Toward a Noninvasive Automatic Seizure Control System in Rats With Transcranial Focal Stimulations via Tripolar Concentric Ring Electrodes

Abstract

Epilepsy affects approximately one percent of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback.

Recent Advances in High-Frequency Oscillations and Seizure Onset Detection Using Laplacian Electroencephalography via Tripolar Concentric Ring Electrodes

“…An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13) with detection of electrographic seizure activity accomplished in advance of the early behavioral seizure activity in 76.92% of the rats [8].…”

“…Early CUSUM/GLRT based seizure onset detection methodologies proposed in [7,8] were based on detecting the changes in signal power since our previous results indicated a significant increase in tEEG power corresponding to seizure onset using population grand average power spectral density estimates [21] and frequency band analysis [7]. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13) with detection of electrographic seizure activity accomplished in advance of the early behavioral seizure activity in 76.92% of the rats [8].…”

“…Compared to EEG with conventional disc electrodes Laplacian EEG via TCREs (tEEG) has been shown to have significantly better spatial selectivity (approximately 2.5 times higher), signal-to-noise ratio (approximately 3.7 times higher), and mutual information (approximately 12 times lower) [4]. Because of its ability to attenuate common movement and muscle artifacts tEEG found numerous applications in a wide range of areas including brain-computer interface [5,6], seizure onset detection [7][8][9][10][11], detection of high-frequency oscillations (HFOs) and seizure onset zones [1,12], etc.…”

“…We have shown feasibility of recording HFOs with TCREs and their presence in tEEG prior to the onset of behavioral seizure manifestations in all our animal [12] and human epilepsy patient [1] seizure data. For seizure onset detection a progression of methodologies was proposed and validated including cumulative sum algorithm (CUSUM) [7] and generalized likelihood ratio test (GLRT) as well as disjunctive combination of the two [8] on pentylenetetrazole (PTZ) induced seizures in rats. Next an approach with three tEEG based features including median absolute deviation, approximate entropy, and maximum singular value serving as inputs to two classifiers including the support vector machines and adaptive boosting was validated on animal model tEEG and publicly available human epilepsy patient EEG data [9].…”

“…Next an approach with three tEEG based features including median absolute deviation, approximate entropy, and maximum singular value serving as inputs to two classifiers including the support vector machines and adaptive boosting was validated on animal model tEEG and publicly available human epilepsy patient EEG data [9]. Unlike all the previous work where data from a single TCRE was used for seizure onset detection [7][8][9], in our recent studies data from multiple TCREs was integrated using the exponentially embedded family (EEF) approach that has been proposed for multi-sensor (or multi-channel) detection [16,17]. Applied to hypothesis testing, EEF has been shown to have superior performance compared to existing methods for cases where sensor outputs are not independent [16,17].…”