A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems
Autor: | Anita Ramachandran, Anupama Karuppiah |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Feature engineering
020205 medical informatics Leadership and Management Computer science Population Health Informatics Review 02 engineering and technology Polysomnography Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Health Information Management 0202 electrical engineering electronic engineering information engineering medicine education wearable systems Sleep disorder education.field_of_study medicine.diagnostic_test business.industry Health Policy Deep learning Sleep apnea deep learning Gold standard (test) medicine.disease sleep apnea respiratory tract diseases machine learning Medicine Artificial intelligence Literature survey business computer 030217 neurology & neurosurgery |
Zdroj: | Healthcare, Vol 9, Iss 914, p 914 (2021) Healthcare |
ISSN: | 2227-9032 |
Popis: | Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey. |
Databáze: | OpenAIRE |
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