Assessment of Sleep Quality Based on Automatic Detection of Emotional Arousal Epochs from EEG Signal

Assessment of Sleep Quality Based on Automatic Detection of Emotional Arousal Epochs from EEG Signal
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-4
Year of Publication : 2024
Author : Bui Huy Hai
DOI : 10.14445/22315381/IJETT-V72I4P134

How to Cite?

Bui Huy Hai, "Assessment of Sleep Quality Based on Automatic Detection of Emotional Arousal Epochs from EEG Signal," International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 333-343, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I4P134

Abstract
Nocturnal sleep is the main time to recover energy and repair cells in the human body. Hence, the detection of insomnia and the assessment of the quality of sleep are important in determining patients’ states of health so that appropriate therapies can be administered. Many previous studies often evaluated sleep quality by analyzing positive and negative emotions; in this study, we developed a new method for evaluating the quality of sleep based on detecting the number of emotional arousal epochs during sleep. Emotional arousal epochs (each contains a 10-second segment of data) were extracted based on analyzing the standard epochs of emotional data. The densities of emotional arousal epochs were correlated with the states of the patient’s health, and the results were compared to develop a table of relationships for the assessment of the quality of sleep. The densities of emotional arousal epochs were correlated with the states of the patient’s health, and the results were compared to develop a table of relationships for the assessment of the quality of sleep. The new method has proven effective when integrated into an automatic identification system; this system identifies emotional segments and classifies sleep quality based on the intensity of emotional epochs in each sleep cycle with an average accuracy of 87.5%.

Keywords
Sleep, Emotional arousal, Wavelet entropy, Electroencephalogram, Sleep disorders.

References
[1] Charles Adedayo Ogunbode et al., “Negative Emotions about Climate Change are Related to Insomnia Symptoms and Mental Health: Cross-Sectional Evidence from 25 Countries,” Current Psychology, vol. 42, pp. 845-854, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] M. Camaioni et al., “EEG Patterns Prior to Motor Activations of Parasomnias: A Systematic,” Nature and Science of Sleep, vol. 13, pp. 713-728, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Anthony Kales, and Allan Rechtschaffen, A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, United States Government Printing Office, Washington, 1968.
[Google Scholar] [Publisher Link]
[4] Yuta Senzai, and Massimo Scanziani, “A Cognitive Process Occurring during Sleep is Revealed by Rapid Eye Movements,” Science, vol. 377, no. 6609, pp. 999-1004, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Aakash K. Patel et al., Physiology, Sleep Stages, StatPearls Publishing, 2022.
[Google Scholar] [Publisher Link]
[6] Mustafa H. Gandhi, and Prabhu D. Emmady, Physiology, K Complex, StatPearls Publishing, 2023.
[Google Scholar] [Publisher Link]
[7] Chris Drew, “Schachter-Singer Theory – Examples and Definition,” Helpful Professor, 2023.
[Google Scholar] [Publisher Link]
[8] Bibek Karna, Abdulghani Sankari, and Geethika Tatikonda, Sleep Disorder, StatPearls Publishing, 2023.
[Google Scholar] [Publisher Link]
[9] Divya Garg, and Gyanendra K. Verma, “Emotion Recognition in Valence-Arousal Space from Multi-Channel EEG Data and Wavelet Based Deep Learning Framework,” Procedia Computer Science, vol. 171, pp. 857-867, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jiaojiao Lu, Yan An, and Jun Qiu, “Relationship between Sleep Quality, Mood State, and Performance of Elite Air-Rifle Shooters,” BMC Sports Science, Medicine and Rehabilitation, vol. 14, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Radu Lefter et al., “Interactions between Sleep and Emotions in Humans and Animal Models,” Medicina, vol. 58, no. 2, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Victor Menezes Silva, Joedyson Emmanuel de Macedo Magalhaes, and Leandro Lourenção Duarte, “Quality of Sleep and Anxiety are Related to Circadian Preference in University Students,” PLoS One, vol. 15, no. 9, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Can Deng et al., “Emotion Recognition Positively Correlates with Steady-State Visual Evoked Potential Amplitude and Alpha Entrainment,” Neuroscience, vol. 434, pp. 191-199, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Giuseppe Loddo et al., “Disorders of Arousal in Adults: New Diagnostic Tools for Clinical Practice,” Sleep Science Practice, vol. 3, pp. 1-13, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Divyanshu Dwivedi, and Ashutosh Chamoli, and Sandip Kumar Rana, “Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data, Entropy, vol. 25, no. 2, pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Maëva Moyne et al., “Brain Reactivity to Emotion Persists in NREM Sleep and is Associated with Individual Dream Recall,” Cerebral Cortex Communications, vol. 3, no. 1, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Marie Vandekerckhove, and Yu-lin Wang, “Emotion, Emotion Regulation and Sleep: An Intimate Relationship,” AIMS Neuroscience, vol. 5, no. 1, pp. 1-17, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Yanjing Wang et al., “Changes in Ventromedial Prefrontal Cortex Functional Connectivity are Correlated with Increased Risk-Taking after Total Sleep Deprivation,” Behavioural Brain Research, vol. 418, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Y. Audrey Hay et al., “Thalamus Mediates Neocortical Down State Transition via GABAB-Receptor-Targeting Interneurons,” Neuron, vol. 109, no. 17, pp. 2682-2690, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sirinthip Roomkham et al., Technologies for Quantifying Sleep: Improved Quality of Life or Overwhelming Gadgets?, Quantifying Quality of Life, Springer, Cham, pp. 151-164, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Dan Guo et al., “ECG-Derived Cardiopulmonary Analysis of Pediatric Sleep-Disordered Breathing,” Sleep Medicine, vol. 12, no. 4, pp. 384-389, 2011.
[CrossRef] [Google Scholar] [Publisher Link]