Fuzzy logic approach for heart rate variability

Abstract

Intrioution. The heart rate variability (HRV) is based on measuring (time) intervals between R-peaks (of RR-intervals) of an electrocardiogram (ECG) and plotting a rhythmogram on their basis with its subsequent analysis by various mathematical methods that are classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM) [1, 2]. Diversity of methods and approaches to analysis of HRV is stemming from complexity and nonlinearity of the phenomenon itself, as well as from greater diversity of physiological reactions of an organism, both in normal and pathological states. Therefore, it appears relevant and important to incorporate currently existing HRV indicators and norms into a unified Fuzzy Logic (FL) methodology, which in turn will allow to integrally assess each metric and HRV results as a whole. Objective. We propose a Fuzzy Logic algorithm for incorporating into a single view of each metric, – Time Domain, Frequency Domain, Nonlinear Methods and HRV as a whole. Materials and methods. We define by FL the extent of belonging to normal state both for each distinct HRV metric – TD, FD and NM, and for a patient's HRV in general. Membership functions of any HRV index and defuzzification rules for FL scores was defined. In order to implement the proposed algorithm, specified parameters of mean values of HRV (М) indicators and their standard deviation (σ) have been found in scientific publications on HRV [1, 3, 7, 8, 9, 10]. We use for FL algorithm demonstration a long-term HRV records by Massachusetts Institute of Technology - Boston’s Beth Israel Hospital (MIT-BIH) from [11], a free-access, on-line archive of physiological signals for Normal Sinus Rhythm (NSR) RR Interval, Congestive Heart Failure (CHF) RR Interval and Atrial Fibrillation (AF) Databases [12]. Conclusion. In this article, we have presented a comprehensive view of HRV by Fuzzy Logic technology and thoroughly examined the peculiarities of its application and interpretation. Of all considered examples of FL analysis, the worst result is demonstrated by a patient from the AF group, while the best one belongs to a patient from the NSR group. Difference in FL Scores between these patients from NSR and CHF groups is almost 4 times, while between patients from NSR and АF groups it is almost 6 times. It appears especially important to implement such a design in portable medical devices for quick and easy interpretation of numerous parameters measured by them.

Heart rate variability series analyzing by fuzzy logic approach

“…Statistically, it corresponds to 99,8 % of confidence level of validity of statement about abnormal value of a parameter. According to presented defuzzification rules [5]: FL { we calculate FL scores of all HRV indicators. Mean values of FL scores for each metric and for all HRV indicators will define the extent of validity of argument about normalcy of state of each metric and of the whole HRV.…”

“…A good review of HRV metrics and norms is provided in [4], in which it is highlighted that every one of the reviewed HRV metrics -TD, FD and NM, has its own distinct features and advantages. We incorporated currently existing HRV indicators into a unified Fuzzy Logic (FL) methodology [5], which in turn will allow to integrally assess each metric and HRV results as a whole. There are more advanced topics of FL applications for HRV [6,7,8,9,10].…”