应用机器学习技术检测标准12导联心电图的心脏纤维化

F. Melgarejo-Meseguer, F. Gimeno-Blanes, J. Rojo-álvarez, M. Salar-Alcaraz, J. Gimeno-Blanes, A. García-Alberola
{"title":"应用机器学习技术检测标准12导联心电图的心脏纤维化","authors":"F. Melgarejo-Meseguer, F. Gimeno-Blanes, J. Rojo-álvarez, M. Salar-Alcaraz, J. Gimeno-Blanes, A. García-Alberola","doi":"10.22489/CinC.2018.174","DOIUrl":null,"url":null,"abstract":"Hypertrophic cardiomyopathy (HCM) is a myocardial disorder that affects 0.2% of the population and it is genetically transmitted. Several ECG findings have been related to the presence of fibrosis in other cardiac diseases, but data for HCM in this setting are lacking. Our hypothesis is that fibrosis affects the electrical cardiac propagation in patients with HCM in a relatively specific way and that this effect may be detected with suitable postprocessing applied to the ECG signals. We used 43 standard 12-lead ECGs from patients with previous clinical diagnosis of HCM. Principal Component Analysis (PCA) was applied by combining the ECG-leads oriented to different anatomic regions, hence assessing the potential fibrosis effects in the resulting leads for postprocessing convenience. Linear classifier of Support Vector Machine type were used with several statistics extracted from the resulting PCA-components, including normalized power, standard deviation, kurtosis, skewness, and local maxima. Results reached 75.0% sensitivity, 80.0% specificity, 85.7% positive predictive value, 66.7% negative predictive value, and 76.9% accuracy in our database. There is evidence that myocardial fibrosis can be detected in patients with HCM by postprocessing their ECG signals.","PeriodicalId":215521,"journal":{"name":"2018 Computing in Cardiology Conference (CinC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Cardiac Fibrosis Detection Applying Machine Learning Techniques to Standard 12-Lead ECG\",\"authors\":\"F. Melgarejo-Meseguer, F. Gimeno-Blanes, J. Rojo-álvarez, M. Salar-Alcaraz, J. Gimeno-Blanes, A. García-Alberola\",\"doi\":\"10.22489/CinC.2018.174\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hypertrophic cardiomyopathy (HCM) is a myocardial disorder that affects 0.2% of the population and it is genetically transmitted. Several ECG findings have been related to the presence of fibrosis in other cardiac diseases, but data for HCM in this setting are lacking. Our hypothesis is that fibrosis affects the electrical cardiac propagation in patients with HCM in a relatively specific way and that this effect may be detected with suitable postprocessing applied to the ECG signals. We used 43 standard 12-lead ECGs from patients with previous clinical diagnosis of HCM. Principal Component Analysis (PCA) was applied by combining the ECG-leads oriented to different anatomic regions, hence assessing the potential fibrosis effects in the resulting leads for postprocessing convenience. Linear classifier of Support Vector Machine type were used with several statistics extracted from the resulting PCA-components, including normalized power, standard deviation, kurtosis, skewness, and local maxima. Results reached 75.0% sensitivity, 80.0% specificity, 85.7% positive predictive value, 66.7% negative predictive value, and 76.9% accuracy in our database. There is evidence that myocardial fibrosis can be detected in patients with HCM by postprocessing their ECG signals.\",\"PeriodicalId\":215521,\"journal\":{\"name\":\"2018 Computing in Cardiology Conference (CinC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Computing in Cardiology Conference (CinC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22489/CinC.2018.174\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Computing in Cardiology Conference (CinC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22489/CinC.2018.174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

肥厚性心肌病(HCM)是一种心肌疾病,影响0.2%的人口,它是遗传遗传的。一些ECG结果与其他心脏疾病的纤维化存在相关,但缺乏HCM在这种情况下的数据。我们的假设是纤维化以一种相对特定的方式影响HCM患者的心电传播,这种影响可以通过对ECG信号进行适当的后处理来检测。我们使用43例既往临床诊断为HCM的患者的标准12导联心电图。主成分分析(PCA)通过结合面向不同解剖区域的ecg导联来应用,从而评估导联中潜在的纤维化作用,以方便后处理。使用支持向量机类型的线性分类器,并从生成的pca分量中提取几种统计量,包括归一化功率、标准差、峰度、偏度和局部最大值。结果敏感性75.0%,特异性80.0%,阳性预测值85.7%,阴性预测值66.7%,准确率76.9%。有证据表明,心肌纤维化可以检测HCM患者通过后处理他们的心电信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cardiac Fibrosis Detection Applying Machine Learning Techniques to Standard 12-Lead ECG
Hypertrophic cardiomyopathy (HCM) is a myocardial disorder that affects 0.2% of the population and it is genetically transmitted. Several ECG findings have been related to the presence of fibrosis in other cardiac diseases, but data for HCM in this setting are lacking. Our hypothesis is that fibrosis affects the electrical cardiac propagation in patients with HCM in a relatively specific way and that this effect may be detected with suitable postprocessing applied to the ECG signals. We used 43 standard 12-lead ECGs from patients with previous clinical diagnosis of HCM. Principal Component Analysis (PCA) was applied by combining the ECG-leads oriented to different anatomic regions, hence assessing the potential fibrosis effects in the resulting leads for postprocessing convenience. Linear classifier of Support Vector Machine type were used with several statistics extracted from the resulting PCA-components, including normalized power, standard deviation, kurtosis, skewness, and local maxima. Results reached 75.0% sensitivity, 80.0% specificity, 85.7% positive predictive value, 66.7% negative predictive value, and 76.9% accuracy in our database. There is evidence that myocardial fibrosis can be detected in patients with HCM by postprocessing their ECG signals.
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
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:604180095
Book学术官方微信