Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.

ABSTRACT: Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack of objective pain biomarkers. The present study applies machine learning to predict which patients will respond to SCS based on intraoperative electroencephalogram (EEG) data and recognized outcome measures. The study included 20 chronic pain patients who were undergoing SCS surgery. During intraoperative monitoring, EEG signals were recorded under SCS OFF (baseline) and ON conditions, including tonic and high density (HD) stimulation. Once spectral EEG features were extracted during offline analysis, principal component analysis (PCA) and a recursive feature elimination approach were used for feature selection. A subset of EEG features, clin

SUBMITTER: Gopal J 

PROVIDER: S-EPMC11920397 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature