Robust heart rate estimation from multimodal physiological signals using beat signal quality index based majority voting fusion method

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• 作者：Shalini A. Rankawat ; ^{shalinirankawat@hotmail.com} ; Rahul Dubey
• 关键词：Multimodal physiological signals ; Non-cardiovascular signals ; R-peak artifact ; Beat signal quality index ; Majority voting fusion method ; Robust heart rate
• 刊名：Biomedical Signal Processing and Control
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：33
• 期：Complete
• 页码：201-212
• 全文大小：2698 K
• 卷排序：33

文摘

A new beat SQI based majority voting fusion method has been proposed for robust heart rate estimation from fusion of cardiovascular and non-cardiovascular (NC) signals. A novel statistical and probability based beat SQI assessment method has been proposed. It has achieved accuracy of 0.91 on PhysioNet/CinC Challenge-2011 set-a database. We have used Slope Sum Function and Teager-Kaiser Energy operator method for R-peak artifacts detection in NC signals. The majority voting fusion method has achieved score of 90.89% in heart beat detection on PhysioNet/CinC Challenge-2014 test dataset and 99.77% on MIT-BIH Polysomnographic dataset. The proposed method has substantially improved average HR rMSE from 15.54 bpm to 0.24 bpm for noisy ECG signals and from 11.68 bpm to 0.84 bpm for noisy ECG and noisy ABP signals of PhysioNet/CinC Challenge-2014 training database.