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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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).
The study is partially supported by NIH R21 AG070909-01, P30 AG072946-01, and R01 HD101508-01.
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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: Younas, M., Awan, I., Benbernou, S., Petcu, D. (eds) The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Deep-BDB 2023. Lecture Notes in Networks and Systems, vol 768. Springer, Cham. https://doi.org/10.1007/978-3-031-42317-8_7
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DOI: https://doi.org/10.1007/978-3-031-42317-8_7
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