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Sensors 2017, 17(3), 506; doi:10.3390/s17030506

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China
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Author to whom correspondence should be addressed.
Academic Editor: Xue Wang
Received: 31 December 2016 / Revised: 16 February 2017 / Accepted: 28 February 2017 / Published: 3 March 2017
(This article belongs to the Section Physical Sensors)
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Abstract

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise. View Full-Text
Keywords: adaptive filter; compressive sensing; heart rate estimation; wrist-type photoplethysmography (WPPG); principle component analysis (PCA); support vector machine (SVM) adaptive filter; compressive sensing; heart rate estimation; wrist-type photoplethysmography (WPPG); principle component analysis (PCA); support vector machine (SVM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xiong, J.; Cai, L.; Wang, F.; He, X. SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals. Sensors 2017, 17, 506.

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