. The proposed 1D-CNN model has achieved an overall accuracy of 88.32%.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCI51800.2020.00134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Metabolic syndrome (MetS) is a cluster of metabolic disorders associated with medical conditions: abdominal obesity, high blood pressure, insulin resistance, etc. People with MetS have a higher risk of cardiovascular diseases and type 2 diabetes mellitus. Hence, early detection of MetS can be useful in the field of healthcare. In this paper, we propose a 1D-Convolution Neural Network (1D-CNN) model for classifying the electrocardiogram (ECG) signals of the GBBANet online database into two classes: a group of people with the medical condition (MetS [n=15]) and a control group (CG [n=10]). The dataset consists of 5 ECG recordings per person. The proposed 1D-CNN model has achieved an overall accuracy of 88.32%.