The heart and brain are reciprocally interconnected and engage in two-way communication for homeostatic regulation. Epilepsy is considered a network disease that also affects the autonomic nervous system (ANS). The neurovisceral integration model (NVM) proposes that cardiac vagal tone, indexed by heart rate variability (HRV), can indicate the functional integrity of cognitive neural networks. ANS activity and the pattern of oscillatory EEG activity covary during the transition of arousal states and associations between cortical and autonomic activity are reflected by HRV. Cognitive dysfunction is one of the common comorbidities that occur in epilepsy, including memory, attention, and processing difficulties. Recent studies have shown evidence for the active involvement of alpha activity in cognitive processes through its active role in the control of neural excitability in the cortex through top-down modulation of cortical networks. In the present pilot study, we evaluated the association between resting EEG oscillatory behavior and ANS function in patients with refractory epilepsy. Our results show: (1) In patients with refractory epilepsy, there is a strong positive correlation between HRV and the power of cortical oscillatory cortical activity in all studied EEG bands (delta, theta, alpha, and beta) in all regions of interest in both hemispheres, the opposite pattern found in controls which had low or negative correlation between these variables; (2) higher heartbeat evoked potential amplitudes in patients with refractory epilepsy than in controls. Taken together, these results point to a significant alteration in heart-brain interaction in patients with refractory epilepsy.
We used machine learning tools to discriminate resting-state brain electrical activity measured with electroencephalography (EEG) of patients with refractory epilepsy (RE) from healthy controls (HC). We propose a crossspectral density-based measure as a signal feature to distinguish between healthy and epileptic subjects using machine-learning algorithms linear discriminant analysis (LDA) and support vector machines (SVM). The resting-state EEG of epileptic patients were obtained from interictal periods without any epileptiform activity. We recorded from 11 epilepsy patients and 7 healthy age-matched controls. Both algorithms obtained 100 % accuracy. Our results show that a distinction between the two groups is possible with high accuracy when a 190dimensional feature vector is used as input.
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