Input Feature Selection in ECG Signal Data Modelling using Long Short Term Memory

Ahmad Saikhu, C. V. Hudiyanti, Arya Yudhi Wijaya
{"title":"Input Feature Selection in ECG Signal Data Modelling using Long Short Term Memory","authors":"Ahmad Saikhu, C. V. Hudiyanti, Arya Yudhi Wijaya","doi":"10.1109/ISRITI54043.2021.9702810","DOIUrl":null,"url":null,"abstract":"One of the diseases that are a significant burden worldwide is cardiovascular disorders, diseases related to the work of the heart have a high probability of causing death. So we need a tool or model to detect the patient's heart signal against the risk of cardiovascular disorders. Electrocardiogram (ECG) recordings are often used to capture the propagation or propagation of electrical signals in the heart from the patient's body surface. Reading the ECG signal data is very tiring because every second, there are around 180 points that are captured which consist of the patient's pulse, movement, and breath. In this research, input feature selection will be carried out using the Long Short Term Memory method for ECG signal data. The results of the prediction of the ECG signal can be used to predict and treat cardiovascular disorders. Furthermore, the results of the model performance that the Long Short Term Memory model with one input, namely (t-1), is the best compared to using two or four input features.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRITI54043.2021.9702810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

One of the diseases that are a significant burden worldwide is cardiovascular disorders, diseases related to the work of the heart have a high probability of causing death. So we need a tool or model to detect the patient's heart signal against the risk of cardiovascular disorders. Electrocardiogram (ECG) recordings are often used to capture the propagation or propagation of electrical signals in the heart from the patient's body surface. Reading the ECG signal data is very tiring because every second, there are around 180 points that are captured which consist of the patient's pulse, movement, and breath. In this research, input feature selection will be carried out using the Long Short Term Memory method for ECG signal data. The results of the prediction of the ECG signal can be used to predict and treat cardiovascular disorders. Furthermore, the results of the model performance that the Long Short Term Memory model with one input, namely (t-1), is the best compared to using two or four input features.
基于长短期记忆的心电信号数据建模中的输入特征选择
在世界范围内造成重大负担的疾病之一是心血管疾病,与心脏工作有关的疾病有很高的导致死亡的概率。因此,我们需要一种工具或模型来检测患者的心脏信号,以防范心血管疾病的风险。心电图(ECG)记录常用于捕捉来自患者体表的电信号在心脏中的传播或传播。读取心电图信号数据是非常累人的,因为每秒要捕获大约180个点,这些点包括患者的脉搏、运动和呼吸。在本研究中,将使用长短期记忆方法对心电信号数据进行输入特征选择。心电信号的预测结果可用于心血管疾病的预测和治疗。此外,模型性能的结果表明,与使用两个或四个输入特征相比,使用一个输入即(t-1)的长短期记忆模型是最好的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信