Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification

TL;DR

This study introduces an automatic method to quantify TMS-EEG perturbation durations as potential biomarkers for Alzheimer's Disease, demonstrating promising classification accuracy between AD patients and healthy controls.

Contribution

The paper proposes three novel TMS-EEG metrics for assessing brain perturbation and applies machine learning for AD classification, advancing biomarker development.

Findings

Achieved 69.32% accuracy in AD classification

Identified TMS-EEG metrics as potential biomarkers

Demonstrated feasibility of automated EEG analysis for AD

Abstract

Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this study, we propose an automatic method of determining the duration of TMS induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations. A preliminary study is conducted in patients with Alzheimer's disease (AD). Three metrics for characterizing the strength and duration of TMS evoked EEG (TEP) activity are proposed and their potential in identifying AD patients from healthy controls was investigated. A dataset of TMS-EEG recordings from 17 AD and 17 healthy controls (HC) was used in our analysis. A Random Forest classification algorithm was trained on the extracted TEP metrics and its performance is evaluated in a leave-one-subject-out cross-validation. The created model showed promising results in identifying AD patients from HC with an accuracy, sensitivity and specificity of 69.32%, 72.23% and 66.41%, respectively.