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

Abstract

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.