Abstract
The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECoG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm obtained an accuracy of 83.05% in identifying language regions, an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.
Highlights
Epilepsy is a chronic neurological disorder characterized by recurrent, unpredictable seizures with over 65 million reported cases around the world (Patricia, 2018)
Our results have indicated that with the proposed deep learning-based classification model, ECoG-FM can be reliably used as a standalone technique for functional language mapping
After we combined the learned features, we trained a recurrent neural network (RNN), a class of deep learning algorithm suited for analyzing sequential data, to classify sub-blocks of signals (Step 4)
Summary
Epilepsy is a chronic neurological disorder characterized by recurrent, unpredictable seizures with over 65 million reported cases around the world (Patricia, 2018). Deep Learning in ECoG-Based Studies for drug-resistant epilepsy, neurosurgeons need to avoid removing tissues associated with language, sensory, and motor functions. This calls for an accurate identification and localization of these functionally significant brain regions. The surgical procedure can be performed more accurately with a precise localization for preventing any corresponding post-surgical neurological/functional deficits Toward this end, this study is aimed toward developing an innovative approach for the mapping of language cortex. This study is aimed toward developing an innovative approach for the mapping of language cortex This would ensure an improved and sustainable post-surgical quality of life for patients presented for brain surgery
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