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.
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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).
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© 2016 Springer International Publishing Switzerland
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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
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DOI: https://doi.org/10.1007/978-3-319-42297-8_15
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