L. Bachi, M. Varanini, Magda Costi, D. Lombardi, F. Rangoni, L. Billeci
{"title":"Multichannel ECG Filtering: Source Consistency Filtering, Eigenfiltering and Traditional Methods","authors":"L. Bachi, M. Varanini, Magda Costi, D. Lombardi, F. Rangoni, L. Billeci","doi":"10.22489/CinC.2022.168","DOIUrl":null,"url":null,"abstract":"Noise reduction is a fundamental aspect of stress electrocardiogram <tex>$(ECG)$</tex> recording. In this setting, muscular noise represents the main antagonist to signal quality. A possible solution to muscle noise in stress <tex>$ECG$</tex> is to exploit the information redundancy in 12 - lead recordings to reduce noise while preserving the <tex>$ECG$</tex> signal. Source Consistency Filtering <tex>$(SCF)$</tex> is a spatial redundancy filter that follows this principle. In this paper, we compare the muscle noise rejection performance of conventional <tex>$25Hz$</tex> and <tex>$40Hz$</tex> low-pass filters (LPFs), the SC <tex>$F$</tex> ‘ and a method based on singular value decomposition <tex>$(SVD)$</tex> which exploits both the spatial and temporal correlation in the <tex>$ECG$</tex> signal. Our results indicate that the <tex>$SCF$</tex> can afford a <tex>$QRS$</tex> complex distortion lower than that of a 40 <tex>$Hz$</tex> lowpass filter while still maintaining a high noise rejection. The <tex>$QRS$</tex> detection accuracy on the filtered <tex>$ECG$</tex> was comparable for all methods except for the <tex>$SVD$</tex> filter, which allowed a superior detection performance score in all the records.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Computing in Cardiology (CinC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22489/CinC.2022.168","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
Noise reduction is a fundamental aspect of stress electrocardiogram $(ECG)$ recording. In this setting, muscular noise represents the main antagonist to signal quality. A possible solution to muscle noise in stress $ECG$ is to exploit the information redundancy in 12 - lead recordings to reduce noise while preserving the $ECG$ signal. Source Consistency Filtering $(SCF)$ is a spatial redundancy filter that follows this principle. In this paper, we compare the muscle noise rejection performance of conventional $25Hz$ and $40Hz$ low-pass filters (LPFs), the SC $F$ ‘ and a method based on singular value decomposition $(SVD)$ which exploits both the spatial and temporal correlation in the $ECG$ signal. Our results indicate that the $SCF$ can afford a $QRS$ complex distortion lower than that of a 40 $Hz$ lowpass filter while still maintaining a high noise rejection. The $QRS$ detection accuracy on the filtered $ECG$ was comparable for all methods except for the $SVD$ filter, which allowed a superior detection performance score in all the records.