Combining EEG signal processing with supervised methods for Alzheimer's patients classification

Giulia Fiscon, Emanuel Weitschek, Alessio Cialini, Giovanni Felici, Paola Bertolazzi, Simona De Salvo, Alessia Bramanti, Placido Bramanti, Maria Cristina De Cola

Research output: Contribution to journalArticle

Abstract

BACKGROUND: Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms.

METHODS: In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods.

RESULTS: By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively.

CONCLUSIONS: Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.

Original languageEnglish
Pages (from-to)35
JournalBMC Medical Informatics and Decision Making
Volume18
Issue number1
DOIs
Publication statusPublished - May 31 2018

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Electroencephalography
Alzheimer Disease
Wavelet Analysis
Fourier Analysis
Dementia
Cognitive Dysfunction

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Combining EEG signal processing with supervised methods for Alzheimer's patients classification. / Fiscon, Giulia; Weitschek, Emanuel; Cialini, Alessio; Felici, Giovanni; Bertolazzi, Paola; De Salvo, Simona; Bramanti, Alessia; Bramanti, Placido; De Cola, Maria Cristina.

In: BMC Medical Informatics and Decision Making, Vol. 18, No. 1, 31.05.2018, p. 35.

Research output: Contribution to journalArticle

Fiscon, Giulia ; Weitschek, Emanuel ; Cialini, Alessio ; Felici, Giovanni ; Bertolazzi, Paola ; De Salvo, Simona ; Bramanti, Alessia ; Bramanti, Placido ; De Cola, Maria Cristina. / Combining EEG signal processing with supervised methods for Alzheimer's patients classification. In: BMC Medical Informatics and Decision Making. 2018 ; Vol. 18, No. 1. pp. 35.
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abstract = "BACKGROUND: Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms.METHODS: In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods.RESULTS: By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83{\%}, 92{\%}, and 79{\%} of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively.CONCLUSIONS: Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.",
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