An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
This work addresses the problem of improving epilepsy surgery outcomes by enabling real-time, on-chip HFO detection for clinicians, representing a novel feasibility study rather than an incremental advance.
The authors tackled real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG using a neuromorphic system, achieving state-of-the-art accuracy of 78%, specificity of 100%, and sensitivity of 33% for predicting postsurgical seizure outcomes with low power consumption of 614.3 μW.
In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO), which are biomarkers for the epileptogenic zone. The device was fabricated using a standard 0.18$μ$m CMOS technology node and has a total area of 99mm$^{2}$. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3$μ$W. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing "neuromorphic intelligence" to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.