Prediction of spine decompression post-surgery outcome through transcranial motor evoked potential using linear discriminant analysis algorithm

Abstract

Transcranial motor evoked potential (TcMEP) is one of the modalities in intraoperative neuromonitoring (IONM) which has been used in spine surgeries to prevent motor function injuries. Studies have shown that improvement to TcMEP could be a potential prognostic information on the actual improvement to the patient after surgery. There is no objective way currently to identify which TcMEP signal is significant to indicate actual positive relief of symptoms. The proposed method utilized linear discriminant analysis (LDA) machine learning algorithm to predict the TcMEP response that correlates to relieve of symptoms post-surgery. TcMEP data were obtained from four patients that had pre surgery symptoms with post-surgery actual relief of symptoms, and six patients that had no pre surgery and post-surgery symptoms which were divided into training and prediction test. The result of the proposed method produced 87.5% of accuracy in prediction capabilities.