Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment

Yosef Bernardus Wirian; Yang Jiang; Sylvia Cerel-Suhl; Jeremiah Suhl; Qiang Cheng

doi:10.1007/978-3-031-42317-8_7

Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment

Yosef Bernardus Wirian, Yang Jiang, Sylvia Cerel-Suhl, Jeremiah Suhl, Qiang Cheng

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

Abstract

Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.

Original language	English
Title of host publication	The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -
Editors	Muhammad Younas, Irfan Awan, Salima Benbernou, Dana Petcu
Pages	81-90
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-031-42317-8_7
State	Published - 2023
Event	4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023 - Marrakech, Morocco Duration: Aug 14 2023 → Aug 16 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	768 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023
Country/Territory	Morocco
City	Marrakech
Period	8/14/23 → 8/16/23

Bibliographical note

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

Funding

The study is partially supported by NIH R21 AG070909-01, P30 AG072946-01, and R01 HD101508-01. Acknowledgements. The Sleep Heart Health Study (SHHS) was supported by National Heart, Lung, and Blood Institute cooperative agreements U01HL53916 (University of California, Davis), U01HL53931 (New York University), U01HL53934 (University of Minnesota), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL53938 (University of Arizona), U01HL53940 (University of Washington), U01HL53941 (Boston University), and U01HL63463 (Case Western Reserve University). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002).

Funders	Funder number
National Institutes of Health (NIH)	R21 AG070909-01, R01 HD101508-01, P30 AG072946-01
National Heart, Lung, and Blood Institute (NHLBI)	U01HL53916
Department of Psychiatry, Columbia University Irving Medical Center, New York, USA; New York State Psychiatric Institute, New York, USA	U01HL53934
Boston University School of Public Health/Boston University Medical Campus	U01HL63463
Minnesota State University-Mankato	U01HL53937, U01HL64360
University of California Davis	U01HL53931
The George Washington University	U01HL53941
The Johns Hopkins University	U01HL53938
University of Northern Arizona	U01HL53940
Case Western Reserve University	75N92019R002, R24 HL114473

Keywords

Deep Learning
EEG
Manifold Alignment
Sleep Architecture

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-42317-8_7

Cite this

Wirian, Y. B., Jiang, Y., Cerel-Suhl, S., Suhl, J., & Cheng, Q. (2023). Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment. In M. Younas, I. Awan, S. Benbernou, & D. Petcu (Eds.), The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) - (pp. 81-90). (Lecture Notes in Networks and Systems; Vol. 768 LNNS). https://doi.org/10.1007/978-3-031-42317-8_7

Wirian, Yosef Bernardus ; Jiang, Yang ; Cerel-Suhl, Sylvia et al. / Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment. The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. editor / Muhammad Younas ; Irfan Awan ; Salima Benbernou ; Dana Petcu. 2023. pp. 81-90 (Lecture Notes in Networks and Systems).

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title = "Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment",

abstract = "Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.",

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Wirian, YB, Jiang, Y, Cerel-Suhl, S, Suhl, J & Cheng, Q 2023, Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment. in M Younas, I Awan, S Benbernou & D Petcu (eds), The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. Lecture Notes in Networks and Systems, vol. 768 LNNS, pp. 81-90, 4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023, Marrakech, Morocco, 8/14/23. https://doi.org/10.1007/978-3-031-42317-8_7

Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment. / Wirian, Yosef Bernardus; Jiang, Yang; Cerel-Suhl, Sylvia et al.
The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. ed. / Muhammad Younas; Irfan Awan; Salima Benbernou; Dana Petcu. 2023. p. 81-90 (Lecture Notes in Networks and Systems; Vol. 768 LNNS).

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

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AU - Cheng, Qiang

PY - 2023

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N2 - Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.

AB - Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.

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Wirian YB, Jiang Y, Cerel-Suhl S, Suhl J, Cheng Q. Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment. In Younas M, Awan I, Benbernou S, Petcu D, editors, The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. 2023. p. 81-90. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-42317-8_7

Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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