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Entropy and Sleep Disorders II

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (18 July 2021) | Viewed by 12208

Special Issue Editors

Biomedical Engineering Group, Department of Theory of Signal and Communications and Telematic Engineering, University of Valladolid, 7, 47005 Valladolid, Spain
Interests: biomedical signal processing; computer-aided diagnosis; neural engineering; brain–computer interface; non-linear analysis
Special Issues, Collections and Topics in MDPI journals
Biomedical Engineering Group, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain
Interests: biomedical signal processing; machine learning; data science; sleep apnea; sleep
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first edition of this Special Issue underlined the usefulness of entropy metrics, complexity quantifiers, and other related measures to study biomedical signals and gain insight into different sleep disorders. Here, we present “Entropy and Sleep Disorders II”, where our purpose is to expand the range of sleep-related problems analyzed.

Sleep apnea, sleep stages classification, sleep spindles, or restless leg syndrome are among the more frequently studied sleep issues. New studies regarding these topics are very welcome. In addition, we would like to encourage researchers to present original and recent developments covering the study of other major diseases through the analysis of sleep-related information. The topics may include, but are not be limited to:

  • Sleep and cognition;
  • Sleep and dementia/Alzheimer’s disease;
  • Sleep and cardiovascular risks;
  • Sleep and schizophrenia;
  • Cardiorespiratory coupling while sleeping.

Similarly, possible suggestions regarding the signals involved in these studies include:

  • Electroencephalogram (EEG);
  • Electrocardiogram (ECG) and R-R time series (also heart rate variability analysis);
  • Photoplethysmography and blood oxygen saturation (SpO2);
  • Respiratory signals;
  • Electrooculogram.

Prof. Dr. Roberto Hornero
Dr. Gonzalo César Gutiérrez-Tobal
Guest Editors

Manuscript Submission Information

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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. Entropy is an international peer-reviewed open access monthly 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.

Keywords

  • sleep apnea
  • spindles
  • cognition
  • cardiovascular risks
  • dementia
  • Alzheimer’s disease
  • respiration
  • sleep stage classification
  • schizophrenia
  • narcolepsy

Related Special Issue

Published Papers (5 papers)

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Research

30 pages, 13240 KiB  
Article
Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea
by Adrián Martín-Montero, Gonzalo C. Gutiérrez-Tobal, David Gozal, Verónica Barroso-García, Daniel Álvarez, Félix del Campo, Leila Kheirandish-Gozal and Roberto Hornero
Entropy 2021, 23(8), 1016; https://0-doi-org.brum.beds.ac.uk/10.3390/e23081016 - 06 Aug 2021
Cited by 12 | Viewed by 2486
Abstract
Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to [...] Read more.
Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0–13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0–0.04 Hz; low frequency: 0.04–0.15 Hz; and high frequency: 0.15–0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001–0.005 Hz; BW2: 0.028–0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders II)
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18 pages, 3094 KiB  
Article
Complexity of Body Movements during Sleep in Children with Autism Spectrum Disorder
by Naoki Furutani, Tetsuya Takahashi, Nobushige Naito, Takafumi Maruishi, Yuko Yoshimura, Chiaki Hasegawa, Tetsu Hirosawa and Mitsuru Kikuchi
Entropy 2021, 23(4), 418; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040418 - 31 Mar 2021
Cited by 4 | Viewed by 2402
Abstract
Recently, measuring the complexity of body movements during sleep has been proven as an objective biomarker of various psychiatric disorders. Although sleep problems are common in children with autism spectrum disorder (ASD) and might exacerbate ASD symptoms, their objectivity as a biomarker remains [...] Read more.
Recently, measuring the complexity of body movements during sleep has been proven as an objective biomarker of various psychiatric disorders. Although sleep problems are common in children with autism spectrum disorder (ASD) and might exacerbate ASD symptoms, their objectivity as a biomarker remains to be established. Therefore, details of body movement complexity during sleep as estimated by actigraphy were investigated in typically developing (TD) children and in children with ASD. Several complexity analyses were applied to raw and thresholded data of actigraphy from 17 TD children and 17 children with ASD. Determinism, irregularity and unpredictability, and long-range temporal correlation were examined respectively using the false nearest neighbor (FNN) algorithm, information-theoretic analyses, and detrended fluctuation analysis (DFA). Although the FNN algorithm did not reveal determinism in body movements, surrogate analyses identified the influence of nonlinear processes on the irregularity and long-range temporal correlation of body movements. Additionally, the irregularity and unpredictability of body movements measured by expanded sample entropy were significantly lower in ASD than in TD children up to two hours after sleep onset and at approximately six hours after sleep onset. This difference was found especially for the high-irregularity period. Through this study, we characterized details of the complexity of body movements during sleep and demonstrated the group difference of body movement complexity across TD children and children with ASD. Complexity analyses of body movements during sleep have provided valuable insights into sleep profiles. Body movement complexity might be useful as a biomarker for ASD. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders II)
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14 pages, 2173 KiB  
Article
Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea
by Duan Liang, Shan Wu, Lan Tang, Kaicheng Feng and Guanzheng Liu
Entropy 2021, 23(3), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/e23030267 - 24 Feb 2021
Cited by 12 | Viewed by 2012
Abstract
Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of [...] Read more.
Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (p < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (|r| = 0.602, p < 0.05), NPSampEn (|r| = 0.756, p < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders II)
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12 pages, 1071 KiB  
Article
A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals
by Xiangfa Zhao and Guobing Sun
Entropy 2021, 23(1), 116; https://0-doi-org.brum.beds.ac.uk/10.3390/e23010116 - 18 Jan 2021
Cited by 14 | Viewed by 2520
Abstract
Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from [...] Read more.
Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders II)
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17 pages, 2091 KiB  
Article
Assessment of Nocturnal Autonomic Cardiac Imbalance in Positional Obstructive Sleep Apnea. A Multiscale Nonlinear Approach
by Daniel Álvarez, C. Ainhoa Arroyo, Julio F. de Frutos, Andrea Crespo, Ana Cerezo-Hernández, Gonzalo C. Gutiérrez-Tobal, Fernando Vaquerizo-Villar, Verónica Barroso-García, Fernando Moreno, Tomás Ruiz, Roberto Hornero and Félix del Campo
Entropy 2020, 22(12), 1404; https://0-doi-org.brum.beds.ac.uk/10.3390/e22121404 - 12 Dec 2020
Cited by 5 | Viewed by 1962
Abstract
Positional obstructive sleep apnea (POSA) is a major phenotype of sleep apnea. Supine-predominant positional patients are frequently characterized by milder symptoms and less comorbidity due to a lower age, body mass index, and overall apnea-hypopnea index. However, the bradycardia-tachycardia pattern during apneic events [...] Read more.
Positional obstructive sleep apnea (POSA) is a major phenotype of sleep apnea. Supine-predominant positional patients are frequently characterized by milder symptoms and less comorbidity due to a lower age, body mass index, and overall apnea-hypopnea index. However, the bradycardia-tachycardia pattern during apneic events is known to be more severe in the supine position, which could affect the cardiac regulation of positional patients. This study aims at characterizing nocturnal heart rate modulation in the presence of POSA in order to assess potential differences between positional and non-positional patients. Patients showing clinical symptoms of suffering from a sleep-related breathing disorder performed unsupervised portable polysomnography (PSG) and simultaneous nocturnal pulse oximetry (NPO) at home. Positional patients were identified according to the Amsterdam POSA classification (APOC) criteria. Pulse rate variability (PRV) recordings from the NPO readings were used to assess overnight cardiac modulation. Conventional cardiac indexes in the time and frequency domains were computed. Additionally, multiscale entropy (MSE) was used to investigate the nonlinear dynamics of the PRV recordings in POSA and non-POSA patients. A total of 129 patients (median age 56.0, interquartile range (IQR) 44.8–63.0 years, median body mass index (BMI) 27.7, IQR 26.0–31.3 kg/m2) were classified as POSA (37 APOC I, 77 APOC II, and 15 APOC III), while 104 subjects (median age 57.5, IQR 49.0–67.0 years, median BMI 29.8, IQR 26.6–34.7 kg/m2) comprised the non-POSA group. Overnight PRV recordings from positional patients showed significantly higher disorderliness than non-positional subjects in the smallest biological scales of the MSE profile (τ = 1: 0.25, IQR 0.20–0.31 vs. 0.22, IQR 0.18–0.27, p < 0.01) (τ = 2: 0.41, IQR 0.34–0.48 vs. 0.37, IQR 0.29–0.42, p < 0.01). According to our findings, nocturnal heart rate regulation is severely affected in POSA patients, suggesting increased cardiac imbalance due to predominant positional apneas. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders II)
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