Preliminary Study on the Impact of EEG Density on TMS-EEG Classification in Alzheimer’s Disease

SOURCE: Annual International Conference Of The IEEE Engineering In Medicine And Biology Society. 2022:394-397, 2022 Jul.

AUTHORS: Tautan AM; Casula E; Borghi I; Maiella M; Bonni S; Minei M; Assogna M; Ionescu B; Koch G; Santarnecchi E

ABSTRACT: Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer’s disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.82% respectively.

CLINICAL RELEVANCE: TMS-EEG responses were successfully used to identify Alzheimer’s disease patients from healthy controls.