Abstract
In order to improve the accuracy and real-timelines of heart rate variability (HRV) estimation in electrocardiogram (ECG) signals interferences, a novel HRV estimation method based on photoplethysmography (PPG) signals is proposed. The short-time autocorrelation principle is used to detect interferences in ECG signals, then, the improving sliding window iterative Discrete Fourier Transform (DFT) is used to estimate HRV in ECG interferences from the synchronously acquisitioned PPG signals. The international commonly used MIT-BIT Arrhythmia Database/Challenge 2014 Training Set is used to verify the interferences detection algorithm and HRV estimation algorithm which are proposed. At the same time, the proposed algorithms are compared with recently existing representative interferences detection algorithm based on RR intervals and PRV directly replaced HRV algorithm, respectively. The results show that the proposed methods are more accurate and more real-time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Stein, P.K.: Challenges of heart rate variability research in the ICU. Crit. Care Med. 41(2), 666–667 (2013)
Karlen, W., Kobayashi, K.: Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation. Physiol. Measur. 33, 1617–1629 (2012)
Orphanidou, C., Bonnici, T., Charlton, P., et al.: Signal-quality indices for the electrocardiogram and Photoplethysmogram: derivation and applications to wireless monitoring. IEEE J. Biomed. Health Inf. 19(3), 832–838 (2015)
Li, Q., Cadathur, R., Clifford, G.D.: A machine learning approach to multi-level ECG signal quality classification. Comput. Methods Programs Biomed. 117, 435–447 (2014)
Yu, E.Z., He, D.N., Su, Y.F., et al.: Feasibility analysis for pulse rate variability to replace heart rate variability of the healthy subjects. In: International Conference on Robotics and Biomimetics (ROBIO), pp. 1065–1070 (2013)
Schulte, R., Krug, J., Rose, G.: Identification of a signal for an optimal heart beat detection in multimodal physiological datasets. In: Computing in Cardiology Conference (CinC), pp. 273–276. IEEE (2014)
Zhang, A.H., Chou, Y.X.: Dynamic pulse signal acquisition and processing. Med. Apparatus Instrum. J. China 36(2), 79–84 (2012)
Lin, Y.Y., Xu, Y.H., Liu, X.B.: Harmonic current detection method of sliding window iteration DFT. Power Syst. Prot. Control 39(3), 78–90 (2011)
Zhang, A.H., Wang, P., Chou, Y.X.: Pulse signal peak detection algorithm based on dynamic differential threshold. J. Jilin Univ. Eng. Sci. 44(3), 847–853 (2014)
Acknowledgments
This work was supported by the National Natural Science Foundation (grant 81360229) of China, the National Key Laboratory Open Project Foundation (grant 201407347) of Pattern Recognition in China and the Basic Research Innovation Group Project in Gansu Province (grant 1506RJIA031).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, A., Wang, Q., Chou, Y. (2016). Heart Rate Variability Estimation in Electrocardiogram Signals Interferences Based on Photoplethysmography Signals. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-42297-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42296-1
Online ISBN: 978-3-319-42297-8
eBook Packages: Computer ScienceComputer Science (R0)