scholarly journals Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Fang ◽  
Hong-Yun Liu ◽  
Zhi-Yan Wang ◽  
Zhao Yang ◽  
Tung-Yang Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Outcome Prediction ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Sleep State ◽  
Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

Heart-rate variability indices as predictors of the response to vagus nerve stimulation in patients with drug-resistant epilepsy

Epilepsia ◽  
2017 ◽  
Vol 58 (6) ◽  
pp. 1015-1022 ◽  
Author(s):  
Hongyun Liu ◽  
Zhao Yang ◽  
Lei Huang ◽  
Wei Qu ◽  
Hongwei Hao ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vagus Nerve ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Drug Resistant ◽  
Drug Resistant Epilepsy

scholarly journals Heart rate variability and vagus nerve stimulation in epilepsy patients

2019 ◽  
Vol 10 (1) ◽  
pp. 223-232 ◽  
Author(s):  
Victor Constantinescu ◽  
Daniela Matei ◽  
Irina Constantinescu ◽  
Dan Iulian Cuciureanu
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vagus Nerve ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Autonomic Function ◽  
Drug Resistant ◽  
Cardiovascular Autonomic Function ◽  
Drug Resistant Epilepsy ◽  
The Impact

Abstract Background Vagus nerve stimulation (VNS) exerts a cortical modulating effect through its diffuse projections, especially involving cerebral structures related to autonomic regulation. The influence of VNS on cardiovascular autonomic function in drug-resistant epilepsy patients is still debated. We aimed to evaluate the impact of VNS on cardiovascular autonomic function in drug-resistant epilepsy patients, after three months of neurostimulation, using the heart rate variability (HRV) analysis. Methodology Multiple Trigonometric Regressive Spectral analysis enables a precise assessment of the autonomic control on the heart rate. We evaluated time and frequency-domain HRV parameters in resting condition and during sympathetic and parasympathetic activation tests in five epilepsy patients who underwent VNS procedure. Results We found appropriate cardiac autonomic responses to sympathetic and parasympathetic activation tests, described by RMSSD, pNN50, HF and LF/HF dynamics after three months of VNS. ON period of the neurostimulation may generate a transient vagal activation reflected on heart rate and RMSSD values, as observed in one of our cases. Conclusion VNS therapy in epilepsy patients seems not to disrupt the cardiac autonomic function. HRV represents a useful tool in evaluating autonomic activity. More extensive studies are needed to further explore cardiac autonomic response after neurostimulation.

scholarly journals Sign and magnitude scaling properties of heart rate fluctuations following vagus nerve stimulation in a patient with drug-resistant epilepsy

2018 ◽  
Vol 10 ◽  
pp. 78-81 ◽  
Author(s):  
Eduardo Gutiérrez-Maldonado ◽  
Claudia Ivette Ledesma-Ramírez ◽  
Adriana Cristina Pliego-Carrillo ◽  
José Javier Reyes-Lagos
Keyword(s):  
Heart Rate ◽  
Vagus Nerve ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Drug Resistant ◽  
Scaling Properties ◽  
Magnitude Scaling ◽  
Heart Rate Fluctuations ◽  
Drug Resistant Epilepsy

scholarly journals Altered Relationship Between Heart Rate Variability and fMRI-Based Functional Connectivity in People With Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Michalis Kassinopoulos ◽  
Ronald M. Harper ◽  
Maxime Guye ◽  
Louis Lemieux ◽  
Beate Diehl
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Basal Ganglia ◽  
Functional Connectivity ◽  
Functional Organization ◽  
Inferior Frontal Gyrus ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Rr Interval ◽  
Drug Resistant Epilepsy

Background: Disruptions in central autonomic processes in people with epilepsy have been studied through evaluation of heart rate variability (HRV). Decreased HRV appears in epilepsy compared to healthy controls, suggesting a shift in autonomic balance toward sympathetic dominance; recent studies have associated HRV changes with seizure severity and outcome of interventions. However, the processes underlying these autonomic changes remain unclear. We examined the nature of these changes by assessing alterations in whole-brain functional connectivity, and relating those alterations to HRV.Methods: We examined regional brain activity and functional organization in 28 drug-resistant epilepsy patients and 16 healthy controls using resting-state functional magnetic resonance imaging (fMRI). We employed an HRV state-dependent functional connectivity (FC) framework with low and high HRV states derived from the following four cardiac-related variables: 1. RR interval, 2. root mean square of successive differences (RMSSD), 4. low-frequency HRV (0.04–0.15 Hz; LF-HRV) and high-frequency HRV (0.15–0.40 Hz; HF-HRV). The effect of group (epilepsy vs. controls), HRV state (low vs. high) and the interactions of group and state were assessed using a mixed analysis of variance (ANOVA). We assessed FC within and between 7 large-scale functional networks consisting of cortical regions and 4 subcortical networks, the amygdala, hippocampus, basal ganglia and thalamus networks.Results: Consistent with previous studies, decreased RR interval (increased heart rate) and decreased HF-HRV appeared in people with epilepsy compared to healthy controls. For both groups, fluctuations in heart rate were positively correlated with BOLD activity in bilateral thalamus and regions of the cerebellum, and negatively correlated with BOLD activity in the insula, putamen, superior temporal gyrus and inferior frontal gyrus. Connectivity strength in patients between right thalamus and ventral attention network (mainly insula) increased in the high LF-HRV state compared to low LF-HRV; the opposite trend appeared in healthy controls. A similar pattern emerged for connectivity between the thalamus and basal ganglia.Conclusion: The findings suggest that resting connectivity patterns between the thalamus and other structures underlying HRV expression are modified in people with drug-resistant epilepsy compared to healthy controls.

Long Term Effects of Low Frequency (10 Hz) Vagus Nerve Stimulation on EEG and Heart Rate Variability in Crohn's Disease: A Case Report

2014 ◽  
Vol 7 (6) ◽  
pp. 914-916 ◽  
Author(s):  
Didier Clarençon ◽  
Sonia Pellissier ◽  
Valérie Sinniger ◽  
Astrid Kibleur ◽  
Dominique Hoffman ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Crohn’S Disease ◽  
Case Report ◽  
Vagus Nerve ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Low Frequency ◽  
Long Term Effects

scholarly journals Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriana Leal ◽  
Mauro F. Pinto ◽  
Fábio Lopes ◽  
Anna M. Bianchi ◽  
Jorge Henriques ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Time ◽  
Epileptic Seizures ◽  
Heart Rate Variability Analysis ◽  
Drug Resistant ◽  
Clustering Methods ◽  
Temporal Lobe Seizures ◽  
New Perspective ◽  
Drug Resistant Epilepsy

AbstractElectrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure’s clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

Vagus nerve stimulation for drug-resistant epilepsy

2019 ◽  
Vol 20 (3) ◽  
pp. 189-198 ◽  
Author(s):  
Laura Pérez-Carbonell ◽  
Howard Faulkner ◽  
Sean Higgins ◽  
Michalis Koutroumanidis ◽  
Guy Leschziner
Keyword(s):  
Vagus Nerve ◽  
Seizure Frequency ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Therapeutic Option ◽  
Drug Resistant ◽  
Drug Resistant Epilepsy ◽  
Randomised Controlled

Vagus nerve stimulation (VNS) is a neuromodulatory therapeutic option for drug-resistant epilepsy. In randomised controlled trials, VNS implantation has resulted in over 50% reduction in seizure frequency in 26%–40% of patients within 1 year. Long-term uncontrolled studies suggest better responses to VNS over time; however, the assessment of other potential predictive factors has led to contradictory results. Although initially designed for managing focal seizures, its use has been extended to other forms of drug-resistant epilepsy. In this review, we discuss the evidence supporting the use of VNS, its impact on seizure frequency and quality of life, and common adverse effects of this therapy. We also include practical guidance for the approach to and the management of patients with VNS in situ.

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