Abstract
Heart rate variability (HRV) analysis yields important insights into the understanding of physiological mechanisms. The HRV analysis is a powerful tool for risk prediction in heart diseases. Most common methods of HRV analysis are fast Fourier transform and autoregressive methods. However, these methods have limitations in the study of long-term nonlinear variations and transient analysis of heart rate variability. Wavelet transform-based HRV analysis overcomes these limitations. This paper identifies the characteristics of wavelet transform in heart rate variability analysis. The number of wavelet filters is suggested in the literature, but every wavelet filter has a specific category of application. To investigate the wavelet filters for heart rate variability analysis, RR tachogram extracted from five minutes of ECG signal recorded from a healthy volunteer. The appropriate wavelet filter should be adaptive to slow and fast variation in the HRV signal. The different wavelet filter performances are assessed, and the observations presented in the results revealed that Db-3 (Daubechies) with six-filter length is the most suitable wavelet filter for HRV analysis.
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Acknowledgements
The authors gratefully acknowledge the support and facilities provided by the Department of Instrumentation and Control Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar (Punjab).
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Sarla, Singh, D. (2022). Performance Analysis of Wavelet Filters for Heart Rate Variability Analysis. In: Vashista, M., Manik, G., Verma, O.P., Bhardwaj, B. (eds) Recent Innovations in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-9236-9_11
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DOI: https://doi.org/10.1007/978-981-16-9236-9_11
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