Background: The purpose of this study was to clarify the dynamics of heart rate variability (HRV) in the dynamics of alcohol withdrawal syndrome relief. Methods: We examined 31 patients at an average age of 43. 9 ± 5. 2 years with alcohol withdrawal syndrome (the middle stage, the period of maintenance therapy) and 15 volunteers of comparable age. The control points for assessing HRV were 1 to 2, 3 to 5, 6 to 9, and 10 to 15 days after stopping alcohol consumption. Electrocardiography was performed using the software and hardware complex Polyspectr-12 (Neurosoft, Russia). HRV was evaluated using a set of statistical and spectral analytical methods, including the coefficient of variation (%), and spectral analysis indicators (based on the fast Fourier transform algorithm using all points without smoothing). The geometric analysis of the cycles of nonlinear cardiac rhythm waves was performed using a chaos test via the method of Gavrilushkin (2007). Results: In our patients with alcohol withdrawal syndrome in the rehabilitation period, there was a pronounced instability of the heart rhythm, which was manifested in the increasing role of its intracardiac regulation mechanisms and the stimulating effect of sympathicotonia. The most pronounced shifts in HRV were detected in the period from 3 to 9 days after the cessation of alcohol consumption. Additionally, the risk of arrhythmogenic complications in alcohol withdrawal syndrome was highest from the third to the ninth day of the rehabilitation period. Conclusions: Our patients with alcohol withdrawal syndrome in the rehabilitation period exhibited a pronounced instability in their heart rhythm, manifesting itself in the increasing role of its intracardiac regulation mechanisms and the stimulating effect of sympathicotonia.

Epilepsy is a neural disorder with unknown nature and epileptic patients suffer from the consequences of unexpected seizures. In this paper, we proposed a new method to predict epileptic seizures using heart rate variability (HRV) signal analysis. During preictal period of epilepsy, increasing in nervous activities of neurons affects the autonomic nervous system that disturbs heart rates. Therefore, epileptic seizures can be predicted through HRV monitoring. In our method, we extracted 12 features of HRV signal from different domains: time, frequency, time-frequency and non-linear domain. We used Multivariate Statistical Process Control (MSPC) algorithm for anomaly detection which is able to detect anomalies that cannot be detected by monitoring each variable independently. This algorithm has been applied to the clinical data collected from 17 patients. The obtained results demonstrated that the proposed method can predict seizure onset with an accuracy of 88. 2%. The proposed HRV-based seizure prediction algorithm is more promising than the conventional EEG-based methods from the viewpoint of practical use.

Background: Heart rate variability biofeedback (HRVB) is an approach to ameliorate conditions in which HRV is relatively low. Some patients with paroxysmal atrial fibrillation (AF) show increased adrenergic tone in their paroxysms. Methods: We conducted this study to determine the effects of HRVB on patients with paroxysmal AF. Thirty-one patients (11 women) at an average age of 58± 10 years (38– 79 y) with paroxysmal AF were included in the study. Of these, 19% had AF during exertion; 29% during rest; and in the remaining 52%, episodes were mixed. A 24-hour ambulatory Holter monitoring was done before and after 5 weeks of biofeedback training. Results: The interpretation of Holter monitoring disclosed that high frequency changed significantly after HRVB. Clinically, 12 patients felt better, 4 patients felt worse, and 15 patients felt no obvious change. Low frequency and more importantly very low frequency decreased, which was due to a decrement in sympathetic tone. Conclusions: HRVB in patients with adrenergic AF might reduce their episodes of paroxysmal AF and help them feel better.