Sleep Apnea Detection Using Contact-Based and Non-Contact-Based Using Deep Learning Methods

2021 ◽  
pp. 87-103
Author(s):  
Anand Singh Rajawat ◽  
Romil Rawat ◽  
Kanishk Barhanpurkar ◽  
Rabindra Nath Shaw ◽  
Ankush Ghosh
Keyword(s):  
Deep Learning ◽  
Sleep Apnea ◽  
Learning Methods ◽  
Apnea Detection

scholarly journals SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches

2021 ◽  
Author(s):  
Arlene John ◽  
Koushik Kumar Nundy ◽  
Barry Cardiff ◽  
Deepu John
Keyword(s):  
Deep Learning ◽  
Sleep Apnea ◽  
Learning Network ◽  
Apnea Detection ◽  
Deep Learning Network

Energy Efficient Deep Learning Inference Embedded on FPGA for Sleep Apnea Detection

2022 ◽  
Author(s):  
Omiya Hassan ◽  
Tanmoy Paul ◽  
Maruf Hossain Shuvo ◽  
Dilruba Parvin ◽  
Rushil Thakker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sleep Apnea ◽  
Energy Efficient ◽  
Apnea Detection

scholarly journals A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 914
Author(s):  
Anita Ramachandran ◽  
Anupama Karuppiah
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Gold Standard ◽  
Large Population ◽  
Literature Survey ◽  
Feature Engineering ◽  
Detection Systems ◽  
Recent Advances ◽  
Apnea Detection

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.

SpO2 based sleep apnea detection using deep learning

2017 ◽  
Author(s):  
Sheikh Shanawaz Mostafa ◽  
Fabio Mendonca ◽  
Fernando Morgado-Dias ◽  
Antonio Ravelo-Garcia
Keyword(s):  
Deep Learning ◽  
Sleep Apnea ◽  
Apnea Detection

Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home

2021 ◽  
Vol 2 (2) ◽  
pp. 1-25
Author(s):  
Stein Kristiansen ◽  
Konstantinos Nikolaidis ◽  
Thomas Plagemann ◽  
Vera Goebel ◽  
Gunn Marit Traaen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Resource Use ◽  
Classification Performance ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Respiratory Disorder ◽  
Sleep Monitoring ◽  
Apnea Detection

Sleep apnea is a common and strongly under-diagnosed severe sleep-related respiratory disorder with periods of disrupted or reduced breathing during sleep. To diagnose sleep apnea, sleep data are collected with either polysomnography or polygraphy and scored by a sleep expert. We investigate in this work the use of supervised machine learning to automate the analysis of polygraphy data from the A3 study containing more than 7,400 hours of sleep monitoring data from 579 patients. We conduct a systematic comparative study of classification performance and resource use with different combinations of 27 classifiers and four sleep signals. The classifiers achieve up to 0.8941 accuracy (kappa: 0.7877) when using all four signal types simultaneously and up to 0.8543 accuracy (kappa: 0.7080) with only one signal, i.e., oxygen saturation. Methods based on deep learning outperform other methods by a large margin. All deep learning methods achieve nearly the same maximum classification performance even when they have very different architectures and sizes. When jointly accounting for classification performance, resource consumption and the ability to achieve with less training data high classification performance, we find that convolutional neural networks substantially outperform the other classifiers.

scholarly journals Machine learning for diagnosis and monitoring of sleep apnea

2020 ◽  
pp. 43-58
Author(s):  
Dmytro Tkachenko ◽  
Ihor Krush ◽  
Vitalii Mykhalko ◽  
Anatolii Petrenko
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Wearable Devices ◽  
Learning Approaches ◽  
Learning Models ◽  
Multimodal Data ◽  
Severity Level ◽  
Complex Models ◽  
Apnea Detection

This paper contains a review and analysis of applications of modern ma-chine learning approaches to solve sleep apnea severity level detection by localization of apnea episodes and prediction of the subsequent apnea episodes. We demonstrate that signals provided by cheap wearable devices can be used to solve typical tasks of sleep apnea detection. We review major publicly available datasets that can be used for training respective deep learning models, and we analyze the usage options of these datasets. In particular, we prove that deep learning could improve the accuracy of sleep apnea classification, sleep apnea localization, and sleep apnea prediction, especially using more complex models with multimodal data from several sensors.

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