A. Kuzmin, Maxim Mitrokhin, N. Mitrokhina, Mikhail M. Rovnyagin, A. Alimuradov
{"title":"Intelligent data processing scheme for mobile heart monitoring system","authors":"A. Kuzmin, Maxim Mitrokhin, N. Mitrokhina, Mikhail M. Rovnyagin, A. Alimuradov","doi":"10.1109/SCM.2017.7970653","DOIUrl":null,"url":null,"abstract":"Paper describes some aspects of data processing in mobile heart monitoring systems. Authors highlight an important cardiological and social problem of arrhythmic pathologies of the heart and the possibilities of new medical equipment to detect the arrythmia. The architecture and the features of up-to-date monitoring systems are investigated. The specificity of long term ECG records is examined via the example of PAF Prediction Challenge Database from physionet.org. The scheme of open architecture modular ECG device is developed for experimental research of HRV analysis and arrhythmia paroxysms prediction. Data processing scheme that enables to design portative monitoring system for the detection of signs of arrhythmia and predict the arrhythmia paroxysm is proposed. Set of required ECG signal processing methods and algorithms is chosen.","PeriodicalId":315574,"journal":{"name":"2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)","volume":"278 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCM.2017.7970653","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
Paper describes some aspects of data processing in mobile heart monitoring systems. Authors highlight an important cardiological and social problem of arrhythmic pathologies of the heart and the possibilities of new medical equipment to detect the arrythmia. The architecture and the features of up-to-date monitoring systems are investigated. The specificity of long term ECG records is examined via the example of PAF Prediction Challenge Database from physionet.org. The scheme of open architecture modular ECG device is developed for experimental research of HRV analysis and arrhythmia paroxysms prediction. Data processing scheme that enables to design portative monitoring system for the detection of signs of arrhythmia and predict the arrhythmia paroxysm is proposed. Set of required ECG signal processing methods and algorithms is chosen.