Lacsogram: a new EEG tool to diagnose Alzheimer's disease

Pedro M. Rodrigues, Bruno C. Bispo, Carolina Garrett, Dilio Alves, João P. Teixeira, Diamantino Freitas

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

This work proposes the application of a new electroencephalogram (EEG) signal processing tool - the lacsogram - to characterize the Alzheimer's disease (AD) activity and to assist on its diagnosis at different stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). Statistical analyzes are performed to lacstral distances between conventional EEG subbands to find measures capable of discriminating AD in all stages and characterizing the AD activity in each electrode. Cepstral distances are used for comparison. Comparing all AD stages and Controls (C), the most important significances are the lacstral distances between subbands and (p=0.0014<0.05). The topographic maps show significant differences in parietal, temporal and frontal regions as AD progresses. Machine learning models with a leave-one-out cross-validation process are applied to lacstral/cepstral distances to develop an automatic method for diagnosing AD. The following classification accuracies are obtained with an artificial neural network: 95.55% for All vs All, 98.06% for C vs MCI, 95.99% for C vs ADM, 93.85% for MCI vs ADM-ADA. In C vs MCI, C vs ADM and MCI vs ADM-ADA, the proposed method outperforms the state-of-art methods by 5%, 1%, and 2%, respectively. In All vs All, it outperforms the state-of-art EEG and non-EEG methods by 6% and 2%, respectively. These results indicate that the proposed method represents an improvement in diagnosing AD.
Original languageEnglish
Article number9390181
Pages (from-to)3384-3395
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number9
DOIs
Publication statusPublished - 1 Sept 2021

Keywords

  • Alzheimer's disease
  • Artificial neural networks
  • Cepstrum
  • Diagnose
  • Diseases
  • Electroencephalography
  • Electronic mail
  • Feature extraction
  • Lacsogram
  • Mild-cognitive impairment
  • Proteins
  • Sensitivity
  • Tools

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