Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

Ahmadzadeh, Sahar; Luo, Jikui; Wiffen, Richard

doi:10.1109/rbme.2020.3033930

Cited by 25 publications

(8 citation statements)

References 154 publications

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“…These technologies are associated with either invasive or non-invasive healthcare. In terms of healthcare, non-invasive (or non-contact) technology can be used for monitoring patients without any physical contact with the body, whereas invasive (or contact) technology requires direct physical bodily contact [ [52] , [53] , [54] , [55] , [56] ].…”

Section: Intelligent Healthcare Technologymentioning

confidence: 99%

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed

Shah

Ahmad

et al. 2022

Journal of Pharmaceutical Analysis

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The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which caused the COVID-19 pandemic, has affected more than 250 million people worldwide. With the recent rise of a new Delta variant, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this review paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, RADAR, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

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Section: Intelligent Healthcare Technologymentioning

confidence: 99%

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed

Shah

Ahmad

et al. 2022

Journal of Pharmaceutical Analysis

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“…These categories are time-frequency analysis which uses time-series and frequency domain in the signal processing, numerical analysis which uses numerical techniques, classification and training which uses machine learning, and other methodologies which uses experimental and mathematical modeling. Ahmadzadeh et al [3] conducted a survey on biomedical sensors, technologies and algorithms for the breath-related sleeping disorders. The paper categorizes the literature into invasive and non-invasive techniques and provides a review of the related articles in both the categories.…”

Section: Related Workmentioning

confidence: 99%

A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep

Hussain,

Sheng,

Zhang

et al. 2021

Preprint

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Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep which is having negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and have now become an important tool for understanding sleep behavior. The gold standard method for sleep analysis is polysomnography (PSG) conducted in a clinical environment but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods which are cheap and easy to use for in-home sleep monitoring. In this paper, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest works done using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on 10 key factors, to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also present some publicly available datasets for different categories of sleep monitoring. In the end, we discuss several open issues and provide future research directions in the area of sleep monitoring.

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“…There are different types of EEG signals such as Routine EEG, Sleep EEG/Sleep deprived signals, Ambulatory EEG, video telemetry, etc. 2…”

Section: Introductionmentioning

confidence: 99%

“…There are different types of EEG signals such as Routine EEG, Sleep EEG/Sleep deprived signals, Ambulatory EEG, video telemetry, etc. 2 Sleep EEG signals are acquired when the patient is asleep to detect sleep disorders. The major reasons for poor sleep are mental stress, unhealthy food habits, lack of physical activity, etc.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary model for sleep quality analytics using fuzzy system

Hangaragi,

Nizampatnam,

Kaliyaperumal

et al. 2023

Proc Inst Mech Eng H

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Electroencephalography (EEG) is a neuro signal reflecting brain activity. These signals provide information about brain activity, eye movements, and muscle tone, which can be used to determine the sleep stage. Categorizing sleep stages can be done manually by visually. Alternatively, automated algorithms can be developed using machine learning techniques to classify sleep stages based on signal features and patterns. This paper aims to automatically classify sleep stages based on extracted patterns from EEG signals. A fuzzy min-max neural network is proposed and implemented for sleep stage classification and clustering. The paper concludes that the fuzzy min-max neural network outperforms other tested methods in sleep stage classification. The models implemented in the study include K-Nearest Neighbor (KNN), Random Forest, Decision Tree, XGBoost, AdaBoost, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN), and the fuzzy min-max classifier. The results indicate that the fuzzy classifier achieves the highest accuracy of 86%, followed by the CNN model with 81%. Among the machine learning algorithms, Random Forest with an accuracy of 55.46%, followed by XGBoost with 53.18%, surpassing the other algorithms used in the experiment. AdaBoost and Gaussian Naive Bayes both achieve an accuracy of 45.10%. Decision Tree, KNN, LDA, and QDA yield accuracies of 37.66%, 16.46%, 28.57%, and 29.5% respectively. These findings demonstrate the efficiency of the fuzzy min-max neural network and the superiority of the fuzzy classifier and CNN models in sleep stage classification, indicating their potential for accurate automated sleep stage analysis.

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Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

Cited by 25 publications

References 154 publications

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep

An evolutionary model for sleep quality analytics using fuzzy system

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