Robust and Sensitive Method of Lyapunov Exponent for Heart Rate Variability
For researchers and clinicians analyzing HRV, this provides a more sensitive tool, though the improvement is incremental over existing methods.
The paper proposes a modified Lyapunov exponent algorithm (Mazhar-Eslam) to improve sensitivity and accuracy in detecting heart rate variability (HRV), addressing inconsistencies between existing Rosenstein and Wolf methods.
Heart Rate Variability (HRV) plays an important role for reporting several cardiological and non-cardiological diseases. Also, the HRV has a prognostic value and is therefore quite important in modelling the cardiac risk. The nature of the HRV is chaotic, stochastic and it remains highly controversial. Because the HRV has utmost importance, it needs a sensitive tool to analyze the variability. In previous work, Rosenstein and Wolf had used the Lyapunov exponent as a quantitative measure for HRV detection sensitivity. However, the two methods diverge in determining the HRV sensitivity. This paper introduces a modification to both the Rosenstein and Wolf methods to overcome their drawbacks. The introduced Mazhar-Eslam algorithm increases the sensitivity to HRV detection with better accuracy.