A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia

Cosimo Ieracitano; Nadia Mammone; Amir Hussain; Francesco Carlo Morabito

doi:10.1109/IJCNN48605.2020.9207167

A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia

Cosimo Ieracitano
, Nadia Mammone
, Amir Hussain
, Francesco Carlo Morabito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

29 Scopus citations

Abstract

In this paper, a novel deep learning based approach is proposed for the automatic classification of Electroencephalographic (EEG) signals of subjects diagnosed with the dementia of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Specifically, a custom Convolutional Neural Network (CNN) is designed to receive as input AD/MCI/HC EEG segments (epochs) of the same temporal width, and perform 2-way classification tasks: AD vs. HC, AD vs. MCI, MCI vs. HC. Our proposed architecture, termed EEG-CNN, is shown to exhibit remarkable abilities to self-learn relevant features directly from the EEG traces, avoiding the need for hand-crafted feature extraction engineering. Comparative experimental results demonstrate the promising performance of EEG-CNN, which is based on an analysis of the EEG time series only, reporting accuracies of 85.78 ± 2.18%, 69.03 ± 1.33%, 85.34 ± 1.86% in AD vs. HC, AD vs. MCI and MCI vs. HC classifications, respectively.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169262
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9207167
State	Published - Jul 2020
Externally published	Yes
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	19/07/20 → 24/07/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Alzheimer's disease
Convolutional Neural Network
Deep Learning
EEG signal
Mild Cognitive Impairment
Self-learning

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN48605.2020.9207167

Cite this

Ieracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (2020). A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9207167 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN48605.2020.9207167

Ieracitano, Cosimo ; Mammone, Nadia ; Hussain, Amir et al. / A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia. 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the International Joint Conference on Neural Networks).

@inproceedings{d73ebd57fc924024b8616c09bb4c118f,

title = "A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia",

abstract = "In this paper, a novel deep learning based approach is proposed for the automatic classification of Electroencephalographic (EEG) signals of subjects diagnosed with the dementia of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Specifically, a custom Convolutional Neural Network (CNN) is designed to receive as input AD/MCI/HC EEG segments (epochs) of the same temporal width, and perform 2-way classification tasks: AD vs. HC, AD vs. MCI, MCI vs. HC. Our proposed architecture, termed EEG-CNN, is shown to exhibit remarkable abilities to self-learn relevant features directly from the EEG traces, avoiding the need for hand-crafted feature extraction engineering. Comparative experimental results demonstrate the promising performance of EEG-CNN, which is based on an analysis of the EEG time series only, reporting accuracies of 85.78 ± 2.18\%, 69.03 ± 1.33\%, 85.34 ± 1.86\% in AD vs. HC, AD vs. MCI and MCI vs. HC classifications, respectively.",

keywords = "Alzheimer's disease, Convolutional Neural Network, Deep Learning, EEG signal, Mild Cognitive Impairment, Self-learning",

author = "Cosimo Ieracitano and Nadia Mammone and Amir Hussain and Morabito, \{Francesco Carlo\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/IJCNN48605.2020.9207167",

language = "English",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Ieracitano, C, Mammone, N, Hussain, A & Morabito, FC 2020, A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9207167, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207167

A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia. / Ieracitano, Cosimo; Mammone, Nadia; Hussain, Amir et al.
2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9207167 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Mammone, Nadia

AU - Hussain, Amir

AU - Morabito, Francesco Carlo

PY - 2020/7

Y1 - 2020/7

N2 - In this paper, a novel deep learning based approach is proposed for the automatic classification of Electroencephalographic (EEG) signals of subjects diagnosed with the dementia of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Specifically, a custom Convolutional Neural Network (CNN) is designed to receive as input AD/MCI/HC EEG segments (epochs) of the same temporal width, and perform 2-way classification tasks: AD vs. HC, AD vs. MCI, MCI vs. HC. Our proposed architecture, termed EEG-CNN, is shown to exhibit remarkable abilities to self-learn relevant features directly from the EEG traces, avoiding the need for hand-crafted feature extraction engineering. Comparative experimental results demonstrate the promising performance of EEG-CNN, which is based on an analysis of the EEG time series only, reporting accuracies of 85.78 ± 2.18%, 69.03 ± 1.33%, 85.34 ± 1.86% in AD vs. HC, AD vs. MCI and MCI vs. HC classifications, respectively.

AB - In this paper, a novel deep learning based approach is proposed for the automatic classification of Electroencephalographic (EEG) signals of subjects diagnosed with the dementia of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Specifically, a custom Convolutional Neural Network (CNN) is designed to receive as input AD/MCI/HC EEG segments (epochs) of the same temporal width, and perform 2-way classification tasks: AD vs. HC, AD vs. MCI, MCI vs. HC. Our proposed architecture, termed EEG-CNN, is shown to exhibit remarkable abilities to self-learn relevant features directly from the EEG traces, avoiding the need for hand-crafted feature extraction engineering. Comparative experimental results demonstrate the promising performance of EEG-CNN, which is based on an analysis of the EEG time series only, reporting accuracies of 85.78 ± 2.18%, 69.03 ± 1.33%, 85.34 ± 1.86% in AD vs. HC, AD vs. MCI and MCI vs. HC classifications, respectively.

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KW - Mild Cognitive Impairment

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M3 - Conference contribution

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BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

Ieracitano C, Mammone N, Hussain A, Morabito FC. A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9207167. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN48605.2020.9207167

A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this