A Variational Mode Decomposition-Based Approach for Heart Rate Monitoring using Wrist-Type Photoplethysmographic Signals during Intensive Physical Exercise
Main Article Content
Abstract
Heart rate monitoring using photoplethysmographic (PPG) signals recorded from wrist during intensive physical exercise is challenging because the PPG signals are contaminated by strong motion artifact. In this paper, we present a new approach for PPG based heart rate monitoring. We first perform the variational mode decomposition to decompose the PPG signal into multiple modes then eliminate the modes whose frequencies coincides with those from accelerator signals. Finally, the spectral analysis step is applied to estimate the spectrum of the signal and selects the spectral peaks corresponding to heart rate. Experimental results on a public available dataset recorded from 12 subjects during fast running validate the performance of the proposed algorithm.
Keywords
Adaptive motion artifact cancellation, Photoplethysmographic (PPG), Heart rate monitoring, Variational mode decomposition, Spectral analysis
Article Details
References
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[3]. E. Khan, F. Al Hossain, S. Uddin, S. Alam, and M. Hasan, A Robust Heart Rate Monitoring Scheme Using Photoplethysmographic Signals Corrupted by Intense Motion Artifacts, IEEE Transactions on Biomedical Engineering, vol. 63, pp. 550 - 562, 2016.
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[6]. R. Yousefi, M. Nourani, S. Ostadabbas, and I. Panahi, A motion-tolerant, adaptive algorithm for wearable photoplethysmography biosensors, IEEE Journal of Biomedical and Health Informatics, vol. 18, pp. 670 - 681, 2014.
[7]. M. Ram, K. V. Madhav, E. H. Krishna, N. R. Komalla, and K. A. Reddy, A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter, IEEE Transactions on Instrumentation and Measurement vol. 61, pp. 1445 - 1457, 2012.
[8]. B. Kim and S. Yoo, Motion artifact reduction in photoplethysmography using independent component analysis, IEEE Trans. Biomed. Eng., vol. 53, pp. 566-568, 2006.
[9]. B. Sun and Z. Zhang, Photoplethysmography-based heart rate monitoring using asymmetric least squares spectrum subtraction and bayesian decision theory, IEEE Sensors Journal vol. 15, pp. 7161 - 7168, 2015.
[10]. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, et al., The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis, Proc. R. Soc. Lond., vol. 454A, pp. 903–995, 1998.
[11]. X. Sun, P. Yang, Y. Li, Z. Gao, and Y.-T. Zhang, Robust heart beat detection from photoplethysmography interlaced with motion artifacts, based on empirical mode decomposition, in Proceedings of International Conference on Biomedical and Health Informatics, pp. 775-778, 2012.
[12]. K. Dragomiretskiy and D. Zosso, Variational mode decomposition, IEEE Transactions on Signal Processing, vol. 62, pp. 531 - 544, 2014.
[2]. J. Allen, Photoplethysmography and its application in clinical physiological measurement, Phys Meas, vol. 28, pp. 1-39, 2007.
[3]. E. Khan, F. Al Hossain, S. Uddin, S. Alam, and M. Hasan, A Robust Heart Rate Monitoring Scheme Using Photoplethysmographic Signals Corrupted by Intense Motion Artifacts, IEEE Transactions on Biomedical Engineering, vol. 63, pp. 550 - 562, 2016.
[4]. M. Mashhadi, F. Asadi, M. Eskandari, S. Kiani, and F. Marvasti, Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise with Simultaneous Accelerometry, IEEE Signal Processing Letters, vol. 23, pp. 227-231, 2016.
[5]. Z. Zhang, Z. Pi, and B. Liu, TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise, IEEE Transactions on Biomedical Engineering vol. 62, pp. 522 - 531, 2015.
[6]. R. Yousefi, M. Nourani, S. Ostadabbas, and I. Panahi, A motion-tolerant, adaptive algorithm for wearable photoplethysmography biosensors, IEEE Journal of Biomedical and Health Informatics, vol. 18, pp. 670 - 681, 2014.
[7]. M. Ram, K. V. Madhav, E. H. Krishna, N. R. Komalla, and K. A. Reddy, A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter, IEEE Transactions on Instrumentation and Measurement vol. 61, pp. 1445 - 1457, 2012.
[8]. B. Kim and S. Yoo, Motion artifact reduction in photoplethysmography using independent component analysis, IEEE Trans. Biomed. Eng., vol. 53, pp. 566-568, 2006.
[9]. B. Sun and Z. Zhang, Photoplethysmography-based heart rate monitoring using asymmetric least squares spectrum subtraction and bayesian decision theory, IEEE Sensors Journal vol. 15, pp. 7161 - 7168, 2015.
[10]. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, et al., The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis, Proc. R. Soc. Lond., vol. 454A, pp. 903–995, 1998.
[11]. X. Sun, P. Yang, Y. Li, Z. Gao, and Y.-T. Zhang, Robust heart beat detection from photoplethysmography interlaced with motion artifacts, based on empirical mode decomposition, in Proceedings of International Conference on Biomedical and Health Informatics, pp. 775-778, 2012.
[12]. K. Dragomiretskiy and D. Zosso, Variational mode decomposition, IEEE Transactions on Signal Processing, vol. 62, pp. 531 - 544, 2014.