[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126594-en":3,"doc-seo-126594-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126594,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Sleep condition detection and assessment with optical fiber interferometer based on machine learning","Sleep disorders are increasingly common in modern life, degrading health and work efficiency while creating a need for practical diagnostic methods. Conventional polysomnography (PSG) is limited by complexity, bulky equipment, and low portability. An optical-fiber sensor using a fiber interferometer is proposed for real-time, low-power, non-invasive sleep monitoring without interference. The system captures ballistocardiography (BCG) and electrocardiogram (ECG) signals and applies a machine-learning approach to detect sleep conditions and assess sleep quality. Experimental results verify improved monitoring and assessment performance.","iScience  \nll  \nOPEN ACCESS  \nArticle  \nSleep condition detection and assessment with optical fiber interferometer based on machine learning  \nQing Wang, Weimin Lyu, Jing Zhou, Changyuan Yu  \n[changyuan.yu@polyu.edu.hk](changyuan.yu@polyu.edu.hk)  \nHighlights  \nAn optical fiber sensor based on fiber interferometer is proposed  \nA machine learning model is proposed to detect vital sleep conditions  \nThe monitoring system has full potential in clinical applications  \nWang et al., iScience 26 , 107244  \nJuly 21, 2023 ª 2023  \n[https://doi.org/10.1016/](https://doi.org/10.1016/)[ ](https://doi.org/10.1016/)[j.isci.2023.107244](j.isci.2023.107244)  \niScience  \nll  \nOPEN ACCESS  \nArticle  \nSleep condition detection and assessment with optical fiber interferometer  \nbased on machine learning  \nQing Wang, 1 Weimin Lyu, 1 Jing Zhou, 1 and Changyuan Yu1,2,3,*  \nSUMMARY  \nThe prevalence of sleep disorders has increased because of the fast-paced and stressful modern lifestyle, negatively impacting the quality of human life and work efficiency. It is crucial to address sleep problems. However, the current practice of diagnosing sleep disorders using polysomnography (PSG) has limitations such as complexity, large equipment, and low portability, hindering its practicality for daily use. To overcome these challenges, in this article an optical fiber sensor is proposed as a viable solution for sleep monitoring. This device offers benefits like low power consumption, non-invasiveness, absence of interference, and real-time health monitoring. We introduce the sensor with an optical fiber interferometer to capture ballistocardiography (BCG) and electrocardiogram (ECG) signals from the human body. Furthermore, a new machine learning method is proposed for sleep condition detection. Experimental results demonstrate the superior performance of this architecture and the proposed model in monitoring and assessing sleep quality.  \nINTRODUCTION  \nAs an essential part of life activities for mankind, sleep plays a vital role in the maintenance and regulation of a variety of biological functions in the molecular sense.1 ,2 Good sleep is beneficial for people to maintain not only physical and mental health but also normal brain function during the day.3–6 In the modern society with rapid developments, there are more and more people suffering from sleep disorders because of the increased stress of life or work, thus resulting in poor quality of sleep.7–12 For example, the occurrence of apnea during sleep causes sleep disruption at night, which affects the metabolism of other organs, thus causing a range of cardiovascular diseases, diabetes, and even mental issues. In particular, the problems caused by cardiovascular diseases become more serious with higher mortality rate in the elderly group than in the younger group.13 , 14 For this reason, more and more scholars and medical professionals have paid attention to the study of sleep, for better treatment of the diseases caused by sleep disorders through the analysis of various physiological indicators at each stage of the sleep process. With the change of sleep time, human brain state changes at different stages and various physiological parameters of the human body change accordingly.15–20 According to these changes, it can be observed that people experience several different stages during sleep, which means the sleep process can be operated by stages.21 The long-term monitoring of vital signs of the body during sleep can assist doctors in making diagnoses, further preventing serious health risks and promoting the development of a healthy lifestyle.22–24  \nMonitoring vital signs is essential in evaluating human states at the physiological level, with numerous applications, including smart home healthcare.25 ,26 One such vital sign is the ballistocardiography (BCG) signal, which is generated by the impact of blood flow on the blood vessels during a heartbeat.27–29 Flexible sensors that do not require physical contact ca","cbCaihzd9pIyPIp5","https://ap.wps.com/l/cbCaihzd9pIyPIp5","pdf",5855446,3,1,20,"English","en",105,"# Summary\n# Introduction\n## Motivation and limitations of current diagnosis\n## Vital-sign monitoring and BCG/ECG signals\n## Role of optical fiber sensors and interferometer design","[{\"question\":\"Why is polysomnography (PSG) considered impractical for daily sleep monitoring?\",\"answer\":\"PSG involves complex procedures and large equipment, and it is not sufficiently portable for routine daily use.\"},{\"question\":\"What signals does the proposed optical-fiber system capture for sleep monitoring?\",\"answer\":\"It uses the optical-fiber interferometer to capture ballistocardiography (BCG) and electrocardiogram (ECG) signals from the human body.\"},{\"question\":\"How does machine learning contribute to the detection and assessment of sleep conditions?\",\"answer\":\"A dedicated machine-learning method is introduced to detect sleep conditions from the monitored data, and experiments show improved performance for monitoring and sleep quality assessment.\"}]","Sleep condition detection and assessment with optical fiber interferometer based on machine learning | 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is polysomnography (PSG) considered impractical for daily sleep monitoring?","Question",{"text":76,"@type":77},"PSG involves complex procedures and large equipment, and it is not sufficiently portable for routine daily use.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What signals does the proposed optical-fiber system capture for sleep monitoring?",{"text":81,"@type":77},"It uses the optical-fiber interferometer to capture ballistocardiography (BCG) and electrocardiogram (ECG) signals from the human body.",{"name":83,"@type":74,"acceptedAnswer":84},"How does machine learning contribute to the detection and assessment of sleep conditions?",{"text":85,"@type":77},"A dedicated machine-learning method is introduced to detect sleep conditions from the monitored data, and experiments show improved performance for monitoring and sleep quality 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