A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification

Awais Abbas; Saadullah Farooq Abbasi; Muhammad Zulfiqar Ali; Saleem Shahid; Wei Chen

doi:10.1007/978-3-031-36258-3_30

A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification

Awais Abbas
, Saadullah Farooq Abbasi
, Muhammad Zulfiqar Ali
, Saleem Shahid^*
, Wei Chen

^*Corresponding author for this work

Electronic, Electrical and Systems Engineering

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

Abstract

Sleep is categorized as an arrangement of modifications occurring in our body inside our brain, muscles, working its way through our eyes (occipital lobe), respiratory along with cardiac activity. It makes the human body fresh and ready for the next day. In neonates, it is essential for brain and physical development. Polysomnography is the gold standard for determining and classification of sleep stages. However, it is expensive and requires human intervention. Therefore, over the past two decades, researchers proposed multiple algorithms for automatic neonatal sleep stage classification. All the previous studies used multichannel EEG recordings for classification. Not every intensive care unit contains a multichannel EEG extraction device. For this reason, a single channel automatic neonatal sleep-wake classification algorithm, using a support vector machine, has been proposed in this paper. 3525 30-s training and testing were used to train and test the network. The proposed algorithm can reach sleep-wake classification accuracy of 77.5% with mean kappa 0.55 using single channel EEG. The results were extracted using five-fold cross-validation and the mean has been reported in this paper. Experimental results and statistical analysis show that single channel EEG can be used for neonatal sleep classification with notable accuracy.

Original language	English
Title of host publication	Advances on Intelligent Computing and Data Science
Subtitle of host publication	Big Data Analytics, Intelligent Informatics, Smart Computing, Internet of Things
Editors	Faisal Saeed, Fathey Mohammed, Errais Mohammed, Tawfik Al-Hadhrami, Mohammed Al-Sarem
Publisher	Springer
Pages	345-352
Number of pages	8
Edition	1
ISBN (Electronic)	9783031362583
ISBN (Print)	9783031362576
DOIs	https://doi.org/10.1007/978-3-031-36258-3_30
Publication status	Published - 17 Aug 2023
Event	3rd International conference of Advanced Computing and Informatics - Morocco, Casablanca, Morocco Duration: 15 Oct 2022 → 16 Oct 2022 Conference number: 3 https://www.icaci-conf.com/ICACIN22/

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer
Volume	179
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

Conference

Conference	3rd International conference of Advanced Computing and Informatics
Abbreviated title	ICACIN
Country/Territory	Morocco
City	Casablanca
Period	15/10/22 → 16/10/22
Internet address	https://www.icaci-conf.com/ICACIN22/

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Electroencephalography
Neonatal
Sleep
Support vector machine

ASJC Scopus subject areas

Information Systems
Media Technology
Computer Science Applications
Computer Networks and Communications
Electrical and Electronic Engineering

Access to Document

10.1007/978-3-031-36258-3_30

Cite this

Abbas, A., Abbasi, S. F., Ali, M. Z., Shahid, S., & Chen, W. (2023). A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification. In F. Saeed, F. Mohammed, E. Mohammed, T. Al-Hadhrami, & M. Al-Sarem (Eds.), Advances on Intelligent Computing and Data Science: Big Data Analytics, Intelligent Informatics, Smart Computing, Internet of Things (1 ed., pp. 345-352). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 179). Springer. https://doi.org/10.1007/978-3-031-36258-3_30

Abbas, Awais ; Abbasi, Saadullah Farooq ; Ali, Muhammad Zulfiqar et al. / A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification. Advances on Intelligent Computing and Data Science: Big Data Analytics, Intelligent Informatics, Smart Computing, Internet of Things. editor / Faisal Saeed ; Fathey Mohammed ; Errais Mohammed ; Tawfik Al-Hadhrami ; Mohammed Al-Sarem. 1. ed. Springer, 2023. pp. 345-352 (Lecture Notes on Data Engineering and Communications Technologies).

@inproceedings{02ca36c418504c5eaf0d3ce87cb8b342,

title = "A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification",

abstract = "Sleep is categorized as an arrangement of modifications occurring in our body inside our brain, muscles, working its way through our eyes (occipital lobe), respiratory along with cardiac activity. It makes the human body fresh and ready for the next day. In neonates, it is essential for brain and physical development. Polysomnography is the gold standard for determining and classification of sleep stages. However, it is expensive and requires human intervention. Therefore, over the past two decades, researchers proposed multiple algorithms for automatic neonatal sleep stage classification. All the previous studies used multichannel EEG recordings for classification. Not every intensive care unit contains a multichannel EEG extraction device. For this reason, a single channel automatic neonatal sleep-wake classification algorithm, using a support vector machine, has been proposed in this paper. 3525 30-s training and testing were used to train and test the network. The proposed algorithm can reach sleep-wake classification accuracy of 77.5\% with mean kappa 0.55 using single channel EEG. The results were extracted using five-fold cross-validation and the mean has been reported in this paper. Experimental results and statistical analysis show that single channel EEG can be used for neonatal sleep classification with notable accuracy.",

keywords = "Electroencephalography, Neonatal, Sleep, Support vector machine",

author = "Awais Abbas and Abbasi, \{Saadullah Farooq\} and Ali, \{Muhammad Zulfiqar\} and Saleem Shahid and Wei Chen",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 3rd International conference of Advanced Computing and Informatics, ICACIN ; Conference date: 15-10-2022 Through 16-10-2022",

year = "2023",

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language = "English",

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series = "Lecture Notes on Data Engineering and Communications Technologies",

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editor = "Faisal Saeed and Fathey Mohammed and Errais Mohammed and Tawfik Al-Hadhrami and Mohammed Al-Sarem",

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}

Abbas, A, Abbasi, SF, Ali, MZ, Shahid, S & Chen, W 2023, A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification. in F Saeed, F Mohammed, E Mohammed, T Al-Hadhrami & M Al-Sarem (eds), Advances on Intelligent Computing and Data Science: Big Data Analytics, Intelligent Informatics, Smart Computing, Internet of Things. 1 edn, Lecture Notes on Data Engineering and Communications Technologies, vol. 179, Springer, pp. 345-352, 3rd International conference of Advanced Computing and Informatics, Casablanca, Morocco, 15/10/22. https://doi.org/10.1007/978-3-031-36258-3_30

A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification. / Abbas, Awais; Abbasi, Saadullah Farooq; Ali, Muhammad Zulfiqar et al.
Advances on Intelligent Computing and Data Science: Big Data Analytics, Intelligent Informatics, Smart Computing, Internet of Things. ed. / Faisal Saeed; Fathey Mohammed; Errais Mohammed; Tawfik Al-Hadhrami; Mohammed Al-Sarem. 1. ed. Springer, 2023. p. 345-352 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 179).

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

TY - GEN

T1 - A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification

AU - Abbas, Awais

AU - Abbasi, Saadullah Farooq

AU - Ali, Muhammad Zulfiqar

AU - Shahid, Saleem

AU - Chen, Wei

N1 - Conference code: 3

PY - 2023/8/17

Y1 - 2023/8/17

N2 - Sleep is categorized as an arrangement of modifications occurring in our body inside our brain, muscles, working its way through our eyes (occipital lobe), respiratory along with cardiac activity. It makes the human body fresh and ready for the next day. In neonates, it is essential for brain and physical development. Polysomnography is the gold standard for determining and classification of sleep stages. However, it is expensive and requires human intervention. Therefore, over the past two decades, researchers proposed multiple algorithms for automatic neonatal sleep stage classification. All the previous studies used multichannel EEG recordings for classification. Not every intensive care unit contains a multichannel EEG extraction device. For this reason, a single channel automatic neonatal sleep-wake classification algorithm, using a support vector machine, has been proposed in this paper. 3525 30-s training and testing were used to train and test the network. The proposed algorithm can reach sleep-wake classification accuracy of 77.5% with mean kappa 0.55 using single channel EEG. The results were extracted using five-fold cross-validation and the mean has been reported in this paper. Experimental results and statistical analysis show that single channel EEG can be used for neonatal sleep classification with notable accuracy.

AB - Sleep is categorized as an arrangement of modifications occurring in our body inside our brain, muscles, working its way through our eyes (occipital lobe), respiratory along with cardiac activity. It makes the human body fresh and ready for the next day. In neonates, it is essential for brain and physical development. Polysomnography is the gold standard for determining and classification of sleep stages. However, it is expensive and requires human intervention. Therefore, over the past two decades, researchers proposed multiple algorithms for automatic neonatal sleep stage classification. All the previous studies used multichannel EEG recordings for classification. Not every intensive care unit contains a multichannel EEG extraction device. For this reason, a single channel automatic neonatal sleep-wake classification algorithm, using a support vector machine, has been proposed in this paper. 3525 30-s training and testing were used to train and test the network. The proposed algorithm can reach sleep-wake classification accuracy of 77.5% with mean kappa 0.55 using single channel EEG. The results were extracted using five-fold cross-validation and the mean has been reported in this paper. Experimental results and statistical analysis show that single channel EEG can be used for neonatal sleep classification with notable accuracy.

KW - Electroencephalography

KW - Neonatal

KW - Sleep

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/85171165031

U2 - 10.1007/978-3-031-36258-3_30

DO - 10.1007/978-3-031-36258-3_30

M3 - Conference contribution

AN - SCOPUS:85171165031

SN - 9783031362576

T3 - Lecture Notes on Data Engineering and Communications Technologies

SP - 345

EP - 352

BT - Advances on Intelligent Computing and Data Science

A2 - Saeed, Faisal

A2 - Mohammed, Fathey

A2 - Mohammed, Errais

A2 - Al-Hadhrami, Tawfik

A2 - Al-Sarem, Mohammed

PB - Springer

T2 - 3rd International conference of Advanced Computing and Informatics

Y2 - 15 October 2022 through 16 October 2022

ER -

Abbas A, Abbasi SF, Ali MZ, Shahid S, Chen W. A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification. In Saeed F, Mohammed F, Mohammed E, Al-Hadhrami T, Al-Sarem M, editors, Advances on Intelligent Computing and Data Science: Big Data Analytics, Intelligent Informatics, Smart Computing, Internet of Things. 1 ed. Springer. 2023. p. 345-352. (Lecture Notes on Data Engineering and Communications Technologies). Epub 2023 Aug 16. doi: 10.1007/978-3-031-36258-3_30

A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this