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Updated: Jun 8, 2025

Author Spotlight: Exploring the Effects of Transauricular Vagus Nerve Stimulation
Published on: January 19, 2024
Effects of vagal nerve stimulation parameters on heart rate variability in epilepsy patients
Ahmet Genç1, Firdevs Ezgi Uçan Tokuç2, Meltem Korucuk2
1Department of Cardiology, Antalya Provincial Health Directorate, Antalya Training and Research Hospital, Antalya, Türkiye.
Introduction:
Vagal nerve stimulation (VNS) is used as an alternative treatment in drug-resistant epilepsy patients. Effects of VNS on the cardiac autonomic system are controversial. In this study, we aimed to investigate the relationship between VNS parameters and heart rate variability (HRV) in epilepsy patients who underwent VNS treatment.
Methods:
Our study included 31 patients who underwent VNS for drug-resistant epilepsy. Patients were divided into groups according to response to VNS and VNS parameters. All patients underwent 24-h Holter ECG.
Results:
The mean age of 31 VNS-treated epilepsy patients included in the study was 33.87 ± 7.6 years. When patients were grouped according to VNS response, 25 patients were in the VNS responder group and six patients were in the VNS-nonresponder group. When comparing Holter parameters in the VNS responder and non-responder groups, the median HF was significantly lower in the VNS responder group. VNS duration and signal frequency had a positive effect on LF/HF, while output and off time had a negative effect on LF/HF. When ROC analysis was performed to determine the cut-off values of the parameters for the VNS-responsive state, the AUC value of the HF parameter was 0.780, which was statistically significant. The cut-off value to distinguish response to VNS was 156.9.
Conclusion:
In conclusion, the effects of VNS parameters on HRV parameters are quite complex. However, the conclusion is that VNS is a neuromodulation method that affects the autonomic system in a complex way. Different levels of VNS parameters may also contribute to this effect. Furthermore, HRV parameters can be used as biomarkers to predict the patient population that may benefit from VNS.
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