A Patient-Specific Machine Learning based EEG Processor for Accurate Estimation of Depth of Anesthesia

Abstract

An electroencephalograph (EEG) based classification processor for the depth of Anesthesia (DoA) during the intraoperative procedure is presented. To enable a DoA to monitor the correct estimation across a range of patients, a novel feature extraction along with machine learning processor is utilized. The decisions are solely based on seven features extracted from EEG along with the EMG signal for motion artifacts rejection. To extract the features efficiently on hardware, a 128-point FFT is proposed that achieves an area reduction and energy/FFT-operation by 39% and 58%, respectively, compared to the conventional. A simple decision tree is used to perform a multiclass DoA classification. The system is synthesized using a 65nm process and experimental verification is done using FPGA based on the subset of patients from the University of Queensland Vital Signs. The proposed patient-specific DoA classification processor achieves a classification accuracy of 79%.