DOI:

Inventory reference

ISSN 1812-7231 Klin.inform.telemed. Volume 16, Issue 17, 2021, Pages 00-00

Author(s)

Raimondi G.¹, Mobilia P.¹, Barsi L.¹, Martynenko A.²

Institution(s)

¹University of Roma "Sapienza", Roma, Italy

²V.N.Karazin Kharkiv National University, Kharkiv, Ukraine

Article title

Fuzzy logic approach for analysis of the heart rate variability of dialyzed patients

Abstract (resume)

Introduction. The number of patients with chronic kidney disease (CKD) has increased over the last few decades because of the increasing incidence of chronic diseases, including diabetes and hypertension. Cardiovascular events account for a majority of deaths in patients with CKD, and they can be attributed to hypertension, deranged lipid metabolism, fluid overload, and autonomic nervous system dysfunction. Some investigations have evaluated the association between heart rate variability (HRV) indices and hemodynamic status during hemodialysis [7-8]. A lower HRV has been shown to be an adverse outcome predictor of cardiovascular events, congestive heart failure, sudden cardiac death, CKD-related hospitalization, and incident end-stage renal disease in patients with CKD [9-13].

The purpose of the work. The aim of this study is to evaluate the neurovegetative cardiovascular regulation during haemodialysis by Heart Rate Variability (HRV).HRV analysis are using various mathematical methods which classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM) [14, 15]. We incorporated currently existing HRV indicators into a unified Fuzzy Logic (FL) methodology [17], which in turn will allow to integrally assess each metric and HRV results as a whole.

Materials and Methods. 24 young subjects aged between 20 and 30 (11 males and 13 females) have been enrolled as Control group and performed Tilt test.Standard 5 min. HRV was recorded in clinostatic and orthostatic for each twice. 31 patients (aging 65 ± 15 years old) with chronic kidney diseases have been enrolled as Dialysis group. Each patient has dialysis procedure (bicarbonate or ultrafiltration) and have been HRV recorded before dialysis, under dialysis and after dialysis. We used the advantages of Fuzzy Logic (FL) to incorporate various defined datainto a unified mathematical model of a fuzzy logical argument. In our case, we define 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.

The results of the study. We demonstrate whole HRV reactions before, during and af-ter dialysis procedure and HRV reaction during Tilt test by FL distances from abnormal state for Total HRV and each HRV metrics (TD, FD, NL) We assume the advantages of the proposed FL approach by analyzing HRV series: we can compare well known physiological reaction for Tilt test in Control group and total reaction of autonomic nervous system (ANS) during Dialysis procedure because in all cases we use the same FL distances metric from abnormal HRV states.

Conclusion. HRV is a complex phenomenon, study of which requires various approach-es and methods. However, a comprehensive view of HRV is only possible when there is a technology similar to Fuzzy Logic, one that allows to combine all used methods and approaches into an integral assessment. In this article, we showed the Fuzzy Logic approach for series of Heart Rate Variability records for the group of patients before, during and after dialysis procedure (Dialysis group) and during Tilt test (Control group).

We demonstrated in Total FL view: no changes before and during dialyses procedure and about 8% improving of distance from HRV abnormality after dialysis that is comparable with physiological reaction for Tilt test in Control group, – 13% improving of distance from HRV abnormality from clinostatic to orthostatic position.

Keywords

dialysis, heart rate variability, fuzzy logic

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