Exploring different computational methods for the High-Frequency band of HRV to capture information related to RSA

• Published in: Biomedical signal processing and control Vol. 84; p. 104802
• Main Authors: Narang, Mahak, Singh, Mandeep
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Correlation; Fast Fourier Transformation (FFT); Heart Rate Variability (HRV); Hilbert Huang Transformation (HHT); Intrinsic Mode Function (IMF); Respiration Frequency (RF); Correlation; Heart Rate Variability (HRV); Fast Fourier Transformation (FFT); Respiration Frequency (RF); Hilbert Huang Transformation (HHT); Intrinsic Mode Function (IMF)
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.104802

•By studying the variation in heart rate, the sympathetic and vagal tones of ANS can be monitored.•This study is used to identifying a robust computational method for the narrowed HF-HRV band (RF ± 0.10) power that gives maximum negative correlation with respiration frequency.•The explored computational methods are FFT method on entire HRV signal, HHT method on the entire HRV signal, FFT method on the first IMF of HRV signal, and HHT method on the first IMF of HRV signal.•FFT method applied on the first IMF of HRV signal is recommended for the computation of HF-HRV band power that best captures information related to RSA. The human heart beats with varying frequencies as per the signal received from the Autonomic Nervous system (ANS), and this variation can be used to monitor and manage human stress. Natural breathing also induces variation in a heartbeat and is known as RSA(Respiratory Sinus Arrhythmia). Interestingly, this variation is in the same frequency band as that caused by stress, that is, the High Frequency (HF) band (0.12 Hz to 0.4 Hz) of Heart Rate Variability (HRV). This frequency band is broad enough to include information not related to respiration. This research aims at identifying a computational method for narrowed HF-HRV band (RF±0.10)power that gives maximum negative correlation with respiration frequency. The explored computational methods are the FFT method on the entire HRV signal, the HHT method on the entire HRV signal, the FFT method on the first IMF of the HRV signal, and the HHT method on the first IMF of the HRV signal. Eighteen subjects have been employed to carry out this study. The results show that the average negative correlation computed from the FFT method applied on the first IMF of the HRV signal using a narrower HF band comes out to be the strongest of all. Therefore, this computational method is recommended to selectively captures information related to RSA. This HF-HRV band power can be employed to distinguish between Normal Sinus Arrhythmia(NSA) and non-NSA, apart from monitoring and managing human stress.