Detecting Sleep Disorders from NREM Using DeepSDBPLM

Haifa Almutairi; Ghulam Mubashar Hassan; Amitava Datta

doi:10.1007/978-981-99-9005-4_58

Detecting Sleep Disorders from NREM Using DeepSDBPLM

Haifa Almutairi, Ghulam Mubashar Hassan, Amitava Datta

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

Abstract

Sleep disorders have negative effects on human health. Sleep Disorder Breathing (SDB) and Periodic Leg Movement (PLM) are common sleep disorders that happen during sleep. Early detection of SDB and PLM from Non-Rapid Eye Movement (NREM) can protect patients from hypertension and cardiovascular diseases. In this study, we propose a novel deep learning architecture DeepSDBPLM for classifying Normal, SDB and PLM from NREM using Electroencephalogram (EEG) and Electromyogram (EMG) signals. Our proposed model is tested in three different classification problems using ISRUC-Sleep database. The results show that our proposed model achieves the best result of F1 score as compared to the state-of-the-art techniques.

Original language	English
Title of host publication	Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications
Editors	Nur Syazreen Ahmad, Junita Mohamad-Saleh, Jiashen Teh
Place of Publication	Singapore
Publisher	Springer Science + Business Media
Pages	459-467
Number of pages	9
ISBN (Electronic)	9789819990054
ISBN (Print)	9789819990047
DOIs	https://doi.org/10.1007/978-981-99-9005-4_58
Publication status	Published - 2024
Event	12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023 - Penang, Malaysia Duration: 28 Aug 2023 → 29 Aug 2023

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1123 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023
Country/Territory	Malaysia
City	Penang
Period	28/08/23 → 29/08/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-981-99-9005-4_58

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Almutairi, H., Hassan, G. M., & Datta, A. (2024). Detecting Sleep Disorders from NREM Using DeepSDBPLM. In N. S. Ahmad, J. Mohamad-Saleh, & J. Teh (Eds.), Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications (pp. 459-467). (Lecture Notes in Electrical Engineering; Vol. 1123 LNEE). Springer Science + Business Media. https://doi.org/10.1007/978-981-99-9005-4_58

Almutairi, Haifa ; Hassan, Ghulam Mubashar ; Datta, Amitava. / Detecting Sleep Disorders from NREM Using DeepSDBPLM. Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. editor / Nur Syazreen Ahmad ; Junita Mohamad-Saleh ; Jiashen Teh. Singapore : Springer Science + Business Media, 2024. pp. 459-467 (Lecture Notes in Electrical Engineering).

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title = "Detecting Sleep Disorders from NREM Using DeepSDBPLM",

abstract = "Sleep disorders have negative effects on human health. Sleep Disorder Breathing (SDB) and Periodic Leg Movement (PLM) are common sleep disorders that happen during sleep. Early detection of SDB and PLM from Non-Rapid Eye Movement (NREM) can protect patients from hypertension and cardiovascular diseases. In this study, we propose a novel deep learning architecture DeepSDBPLM for classifying Normal, SDB and PLM from NREM using Electroencephalogram (EEG) and Electromyogram (EMG) signals. Our proposed model is tested in three different classification problems using ISRUC-Sleep database. The results show that our proposed model achieves the best result of F1 score as compared to the state-of-the-art techniques.",

keywords = "Attention, Convolutional neural networks, EMD, Sleep disorder",

author = "Haifa Almutairi and Hassan, {Ghulam Mubashar} and Amitava Datta",

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language = "English",

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Almutairi, H, Hassan, GM & Datta, A 2024, Detecting Sleep Disorders from NREM Using DeepSDBPLM. in NS Ahmad, J Mohamad-Saleh & J Teh (eds), Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol. 1123 LNEE, Springer Science + Business Media, Singapore, pp. 459-467, 12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023, Penang, Malaysia, 28/08/23. https://doi.org/10.1007/978-981-99-9005-4_58

Detecting Sleep Disorders from NREM Using DeepSDBPLM. / Almutairi, Haifa; Hassan, Ghulam Mubashar ; Datta, Amitava.
Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. ed. / Nur Syazreen Ahmad; Junita Mohamad-Saleh; Jiashen Teh. Singapore: Springer Science + Business Media, 2024. p. 459-467 (Lecture Notes in Electrical Engineering; Vol. 1123 LNEE).

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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T1 - Detecting Sleep Disorders from NREM Using DeepSDBPLM

AU - Almutairi, Haifa

AU - Hassan, Ghulam Mubashar

AU - Datta, Amitava

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Sleep disorders have negative effects on human health. Sleep Disorder Breathing (SDB) and Periodic Leg Movement (PLM) are common sleep disorders that happen during sleep. Early detection of SDB and PLM from Non-Rapid Eye Movement (NREM) can protect patients from hypertension and cardiovascular diseases. In this study, we propose a novel deep learning architecture DeepSDBPLM for classifying Normal, SDB and PLM from NREM using Electroencephalogram (EEG) and Electromyogram (EMG) signals. Our proposed model is tested in three different classification problems using ISRUC-Sleep database. The results show that our proposed model achieves the best result of F1 score as compared to the state-of-the-art techniques.

AB - Sleep disorders have negative effects on human health. Sleep Disorder Breathing (SDB) and Periodic Leg Movement (PLM) are common sleep disorders that happen during sleep. Early detection of SDB and PLM from Non-Rapid Eye Movement (NREM) can protect patients from hypertension and cardiovascular diseases. In this study, we propose a novel deep learning architecture DeepSDBPLM for classifying Normal, SDB and PLM from NREM using Electroencephalogram (EEG) and Electromyogram (EMG) signals. Our proposed model is tested in three different classification problems using ISRUC-Sleep database. The results show that our proposed model achieves the best result of F1 score as compared to the state-of-the-art techniques.

KW - Attention

KW - Convolutional neural networks

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T3 - Lecture Notes in Electrical Engineering

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BT - Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications

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A2 - Mohamad-Saleh, Junita

A2 - Teh, Jiashen

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Y2 - 28 August 2023 through 29 August 2023

ER -

Almutairi H, Hassan GM , Datta A. Detecting Sleep Disorders from NREM Using DeepSDBPLM. In Ahmad NS, Mohamad-Saleh J, Teh J, editors, Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. Singapore: Springer Science + Business Media. 2024. p. 459-467. (Lecture Notes in Electrical Engineering). Epub 2024 Mar 31. doi: 10.1007/978-981-99-9005-4_58

Detecting Sleep Disorders from NREM Using DeepSDBPLM

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UN SDGs

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Improving sleep health with deep learning: automated classification of sleep stages and detection of sleep disorders

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