Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease

• URL: http://arxiv.org/abs/2206.07492v2
• Date: Thu, 16 Jun 2022 17:27:44 GMT
• Title: Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease
• Authors: Alexandra-Maria Tautan, Elias Casula, Ilaria Borghi, Michele Maiella, Sonia Bonni, Marilena Minei, Martina Assogna, Bogdan Ionescu, Giacomo Koch, Emiliano Santarnecchi
• Abstract summary: We use TMS-evoked EEG responses to classify Alzheimer's patients from healthy controls. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.2% respectively.
• Score: 48.42347515853289
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 88.2% respectively.

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