{"title":"Automatic SWT Based QRS Detection Using Weighted Subbands and Shannon Energy Peak Amplification for ECG Signal Analysis Devices","authors":"Jomole Varghese V, M. Manikandan, R. B. Pachori","doi":"10.1109/CCIP57447.2022.10058632","DOIUrl":null,"url":null,"abstract":"In this paper, we present a straightforward automatic QRS complex detection method for electrocardiogram (ECG) signal analysis applications. The proposed method consists of stationary wavelet transform (SWT) for suppressing low- and high-frequency noises and extracting QRS complexes, amplitude thresholding to suppress the effect residual noise components, Shannon energy based peak amplitude normalization, negative zero-crossing for detecting peaks candidate smoothed QRS complex waveform and peak correction for determining true R peaks in the ECG signal. On the standard MIT-BIH database, our method had an accuracy of 99.50%, sensitivity of 99.69%, and a positive predictivity of 99.81 %. The proposed method outperforms other existing methods which included sets of amplitude-and duration-dependent thresholds to include or reject missed R peaks and noise peaks, respectively that may not work in practise for the case of QRS complex with irregular rates and long-pause between two consecutive QRS complexes.","PeriodicalId":309964,"journal":{"name":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIP57447.2022.10058632","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we present a straightforward automatic QRS complex detection method for electrocardiogram (ECG) signal analysis applications. The proposed method consists of stationary wavelet transform (SWT) for suppressing low- and high-frequency noises and extracting QRS complexes, amplitude thresholding to suppress the effect residual noise components, Shannon energy based peak amplitude normalization, negative zero-crossing for detecting peaks candidate smoothed QRS complex waveform and peak correction for determining true R peaks in the ECG signal. On the standard MIT-BIH database, our method had an accuracy of 99.50%, sensitivity of 99.69%, and a positive predictivity of 99.81 %. The proposed method outperforms other existing methods which included sets of amplitude-and duration-dependent thresholds to include or reject missed R peaks and noise peaks, respectively that may not work in practise for the case of QRS complex with irregular rates and long-pause between two consecutive QRS complexes.