{"title":"一种有效的体域网可穿戴心电QRS检测算法","authors":"Fei Zhang, Jun‐Kai Tan, Y. Lian","doi":"10.1109/BIOCAS.2007.4463342","DOIUrl":null,"url":null,"abstract":"A novel QRS detection algorithm for wearable ECG devices and its FPGA implementation are presented in this paper. The proposed algorithm utilizes the hybrid opening- closing mathematical morphology filtering to suppress the impulsive noise and remove the baseline drift and uses modulus accumulation to enhance the signal. The proposed algorithm achieves an average QRS detection rate of 99.53%, a sensitivity of 99.82% and a positive prediction of 99.71% against the MIT/BIH Arrhythmia Database. It compares favorably to published methods.","PeriodicalId":273819,"journal":{"name":"2007 IEEE Biomedical Circuits and Systems Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"An Effective QRS Detection Algorithm for Wearable ECG in Body Area Network\",\"authors\":\"Fei Zhang, Jun‐Kai Tan, Y. Lian\",\"doi\":\"10.1109/BIOCAS.2007.4463342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel QRS detection algorithm for wearable ECG devices and its FPGA implementation are presented in this paper. The proposed algorithm utilizes the hybrid opening- closing mathematical morphology filtering to suppress the impulsive noise and remove the baseline drift and uses modulus accumulation to enhance the signal. The proposed algorithm achieves an average QRS detection rate of 99.53%, a sensitivity of 99.82% and a positive prediction of 99.71% against the MIT/BIH Arrhythmia Database. It compares favorably to published methods.\",\"PeriodicalId\":273819,\"journal\":{\"name\":\"2007 IEEE Biomedical Circuits and Systems Conference\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Biomedical Circuits and Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIOCAS.2007.4463342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Biomedical Circuits and Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOCAS.2007.4463342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Effective QRS Detection Algorithm for Wearable ECG in Body Area Network
A novel QRS detection algorithm for wearable ECG devices and its FPGA implementation are presented in this paper. The proposed algorithm utilizes the hybrid opening- closing mathematical morphology filtering to suppress the impulsive noise and remove the baseline drift and uses modulus accumulation to enhance the signal. The proposed algorithm achieves an average QRS detection rate of 99.53%, a sensitivity of 99.82% and a positive prediction of 99.71% against the MIT/BIH Arrhythmia Database. It compares favorably to published methods.