Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview
Abstract
:1. Introduction
- An in-depth discussion about sleep’s role in psychological, physiological, and behavioral mechanisms and the importance of sleep monitoring to detect and track symptoms related to sleep disturbances or disorders;
- A comprehensive overview of prototype and commercial wearable devices for sleep monitoring, reported in the scientific literature and on the market, enabling discreet and accurate monitoring of users’ vital signs during sleep;
- An overview of classical and representative-learning algorithms for sleep staging and sleep disorder detection by analyzing data related to single or multiple vital signs;
- A survey of the scientific works analyzing the effect of the COVID-19 pandemic on sleep functions, ascribable to both infection and changes in lifestyle.
2. Importance of Sleep Monitoring
3. Survey of Wearable Devices for Sleep Monitoring Presented in the Literature
- Brain activity using EEG with a high SNR, measuring also advanced sleep markers, such as sleep spindles and K-complexes;
- Eye movement using EOG;
- Physiological factors, with high accuracy for measuring heart rate (median error of 1.7 beats/min) and respiration (median error of 1 BrPM);
- Sleep stages, almost as well as polysomnography;
- Gross body movement and sleep posture.
4. Overview of Commercial Wearable Devices for Detecting Sleep Disorders
5. A Survey about Algorithms for Sleep Staging and Disorders Detection
5.1. Overview of Algorithms for Sleep Staging
5.2. Algorithms for Detecting Sleep Disorders
6. Effects of COVID-19 Pandemic on Sleep
- Insomnia;
- Sleep disruptions;
- Irregularity of the circadian rhythm;
- Nightmares;
- Reduction in sleep quality and duration;
- Excessive daytime sleepiness;
- Decreased focus;
- Bad mood.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Work | Number of Detected Parameters | Type of Detected Parameters | Availability of Wireless Module to Transfer Data | Tested Individuals | Accuracy | Sensitivity | Invasiveness |
---|---|---|---|---|---|---|---|
Mask B [39] | 1 | Eye movement | No | 4 | N.A. 1 | N.A. 1 | Low |
Phymask [41] | 5 | Brain activity, eye movement, heart, and respiration rate, sleep stages, body movement | Yes | 10 | >0.8 2 | >0.8 2 | Low |
HealthSOS [42] | 1 | Brain activity | Yes | 37 | 92% | 98% | Low |
Smart Sleep Mask [43] | 4 | Eye movement, head position, temperature, and breathing sounds | Yes | 1 | N.A. 1 | N.A. 1 | Low |
Chesma [44] | 2 | Eye movement, heart rate | Yes | 1 | N.A. 1 | N.A. 1 | Low |
ARAM [45] | 2 | Respiration activity, body movement | No | 6 | N.A. 1 | N.A. 1 | Medium |
Morfea [46] | 3 | Apnea and hypopnea events, chest movements, head position | Yes | 1 | 93% | 89% | Medium |
Device | Number of Parameters Detected | Integrated Sensors | Gathered Parameters | Feedbacks/ Interventions | Cost |
---|---|---|---|---|---|
BrainBit [50] | 4 | EEG, PPG, EMG, EOG | Brain activity, heart rate, body movement, eye movement | Psychology and cognitive remediation | USD $499 |
SmartSleep [67] | 1 | EEG | Brain activity | Audio tones to boost the slow wave | USD $399 |
Muse S [53] | 4 | EEG, PPG, gyroscope, accelerometer | Brain activity, heart rate, breath rate, body movements | Digital sleeping pills (sleep stories and meditation, ambient soundscape, nature and music biofeedbacks) | USD $399 |
Dreem 2 [55] | 4 | EEG, PPG, gyroscope, accelerometer | Brain activity, heart rate, breath rate, body movement | CBT-I exercises | N.A. 1 |
iBand+ [58] | 2 | EEG, accelerometer, gyroscope | Brain activity, head movement | Audio tones to induce sleep | USD $449 |
Neuroon Open [59] | 4 | EEG, EOG, PPG, thermometer, | Brain activity, eye movement, body temperature, blood oxygenation | Audio tones to induce sleep | N.A. 1 |
Somni [60] | 2 | EOG, accelerometer | Eye movement, head movement | Audiovisual feedback to induce sleep | N.A. 1 |
BrainLink Pro [61] | 4 | EEG, PPG, gyroscope thermometer, accelerometer, | Brain activity, heart rate, body temperature, head movement | No | USD $259 |
Sleep Shepherd [62] | 2 | EEG, gyroscope, movement sensor | Brain activity head movement | Binaural tones to induce sleep | N.A. 1 |
Sleep Profiler [64] | 5 | EEG, EOG, EMG, accelerometer, ECG (optional), PPG (optional), nasal transducer (model SP29), pulse rate sensor (model SP29), oximeter (model SP29) | Brain activity, eye movement, head position, heart rate, quantitative snoring | No | N.A. 1 |
CGMH-Training | CGMH-Validation | DRAMS Subjects | UCDSADB | |
---|---|---|---|---|
TP | 4.464 | 1.800 | 1.777 | 1.838 |
FP | 2.143 | 1.763 | 2.151 | 2.853 |
TN | 31.550 | 14.906 | 14.532 | 12.883 |
FN | 3.315 | 1.633 | 1.572 | 2.400 |
SE (%) | 57.4 | 52.4 | 53.1 | 43.4 |
SP (%) | 93.6 | 89.4 | 87.1 | 81.9 |
ACC (%) | 86.8 | 83.1 | 81.4 | 73.7 |
PR (%) | 67.6 | 50.5 | 45.2 | 39.2 |
F1 | 0.62 | 0.51 | 0.49 | 0.41 |
AUC | 0.90 | 0.83 | 0.81 | 0.72 |
Kappa | 0.54 | 0.41 | 0.38 | 0.24 |
Stage | Precision | Accuracy | Cohen’s Kappa |
---|---|---|---|
Wake | 0.73 ± 0.20 | 0.90 ± 0.07 | 0.63 ± 0.19 |
REM | 0.71 ± 0.22 | 0.92 ± 0.04 | 0.68 ± 0.22 |
N1/N2 | 0.80 ± 0.11 | 0.79 ± 0.08 | 0.56 ± 0.15 |
N3 | 0.62 ± 0.33 | 0.92 ± 0.04 | 0.53 ± 0.27 |
Authors | Number of Detected Parameters | Detected Parameters | Number of Sleep Stage | Accuracy [%] | Used Algorithms | Participants |
---|---|---|---|---|---|---|
J. Malik et al. [71] | 2 | ECG and PPG (deriving HRV and IHR 1) | 2 | 86.8 | CNN | 56 patients and 90 healthy subjects |
M. Radha et al. [74] | 4 | ECG, EEG, EOG, EMG (deriving HRV) | 4 | N.A. 2 | LSTM | 97 patients and 195 healthy subjects |
H. Hwang et al. [75] | 2 | Breathing activity and body movements from the PVDF sensor | 2 | 70.9 | Decision rules algorithm | 13 patients and 12 healthy subjects |
A. Tataraidze et al. [76] | 1 | Effort signals using RIP | 4 | N.A. 2 | XGB, a decision tree-based algorithm | 685 healthy subjects |
Z. Beattie et al. [77] | 2 | Breathing activity and body movements from a 3D accelerometer and optical PPG (deriving HRV) | 4 | 69.0 | LDA | 60 healthy subjects |
K. Aggarwal et al. [79] | 1 | Breathing activity from CPAP | 4 | 74.1 | CRF | 400 patients |
A. Malafeev et al. [81] | 4 | EEG, EMG, ECG, and EOG | 5 | N.A. 2 | RF, LSTM, CNN-LSTM | 23 patients and 18 healthy subjects |
W. Wen [82] | 1 | EEG | 5 | N.A. 2 | SVM | 6641 healthy subjects |
H. Shen et al. [83] | 1 | EEG | 4 | 92.0 | Begged trees | Patients and healthy subjects from three databases |
R. Agarwal et al. [87] | 3 | EEG, EOG, and EMG | 6 | N.A. 2 | CASS | 12 subjects, some of them suffering from sleep disorders |
A. Rahimi et al. [88] | 1 | ECG (deriving HRV and EDR) | 2 | 81.8 | SVM | Not specified |
P. Fonseca et al. [91] | 2 | ECG and RIP | 5 | 61.1% | LD, HMM, CRF | 231 subjects, some of them suffering from sleep disorders |
Q. Li et al. [92] | 1 | ECG (deriving HRV, EDR, and RSA) | 3 | 85.1% | CPC, CNN, SVM | 7451 subjects |
Work | Number of Detected Parameters | Detected Parameters | Type of Used Algorithm | Detected Sleep Disorder | Features Extracted |
---|---|---|---|---|---|
M. Bahrami et al. [99] | 1 | ECG | LDA, QDA, LR, Gaussian naïve Bayes classifiers, Gaussian process, SVMs, KNN, DT, ET, RF, AdaBoost, GB, MLP, MV, convolutional networks, and DRNNs | Sleep apnea | From R–R intervals: minimum, range, median, mean, standard deviation, skewness, kurtosis, the standard deviation of successive differences between adjacent R–R intervals, root mean square of successive differences between normal heartbeats, VLF, LF, HF, cardiovagal index, cardio sympathetic index |
S. S. Mostafa et al. [101] | 1 | EEG | EBT; EBooT, SVMs, and KNN | OSA | Activity, mobility, and complexity |
M. Sharma et al. [100] | 2 | ECG and SpO2 | CNN and NSGA-II | Insomnia, NFLE, RBD, PLM disorder, and SDB | N.A. 1 |
M. J. Lado et al. [102] | 1 | ECG (deriving HR and HRV) | RHRV | OSA | LF/HF quotient |
M. Bahrami et al. [104] | 1 | ECG | DRNNs and CNN | Sleep apnea | R-peak amplitude and R–R intervals |
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De Fazio, R.; Mattei, V.; Al-Naami, B.; De Vittorio, M.; Visconti, P. Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview. Micromachines 2022, 13, 1335. https://doi.org/10.3390/mi13081335
De Fazio R, Mattei V, Al-Naami B, De Vittorio M, Visconti P. Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview. Micromachines. 2022; 13(8):1335. https://doi.org/10.3390/mi13081335
Chicago/Turabian StyleDe Fazio, Roberto, Veronica Mattei, Bassam Al-Naami, Massimo De Vittorio, and Paolo Visconti. 2022. "Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview" Micromachines 13, no. 8: 1335. https://doi.org/10.3390/mi13081335