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Assistive Sensors and Related Algorithms for Sleep, Respiration, Asthma and Stress Monitoring

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Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 2035

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Special Issue Information

Dear Colleagues,

The combination of recent developments in sensor technologies and machine learning provides a multitude of opportunities in medical speciality and wellbeing applications. Home sleep monitoring using ambient and/or wearable sensors is one such area that enables us to monitor a wide range of sleep disorders on a continuous basis in the native environment of the subject. Another condition that benefits from recent advances in sensors and machine learning is stress. Stress has long been measured through physical tests and questionnaires that rely primarily on user-provided data. Sensor technologies and algorithms can promise real-time, continuous collection of stress data that can be used in clinical diagnoses or for personal stress monitoring and mediation. Stress can cause the autonomic nervous system (ANS) to release hormones, such as adrenaline and cortisol. These hormones raise the heart rate to circulate blood to vital organs and muscles more efficiently, preparing the body to take immediate action if necessary. High levels of cortisol result in disturbed sleep patterns. Not sleeping enough can lead to irritability, mood swings, and fatigue which can further exacerbate psychological or even physical stress. Wearable and ambient sensors may help us to monitor both sleep patterns and stress levels and develop remedies for breaking the vicious sleep-stress cycle.

Low back pain is another condition that effects sleep and triggers sleep disturbances. Low back pain (LBP) affects up to 80% of people at some point in their lives and is second only to respiratory illness for days of lost work. Advances in sensor technologies may allow ambulatory monitoring of spine posture during daily activities and sleep, revealing the underlying causes of low back pain. Understanding these factors is a necessary first step for developing targeted interventions and reducing the overall costs associated with chronic and recurrent LBP problems in terms of human suffering and utilization of healthcare services.

As one of the most common chronic conditions, asthma affects over 235 million people worldwide. In the U.S. alone, asthma affects 18.7 million adults and 6.8 million children. Whether it is due to the symptoms of asthma or just staying up too late, missing sleep can make asthma worse. The chances of experiencing asthma symptoms are higher during sleep. Nocturnal wheezing, cough, and trouble breathing are common. Stress is also a common asthma trigger. New sensor technologies can help to monitor respiratory rate, movement, stress levels, and sleep patterns and may allow us improve management of asthma exacerbations.

We invite manuscripts proposing new sensors, presenting and evaluating sensors, sensor technologies and algorithms for assessing sleep, stress, respiratory conditions and their cause-and-effect relationships. In addition, we welcome novel sensors monitoring the above-mentioned conditions as well as algorithms and solutions to address these conditions.

Prof. Dr. Yusuf Ozturk
Prof. Dr. Ahmet Enis Cetin
Guest Editors

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Keywords

sleep quality monitoring
stress measurements
analysis methods
respiration rate measurement
respiration anomaly detection
movement detection
multisensory fusion
asthma

Published Papers (1 paper)

Research

24 pages, 2149 KiB

Open AccessArticle

A Holistic Strategy for Classification of Sleep Stages with EEG

by Sunil Kumar Prabhakar, Harikumar Rajaguru, Semin Ryu, In cheol Jeong and Dong-Ok Won

Sensors 2022, 22(9), 3557; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093557 - 07 May 2022

Cited by 4 | Viewed by 1677

Abstract

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem. Full article

(This article belongs to the Special Issue Assistive Sensors and Related Algorithms for Sleep, Respiration, Asthma and Stress Monitoring)

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