Abstract
Background: Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS).Methods: Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. Accuracy of seizure identification was tested in two scenarios: patients with seizures occurring following a period of chronic recording (scenario 1) and patients with seizures occurring immediately following implantation (scenario 2). The accuracy of the CNN in identifying RNS-recorded iEEG ictal patterns was evaluated against human neurophysiology expertise. Statistical performance was assessed via the area-under-precision-recall curve (AUPRC).Results: In scenario 1, the CNN achieved a maximum mean binary classification AUPRC of 0.84 ± 0.19 (95%CI, 0.72–0.93) and mean regression accuracy of 6.3 ± 1.0 s (95%CI, 4.3–8.5 s) at 30 seed samples. In scenario 2, maximum mean AUPRC was 0.80 ± 0.19 (95%CI, 0.68–0.91) and mean regression accuracy was 6.3 ± 0.9 s (95%CI, 4.8–8.3 s) at 20 seed samples. We obtained near-maximum accuracies at seed size of 10 in both scenarios. CNN classification failures can be explained by ictal electro-decrements, brief seizures, single-channel ictal patterns, highly concentrated interictal activity, changes in the sleep-wake cycle, and progressive modulation of electrographic ictal features.Conclusions: We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient's seizures.
Highlights
Since its clinical establishment in the early twentieth century, intracranial electroencephalography has become the fundamental modality for evaluation and subsequent management in epilepsy surgery [1,2,3,4]
Recorded either with the use of subdural electrodes [5] or stereotactic electroencephalography [6], the intracranial electroencephalography (iEEG) allows for localization of the epileptogenic zone or the epileptogenic network giving rise to seizures [7, 8]
The development of reliable automated seizure detection methods is urgently needed to support routine clinical evaluation of responsive neurostimulation system (RNS) patients, as well as to facilitate analytics for personalized treatment [57]. Our study addresses this need and evaluates the efficiency of deep learning methodology in detecting iEEG ictal patterns using a large RNS-derived dataset of ictal patterns
Summary
Since its clinical establishment in the early twentieth century, intracranial electroencephalography (iEEG) has become the fundamental modality for evaluation and subsequent management in epilepsy surgery [1,2,3,4]. CNNs are artificial neural networks with multiple consecutive layers that perform convolutions in a hierarchical fashion [13, 14]. They are considered to be the deep learning model of choice in applications that require processing of multiple array data, as they can successfully identify local conjunctions in data and build high-level features from lowlevel ones [15]. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS)
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