A Neuromorphic System Based on Spiking-Timing Dependent Plasticity for Evaluating Wakefulness and Anesthesia States using Intracranial EEG Signals

The primary method for assessing the depth of anesthesia during clinical surgeries currently relies on physiological and behavioral cues, such as heart rate, blood pressure, and reaction to external stimuli. These measures might not be dependable because their relationship to patients’ levels of awareness is not firmly established. Here, we present a neuromorphic framework comprising a Spiking Neural Network (SNN) with simulated neurons to access conscious states during general anesthesia, such as wakefulness and anesthesia, from intracranial electroencephalography (iEEG) and electroencephalogram (EEG) signals. This framework adjusts synapse weight of each neuron utilizing Spiking-Timing Dependent Plasticity (STDP) rules. Our analysis revealed that the proposed neuromorphic approach can access states of consciousness during anesthesia and characterize the transition from wakefulness to anesthetic-induced unconsciousness. We further implemented this framework on the Field Programmable Gate Array platform to enhance execution efficiency and address the clinical requirements for real-time monitoring of anesthesia states. Importantly, this study signifies an initial viable investigation using neuromorphic computing techniques for evaluating patients’ levels of anesthesia based on iEEG signals.