基于hrv的不同分类器房颤检测特征选择研究

2021 Signal Processing Symposium (SPSympo) Pub Date : 2021-09-20 DOI:10.1109/spsympo51155.2020.9593769

Szymon Buś, K. Jędrzejewski, P. Guzik

{"title":"基于hrv的不同分类器房颤检测特征选择研究","authors":"Szymon Buś, K. Jędrzejewski, P. Guzik","doi":"10.1109/spsympo51155.2020.9593769","DOIUrl":null,"url":null,"abstract":"We investigated the selection of features used in various machine learning algorithms to detect atrial fibrillation. The features were derived from the analysis of Heart Rate Variability from ECG. We show how the feature selection impacts the statistical metrics of the atrial fibrillation detection and identify the best feature sets for particular classifiers.","PeriodicalId":380515,"journal":{"name":"2021 Signal Processing Symposium (SPSympo)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Study on Selection of HRV-based Features for Different Classifiers in Atrial Fibrillation Detection\",\"authors\":\"Szymon Buś, K. Jędrzejewski, P. Guzik\",\"doi\":\"10.1109/spsympo51155.2020.9593769\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigated the selection of features used in various machine learning algorithms to detect atrial fibrillation. The features were derived from the analysis of Heart Rate Variability from ECG. We show how the feature selection impacts the statistical metrics of the atrial fibrillation detection and identify the best feature sets for particular classifiers.\",\"PeriodicalId\":380515,\"journal\":{\"name\":\"2021 Signal Processing Symposium (SPSympo)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Signal Processing Symposium (SPSympo)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/spsympo51155.2020.9593769\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Signal Processing Symposium (SPSympo)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/spsympo51155.2020.9593769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

我们研究了各种机器学习算法中用于检测房颤的特征选择。这些特征是通过分析心电图的心率变异性得出的。我们展示了特征选择如何影响心房颤动检测的统计指标，并为特定分类器确定最佳特征集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Study on Selection of HRV-based Features for Different Classifiers in Atrial Fibrillation Detection

We investigated the selection of features used in various machine learning algorithms to detect atrial fibrillation. The features were derived from the analysis of Heart Rate Variability from ECG. We show how the feature selection impacts the statistical metrics of the atrial fibrillation detection and identify the best feature sets for particular classifiers.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2021 Signal Processing Symposium (SPSympo)

自引率

0.00%

发文量