Anomaly detection in electroencephalography signal using deep learning model

Sharaban Tahura; S. M. Hasnat Samiul; M. Shamim Kaiser; Mufti Mahmud

doi:10.1007/978-981-33-4673-4_18

Anomaly detection in electroencephalography signal using deep learning model

Sharaban Tahura
, S. M. Hasnat Samiul
, M. Shamim Kaiser^*
, Mufti Mahmud

^*Corresponding author for this work

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

27 Scopus citations

Abstract

Biosignals such as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG) represent the electrical activities of various parts of human body. Various low cost non-invasive bio-sensors measures bio-signals and assist medical practitioner to monitor physiological conditions of a human health and identify associated risk. The volume bio-signals is a big data and can not be analyzed and identify anomaly manually, therefore intelligent algorithms have been proposed to detect personalized anomaly in real time data. This paper presents a review on Deep Learning (DL) based anomaly detection techniques in EEG. The convolutional neural network, recurrent neural network and autoencoder based DL algorithms are considered. Here EEG signal acquisition, feature extracting techniques and key-anomaly features and corresponding performance of the various techniques found in the literature are also discussed. The challenges and open research questions are outlined at the end of the article.

Original language	English
Title of host publication	Proceedings of International Conference on Trends in Computational and Cognitive Engineering - Proceedings of TCCE 2020
Editors	M. Shamim Kaiser, Anirban Bandyopadhyay, Mufti Mahmud, Kanad Ray
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	205-217
Number of pages	13
ISBN (Print)	9789813346727
DOIs	https://doi.org/10.1007/978-981-33-4673-4_18
State	Published - 2021
Externally published	Yes
Event	2nd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2020 - Savar, Bangladesh Duration: 17 Dec 2020 → 18 Dec 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1309
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	2nd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2020
Country/Territory	Bangladesh
City	Savar
Period	17/12/20 → 18/12/20

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Autoencoder
Convolutional neural network
Machine learning
Prediction
Recurrent neural network

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-981-33-4673-4_18

Cite this

Tahura, S., Hasnat Samiul, S. M., Shamim Kaiser, M., & Mahmud, M. (2021). Anomaly detection in electroencephalography signal using deep learning model. In M. S. Kaiser, A. Bandyopadhyay, M. Mahmud, & K. Ray (Eds.), Proceedings of International Conference on Trends in Computational and Cognitive Engineering - Proceedings of TCCE 2020 (pp. 205-217). (Advances in Intelligent Systems and Computing; Vol. 1309). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-33-4673-4_18

Tahura, Sharaban ; Hasnat Samiul, S. M. ; Shamim Kaiser, M. et al. / Anomaly detection in electroencephalography signal using deep learning model. Proceedings of International Conference on Trends in Computational and Cognitive Engineering - Proceedings of TCCE 2020. editor / M. Shamim Kaiser ; Anirban Bandyopadhyay ; Mufti Mahmud ; Kanad Ray. Springer Science and Business Media Deutschland GmbH, 2021. pp. 205-217 (Advances in Intelligent Systems and Computing).

@inproceedings{f891632496d448fc9eee8b68560282af,

title = "Anomaly detection in electroencephalography signal using deep learning model",

abstract = "Biosignals such as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG) represent the electrical activities of various parts of human body. Various low cost non-invasive bio-sensors measures bio-signals and assist medical practitioner to monitor physiological conditions of a human health and identify associated risk. The volume bio-signals is a big data and can not be analyzed and identify anomaly manually, therefore intelligent algorithms have been proposed to detect personalized anomaly in real time data. This paper presents a review on Deep Learning (DL) based anomaly detection techniques in EEG. The convolutional neural network, recurrent neural network and autoencoder based DL algorithms are considered. Here EEG signal acquisition, feature extracting techniques and key-anomaly features and corresponding performance of the various techniques found in the literature are also discussed. The challenges and open research questions are outlined at the end of the article.",

keywords = "Autoencoder, Convolutional neural network, Machine learning, Prediction, Recurrent neural network",

author = "Sharaban Tahura and \{Hasnat Samiul\}, \{S. M.\} and \{Shamim Kaiser\}, M. and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 2nd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2020 ; Conference date: 17-12-2020 Through 18-12-2020",

year = "2021",

doi = "10.1007/978-981-33-4673-4\_18",

language = "English",

isbn = "9789813346727",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Science and Business Media Deutschland GmbH",

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address = "Germany",

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Tahura, S, Hasnat Samiul, SM, Shamim Kaiser, M & Mahmud, M 2021, Anomaly detection in electroencephalography signal using deep learning model. in MS Kaiser, A Bandyopadhyay, M Mahmud & K Ray (eds), Proceedings of International Conference on Trends in Computational and Cognitive Engineering - Proceedings of TCCE 2020. Advances in Intelligent Systems and Computing, vol. 1309, Springer Science and Business Media Deutschland GmbH, pp. 205-217, 2nd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2020, Savar, Bangladesh, 17/12/20. https://doi.org/10.1007/978-981-33-4673-4_18

Anomaly detection in electroencephalography signal using deep learning model. / Tahura, Sharaban; Hasnat Samiul, S. M.; Shamim Kaiser, M. et al.
Proceedings of International Conference on Trends in Computational and Cognitive Engineering - Proceedings of TCCE 2020. ed. / M. Shamim Kaiser; Anirban Bandyopadhyay; Mufti Mahmud; Kanad Ray. Springer Science and Business Media Deutschland GmbH, 2021. p. 205-217 (Advances in Intelligent Systems and Computing; Vol. 1309).

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

TY - GEN

T1 - Anomaly detection in electroencephalography signal using deep learning model

AU - Tahura, Sharaban

AU - Hasnat Samiul, S. M.

AU - Shamim Kaiser, M.

AU - Mahmud, Mufti

PY - 2021

Y1 - 2021

N2 - Biosignals such as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG) represent the electrical activities of various parts of human body. Various low cost non-invasive bio-sensors measures bio-signals and assist medical practitioner to monitor physiological conditions of a human health and identify associated risk. The volume bio-signals is a big data and can not be analyzed and identify anomaly manually, therefore intelligent algorithms have been proposed to detect personalized anomaly in real time data. This paper presents a review on Deep Learning (DL) based anomaly detection techniques in EEG. The convolutional neural network, recurrent neural network and autoencoder based DL algorithms are considered. Here EEG signal acquisition, feature extracting techniques and key-anomaly features and corresponding performance of the various techniques found in the literature are also discussed. The challenges and open research questions are outlined at the end of the article.

AB - Biosignals such as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG) represent the electrical activities of various parts of human body. Various low cost non-invasive bio-sensors measures bio-signals and assist medical practitioner to monitor physiological conditions of a human health and identify associated risk. The volume bio-signals is a big data and can not be analyzed and identify anomaly manually, therefore intelligent algorithms have been proposed to detect personalized anomaly in real time data. This paper presents a review on Deep Learning (DL) based anomaly detection techniques in EEG. The convolutional neural network, recurrent neural network and autoencoder based DL algorithms are considered. Here EEG signal acquisition, feature extracting techniques and key-anomaly features and corresponding performance of the various techniques found in the literature are also discussed. The challenges and open research questions are outlined at the end of the article.

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KW - Convolutional neural network

KW - Machine learning

KW - Prediction

KW - Recurrent neural network

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Y2 - 17 December 2020 through 18 December 2020

ER -

Tahura S, Hasnat Samiul SM, Shamim Kaiser M, Mahmud M. Anomaly detection in electroencephalography signal using deep learning model. In Kaiser MS, Bandyopadhyay A, Mahmud M, Ray K, editors, Proceedings of International Conference on Trends in Computational and Cognitive Engineering - Proceedings of TCCE 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 205-217. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-33-4673-4_18

Anomaly detection in electroencephalography signal using deep learning model

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this