Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4973611 | Biomedical Signal Processing and Control | 2017 | 12 Pages |
Abstract
In this paper, we present a new beat signal quality index (SQI) based majority voting fusion algorithm for robust heart rate (HR) estimation from multimodal physiological signals, namely, cardiovascular and non-cardiovascular signals. A novel statistical and probabilistic based beat SQI assessment method has been developed for voting fusion. Modified slope sum function and Teager-Kaiser energy operator method has been used for beat detection in electrocardiogram (ECG) and non-cardiovascular signals. The performance of majority voting fusion method in beat detection has been evaluated on PhysioNet/CinC Challenge-2014 public training dataset and has achieved overall score of 94.93%. The performance of the algorithm has been tested on PhysioNet/CinC Challenge-2014 hidden test set and MIT-BIH Polysomnographic dataset and it has achieved scores of 90.89% and 99.77% respectively. The proposed method has improved average rMSE of HR estimate from 15.54Â bpm to 0.24Â bpm for noisy ECG signals and from 11.68Â bpm to 0.84 bpm for noisy ECG and noisy ABP signals of PhysioNet/CinC Challenge-2014 public training database. The majority voting fusion method has yielded HR estimate with average rMSE of 1.80Â bpm, when both ECG (avg. rMSE of 4.58Â bpm) and ABP (avg. rMSE of 3.96Â bpm) signals of MIT-BIH Polysomnographic dataset are noisy. The use of multimodal signals in fusion has increased the accuracy of HR estimates in noisy ECG and ABP signals. The majority voting fusion algorithm based on beat SQI has enabled effective and reliable use of non-cardiovascular signals in robust HR estimation from multimodal physiological signals, even when both ECG and ABP signals are noisy.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Shalini A. Rankawat, Rahul Dubey,