D. Bojanic, R. Petrovic, N. Jorgovanovic, D. Popović
{"title":"二进小波实时心率监测","authors":"D. Bojanic, R. Petrovic, N. Jorgovanovic, D. Popović","doi":"10.1109/NEUREL.2006.341195","DOIUrl":null,"url":null,"abstract":"We developed the new intelligent virtual ECG device by integrating the dyadic wavelet (DyWT) based algorithm for QRS complex detection into the virtual teleECG. The new virtual instrument (VI) was realized by using LabVIEW software. The system allows real-time detection of the heart rhythm, offline analysis of the previously recorded signals or offline analysis when using the system via Internet. The new system allows the physician to locate and recognize life threatening events in ECG recordings and provides the patient with an ECG alarm system. The tests on data from a MIT-BIH database show that the DyWT based detector detects accurately 99.53% of QRS complexes, and similarly better than 99% for the clinical recordings. The analysis in clinical environment showed that in ECG signals comprising a sharp and large T wave the algorithm must be fine tuned, otherwise it could result with classifying the T wave as the R peak","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Dyadic Wavelets for Real-time Heart Rate Monitoring\",\"authors\":\"D. Bojanic, R. Petrovic, N. Jorgovanovic, D. Popović\",\"doi\":\"10.1109/NEUREL.2006.341195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We developed the new intelligent virtual ECG device by integrating the dyadic wavelet (DyWT) based algorithm for QRS complex detection into the virtual teleECG. The new virtual instrument (VI) was realized by using LabVIEW software. The system allows real-time detection of the heart rhythm, offline analysis of the previously recorded signals or offline analysis when using the system via Internet. The new system allows the physician to locate and recognize life threatening events in ECG recordings and provides the patient with an ECG alarm system. The tests on data from a MIT-BIH database show that the DyWT based detector detects accurately 99.53% of QRS complexes, and similarly better than 99% for the clinical recordings. The analysis in clinical environment showed that in ECG signals comprising a sharp and large T wave the algorithm must be fine tuned, otherwise it could result with classifying the T wave as the R peak\",\"PeriodicalId\":231606,\"journal\":{\"name\":\"2006 8th Seminar on Neural Network Applications in Electrical Engineering\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 8th Seminar on Neural Network Applications in Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2006.341195\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2006.341195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dyadic Wavelets for Real-time Heart Rate Monitoring
We developed the new intelligent virtual ECG device by integrating the dyadic wavelet (DyWT) based algorithm for QRS complex detection into the virtual teleECG. The new virtual instrument (VI) was realized by using LabVIEW software. The system allows real-time detection of the heart rhythm, offline analysis of the previously recorded signals or offline analysis when using the system via Internet. The new system allows the physician to locate and recognize life threatening events in ECG recordings and provides the patient with an ECG alarm system. The tests on data from a MIT-BIH database show that the DyWT based detector detects accurately 99.53% of QRS complexes, and similarly better than 99% for the clinical recordings. The analysis in clinical environment showed that in ECG signals comprising a sharp and large T wave the algorithm must be fine tuned, otherwise it could result with classifying the T wave as the R peak