  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.
