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. 2018 Mar 20;13(3):e0193607.
doi: 10.1371/journal.pone.0193607. eCollection 2018.

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework

Nesma Houmani  1 , François Vialatte  2   3 , Esteve Gallego-Jutglà  4 , Gérard Dreyfus  3 , Vi-Huong Nguyen-Michel  5 , Jean Mariani  5   6   7 , Kiyoka Kinugawa  5   6   7
Affiliations

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework

Nesma Houmani et al. PLoS One. .

Abstract

This study addresses the problem of Alzheimer's disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer's disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Illustration of multi-channel (N = 4, D = 2) EEG signal modeling with HMM.
Fig 2
Fig 2. Box plots of the best features discriminating SCI patients from AD patients.
The figures follow the ranking in order of decreasing relevance: (a) EpEn on all electrodes [8–12] Hz; (b) EpEn on temporal region [8–30] Hz; (c) BM on Frontal region [4–8] Hz; (d) EpEn on all electrodes [8–30] Hz; (e) EpEn on frontal + occipital region [8–30] Hz.
Fig 3
Fig 3. Box plots of the most relevant features for discriminating possible AD patients from “Other” patients (patients with MCI or other pathologies).
Figures follow the same order given by the OFR algorithm as noted in Table 3: (a) EpEn on Temporal region [8–30] Hz; (b) EpEn on all electrodes [8–30] Hz; (c) EpEn on Frontal + Occipital region [8–30] Hz; (d) EpEn on all electrodes [12–30] Hz; (e) BM on Temporal region [12–30] Hz; (f) EpEn on all electrodes [8–12] Hz; (g) BM on Frontal region [12–30] Hz.
See this image and copyright information in PMC

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