Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification
This work addresses the need for automated diagnostic tools in Alzheimer's Disease, but it is incremental as it builds on existing TMS-EEG methods with a small dataset.
The authors tackled the problem of classifying Alzheimer's Disease (AD) patients from healthy controls using TMS-EEG perturbation indexes based on signal energy, achieving an accuracy of 69.32%, sensitivity of 72.23%, and specificity of 66.41% in a preliminary study.
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.