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Electrical Engineering and Systems Science > Signal Processing

arXiv:2206.07492 (eess)
[Submitted on 19 May 2022 (v1), last revised 16 Jun 2022 (this version, v2)]

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
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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.
Comments: 4 pages, 4 figures, accepted to the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 11-15 July 2022, Glasgow, Scotland, UK
Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Cite as: arXiv:2206.07492 [eess.SP]
  (or arXiv:2206.07492v2 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2206.07492

Submission history

From: Alexandra Tautan [view email]
[v1] Thu, 19 May 2022 20:34:04 UTC (704 KB)
[v2] Thu, 16 Jun 2022 17:27:44 UTC (704 KB)
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