Automatic neonatal sleep stage classification: A comparative study

Saadullah Farooq Abbasi; Awais Abbas; Iftikhar Ahmad; Mohammed S. Alshehri; Sultan Almakdi; Yazeed Yasin Ghadi; Jawad Ahmad

doi:10.1016/j.heliyon.2023.e22195

Automatic neonatal sleep stage classification: A comparative study

Saadullah Farooq Abbasi^*, Awais Abbas, Iftikhar Ahmad, Mohammed S. Alshehri, Sultan Almakdi, Yazeed Yasin Ghadi, Jawad Ahmad

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

81 Downloads (Pure)

Abstract

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.

Original language	English
Article number	e22195
Number of pages	8
Journal	Heliyon
Volume	9
Issue number	11
DOIs	https://doi.org/10.1016/j.heliyon.2023.e22195
Publication status	Published - 13 Nov 2023

Bibliographical note

Funding statement:
The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the research groups funding program grant code (NU/RG/SERC/12/3).

Keywords

Neonatal sleep staging
Polysomnography
Classification
Electroencephalography

Access to Document

10.1016/j.heliyon.2023.e22195Licence: Creative Commons: Attribution (CC BY)

AbbasiS2023AutomaticFinal published version, 649 KBLicence: Creative Commons: Attribution (CC BY)

Cite this

@article{ab2a10ef8220431894957a8a4f2932b0,

title = "Automatic neonatal sleep stage classification: A comparative study",

abstract = "Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.",

keywords = "Neonatal sleep staging, Polysomnography, Classification, Electroencephalography",

author = "Abbasi, {Saadullah Farooq} and Awais Abbas and Iftikhar Ahmad and Alshehri, {Mohammed S.} and Sultan Almakdi and Ghadi, {Yazeed Yasin} and Jawad Ahmad",

note = "Funding statement: The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the research groups funding program grant code (NU/RG/SERC/12/3).",

year = "2023",

month = nov,

day = "13",

doi = "10.1016/j.heliyon.2023.e22195",

language = "English",

volume = "9",

journal = "Heliyon",

issn = "2405-8440",

publisher = "Elsevier",

number = "11",

}

TY - JOUR

T1 - Automatic neonatal sleep stage classification

T2 - A comparative study

AU - Abbasi, Saadullah Farooq

AU - Abbas, Awais

AU - Ahmad, Iftikhar

AU - Alshehri, Mohammed S.

AU - Almakdi, Sultan

AU - Ghadi, Yazeed Yasin

AU - Ahmad, Jawad

N1 - Funding statement: The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the research groups funding program grant code (NU/RG/SERC/12/3).

PY - 2023/11/13

Y1 - 2023/11/13

N2 - Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.

AB - Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.

KW - Neonatal sleep staging

KW - Polysomnography

KW - Classification

KW - Electroencephalography

U2 - 10.1016/j.heliyon.2023.e22195

DO - 10.1016/j.heliyon.2023.e22195

M3 - Review article

SN - 2405-8440

VL - 9

JO - Heliyon

JF - Heliyon

IS - 11

M1 - e22195

ER -

Automatic neonatal sleep stage classification: A comparative study

Abstract

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this