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Clustering Sleep-Wake Transitions in Electromyography Data

Huynh, Linh
http://rave.ohiolink.edu/etdc/view?acc_num=osu1492762116527713

Abstract Details

2017, Master of Mathematical Sciences, Ohio State University, Mathematical Sciences.
Contrary to the common perception that sleep is continuous, sleep is actually fragmented by brief awakenings throughout the night in all mammalian species studied. Experiments have shown that sleep interval lengths are exponentially distributed in both 2-day-old and 21-day-old rats, while the distribution of wake interval lengths changes from exponential in 2-day-olds to power law in 21-day-olds. The analysis of metastable neuronal network models suggest that gradual sleep-wake transitions are responsible for this power law behavior. This finding motivates the question of whether there are different types of sleep-wake transitions and if so, what are they? To address the question, twenty wake-to-sleep transition segments of a 21-day-old rat’s EMG signal are clustered using hierarchical agglomerative clustering with dynamic time warping as a distance measure between clusters. In extracting transition neighborhoods for clustering, we have found that the wake-to-sleep transitions are different: some can be easily recognized with just short neighborhoods around the transition points, while some require longer neighborhoods, suggesting more gradual transitions. This is also consistent with the two separate groups identified by clustering under different conditions. The results provide a foundation for future work on explaining the power law distribution of wake interval lengths.
Janet Best (Advisor)
Ching-Shan Chou (Committee Member)
62 p.
Mathematics

Recommended Citations

Citations

  • Huynh, L. (2017). Clustering Sleep-Wake Transitions in Electromyography Data [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492762116527713

    APA Style (7th edition)

  • Huynh, Linh. Clustering Sleep-Wake Transitions in Electromyography Data . 2017. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1492762116527713.

    MLA Style (8th edition)

  • Huynh, Linh. "Clustering Sleep-Wake Transitions in Electromyography Data ." Master's thesis, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492762116527713

    Chicago Manual of Style (17th edition)

osu1492762116527713
370
© 2017, some rights reserved.Creative Commons License Clustering Sleep-Wake Transitions in Electromyography Data by Linh Huynh is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.