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Smart Sensing for Advanced Sleep Analysis
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Smart Sensing for Advanced Sleep Analysis

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

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 14772

Special Issue Editors

Dr. Javier Andreu-Perez

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, 5B.542, Colchester Campus, UK
Interests: Artificial Intelligence; Soft Computing; Machine/Deep Learning; Cognitive Neuroscience; Neuro-engineering; Neurobilogy; Industrial Informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Delaram Jarchi

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: biomedical signal processing; machine learning and wearable sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human sleep monitoring is essential for the diagnosis and management of various sleep-related disorders. Wearable sensors can be used to measure physiological parameters to predict the occurrence of such disorders and assess the quality of sleep. In addition, they can be used to monitor, treat, and manage medical conditions related to sleep. The most common sleep abnormalities include obstructive sleep apnoea, upper airway resistance syndrome, periodic limb movement disorders, restless leg movement disorder, narcolepsy, rapid eye movement (REM), sleep behaviour, insomnia, dyssomnias, parasomnias, hypersomnia, nocturia, sleep stage detection and categorisation (wake, N1, N2, N3, and REM Sleep), circadian rhythm disorders and consciousness disorders.

Currently, the use of wearable sensors is limited to professional installation in a clinical setting. However, recent research has focused on transforming sleep monitoring systems to allow their use inside the home and as online and mobile modalities. This involves a reduction of the number of sensors used while still maintaining an acceptable level of accuracy.

This Special Issue will present novel methods for sleep monitoring applications including the use of various body-worn sensors such as smart watches, smart phones, and wearable EEGs. In particular, it will focus on methods incorporating advanced analytics techniques powered by the latest machine intelligence methods, such as deep learning or other new artificial intelligence paradigms, to identify various sleep stages, classify common body positions and unobtrusively estimate the respiratory rate.

Dr. Javier Andreu-Perez
Dr. Delaram Jarchi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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14 pages, 1460 KiB  
Article
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning
by Delaram Jarchi, Javier Andreu-Perez, Mehrin Kiani, Oldrich Vysata, Jiri Kuchynka , Ales Prochazka and Saeid Sanei
Sensors 2020, 20(9), 2594; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092594 - 02 May 2020
Cited by 21 | Viewed by 4253
Abstract
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal [...] Read more.
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. Full article
(This article belongs to the Special Issue Smart Sensing for Advanced Sleep Analysis)
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11 pages, 780 KiB  
Article
Sleep Apnea Detection with Polysomnography and Depth Sensors
by Martin Schätz, Aleš Procházka, Jiří Kuchyňka and Oldřich Vyšata
Sensors 2020, 20(5), 1360; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051360 - 02 Mar 2020
Cited by 20 | Viewed by 4379
Abstract
This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have [...] Read more.
This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep apnea events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20–35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep apneas by a sleep specialist. The resulting classifier can mark all apnea events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. When compared to the classification of polysomnographic breathing signal segments by a sleep specialist, which is used for calculating length of the event, the classifier has an F 1 score of 92.2% Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep apnea events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data. Full article
(This article belongs to the Special Issue Smart Sensing for Advanced Sleep Analysis)
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Review

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21 pages, 2043 KiB  
Review
A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies—Related Issues and Future Directions
by Jinyoung Choi, Moonyoung Kwon and Sung Chan Jun
Sensors 2020, 20(10), 2770; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102770 - 13 May 2020
Cited by 16 | Viewed by 5573
Abstract
Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have [...] Read more.
Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep’s effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with ‘sleep and stimulation’ and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems. Full article
(This article belongs to the Special Issue Smart Sensing for Advanced Sleep Analysis)
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