E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Entropy and Sleep Disorders"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (31 March 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Roberto Hornero

Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011, Valladolid, Spain
Website | E-Mail
Interests: biomedical signal processing; information theory; non-linear dynamics, entropy and complexity; sleep disorders; neurodegenerative diseases

Special Issue Information

Dear Colleagues,

Although we spend about 1/3 of our life asleep, there has been relatively little attention paid to disorders of sleep until recently. Sleep disorders are amongst the most prevalent illness in today’s society. Unfortunately, the consequences of impaired sleep and sleep disorders are frequently under recognized and many patients go undiagnosed and untreated for years. Some of the most common sleep disorders are insomnia, sleep apnea, restless leg syndrome, narcolepsy, REM sleep behaviour disorder and parasomnias. These sleep disorders are often related to major medical conditions, such as heart disease, strokes and hypertension.

Polysomnography (PSG) is commonly ordered to search for sleep pathological conditions. PSG is a study conducted while patients are fully asleep or trying to sleep. Several biomedical signals are registered, including brain waves (electroencephalogram), eye movements (electroculogram), electrical activity of muscles (electromyogram), heart rate and electrical activity of hearth (electrocardiogram), blood oxygen levels, breathing effort or airflow. The aim of this Special Issue is to encourage researchers to present original and recent developments on time series analysis using entropy metrics, complexity quantifiers and related measures to study these biomedical signals during a PSG to help in the diagnosis of different sleep disorders.

Prof. Dr. Roberto Hornero
Guest Editor

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 papers will be 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. 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 1500 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

  • Biomedical signals during sleep
  • Entropy measures
  • Complexity quantifiers
  • Non-linear methods
  • Insomnia
  • Sleep apnea
  • Restless leg syndrome
  • Narcolepsy
  • REM sleep behaviour disorder
  • Parasomnias

Published Papers (6 papers)

View options order results:
result details:
Displaying articles 1-6
Export citation of selected articles as:

Research

Open AccessFeature PaperArticle Influence of Parameter Selection in Fixed Sample Entropy of Surface Diaphragm Electromyography for Estimating Respiratory Activity
Entropy 2017, 19(9), 460; doi:10.3390/e19090460
Received: 17 May 2017 / Revised: 1 August 2017 / Accepted: 29 August 2017 / Published: 1 September 2017
PDF Full-text (5429 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Fixed sample entropy (fSampEn) is a robust technique that allows the evaluation of inspiratory effort in diaphragm electromyography (EMGdi) signals, and has potential utility in sleep studies. To appropriately estimate respiratory effort, fSampEn requires the adjustment of several parameters. The aims of the
[...] Read more.
Fixed sample entropy (fSampEn) is a robust technique that allows the evaluation of inspiratory effort in diaphragm electromyography (EMGdi) signals, and has potential utility in sleep studies. To appropriately estimate respiratory effort, fSampEn requires the adjustment of several parameters. The aims of the present study were to evaluate the influence of the embedding dimension m, the tolerance value r, the size of the moving window, and the sampling frequency, and to establish recommendations for estimating the respiratory activity when using the fSampEn on surface EMGdi recorded for different inspiratory efforts. Values of m equal to 1 and r ranging from 0.1 to 0.64, and m equal to 2 and r ranging from 0.13 to 0.45, were found to be suitable for evaluating respiratory activity. fSampEn was less affected by window size than classical amplitude parameters. Finally, variations in sampling frequency could influence fSampEn results. In conclusion, the findings suggest the potential utility of fSampEn for estimating muscle respiratory effort in further sleep studies. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders)
Figures

Figure 1

Open AccessFeature PaperArticle Irregularity and Variability Analysis of Airflow Recordings to Facilitate the Diagnosis of Paediatric Sleep Apnoea-Hypopnoea Syndrome
Entropy 2017, 19(9), 447; doi:10.3390/e19090447
Received: 28 July 2017 / Revised: 22 August 2017 / Accepted: 24 August 2017 / Published: 26 August 2017
PDF Full-text (1906 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this paper is to evaluate the evolution of irregularity and variability of airflow (AF) signals as sleep apnoea-hypopnoea syndrome (SAHS) severity increases in children. We analyzed 501 AF recordings from children 6.2 ± 3.4 years old. The respiratory rate variability
[...] Read more.
The aim of this paper is to evaluate the evolution of irregularity and variability of airflow (AF) signals as sleep apnoea-hypopnoea syndrome (SAHS) severity increases in children. We analyzed 501 AF recordings from children 6.2 ± 3.4 years old. The respiratory rate variability (RRV) signal, which is obtained from AF, was also estimated. The proposed methodology consisted of three phases: (i) extraction of spectral entropy (SE1), quadratic spectral entropy (SE2), cubic spectral entropy (SE3), and central tendency measure (CTM) to quantify irregularity and variability of AF and RRV; (ii) feature selection with forward stepwise logistic regression (FSLR), and (iii) classification of subjects using logistic regression (LR). SE1, SE2, SE3, and CTM were used to conduct exploratory analyses that showed increasing irregularity and decreasing variability in AF, and increasing variability in RRV as apnoea-hypopnoea index (AHI) was higher. These tendencies were clearer in children with a higher severity degree (from AHI ≥ 5 events/hour). Binary LR models achieved 60%, 76%, and 80% accuracy for the AHI cutoff points 1, 5, and 10 e/h, respectively. These results suggest that irregularity and variability measures are able to characterize paediatric SAHS in AF recordings. Hence, the use of these approaches could be helpful in automatically detecting SAHS in children. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders)
Figures

Open AccessArticle Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home
Entropy 2017, 19(6), 284; doi:10.3390/e19060284
Received: 6 May 2017 / Revised: 12 June 2017 / Accepted: 14 June 2017 / Published: 17 June 2017
PDF Full-text (2045 KB) | HTML Full-text | XML Full-text
Abstract
Untreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as
[...] Read more.
Untreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders)
Figures

Figure 1

Open AccessFeature PaperArticle Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing
Entropy 2017, 19(6), 282; doi:10.3390/e19060282
Received: 6 May 2017 / Revised: 7 June 2017 / Accepted: 9 June 2017 / Published: 16 June 2017
PDF Full-text (460 KB) | HTML Full-text | XML Full-text
Abstract
Pulse rate variability (PRV), an alternative measure of heart rate variability (HRV), is altered during obstructive sleep apnea. Correntropy spectral density (CSD) is a novel spectral analysis that includes nonlinear information. We recruited 160 children and recorded SpO2 and photoplethysmography (PPG), alongside
[...] Read more.
Pulse rate variability (PRV), an alternative measure of heart rate variability (HRV), is altered during obstructive sleep apnea. Correntropy spectral density (CSD) is a novel spectral analysis that includes nonlinear information. We recruited 160 children and recorded SpO2 and photoplethysmography (PPG), alongside standard polysomnography. PPG signals were divided into 1-min epochs and apnea/hypoapnea (A/H) epochs labeled. CSD was applied to the pulse-to-pulse interval time series (PPIs) and five features extracted: the total spectral power (TP: 0.01–0.6 Hz), the power in the very low frequency band (VLF: 0.01–0.04 Hz), the normalized power in the low and high frequency bands (LFn: 0.04–0.15 Hz, HFn: 0.15–0.6 Hz), and the LF/HF ratio. Nonlinearity was assessed with the surrogate data technique. Multivariate logistic regression models were developed for CSD and power spectral density (PSD) analysis to detect epochs with A/H events. The CSD-based features and model identified epochs with and without A/H events more accurately relative to PSD-based analysis (area under the curve (AUC) 0.72 vs. 0.67) due to the nonlinearity of the data. In conclusion, CSD-based PRV analysis provided enhanced performance in detecting A/H epochs, however, a combination with overnight SpO2 analysis is suggested for optimal results. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders)
Figures

Figure 1

Open AccessArticle Entropy Information of Cardiorespiratory Dynamics in Neonates during Sleep
Entropy 2017, 19(5), 225; doi:10.3390/e19050225
Received: 30 March 2017 / Revised: 11 May 2017 / Accepted: 12 May 2017 / Published: 15 May 2017
PDF Full-text (724 KB) | HTML Full-text | XML Full-text
Abstract
Sleep is a central activity in human adults and characterizes most of the newborn infant life. During sleep, autonomic control acts to modulate heart rate variability (HRV) and respiration. Mechanisms underlying cardiorespiratory interactions in different sleep states have been studied but are not
[...] Read more.
Sleep is a central activity in human adults and characterizes most of the newborn infant life. During sleep, autonomic control acts to modulate heart rate variability (HRV) and respiration. Mechanisms underlying cardiorespiratory interactions in different sleep states have been studied but are not yet fully understood. Signal processing approaches have focused on cardiorespiratory analysis to elucidate this co-regulation. This manuscript proposes to analyze heart rate (HR), respiratory variability and their interrelationship in newborn infants to characterize cardiorespiratory interactions in different sleep states (active vs. quiet). We are searching for indices that could detect regulation alteration or malfunction, potentially leading to infant distress. We have analyzed inter-beat (RR) interval series and respiration in a population of 151 newborns, and followed up with 33 at 1 month of age. RR interval series were obtained by recognizing peaks of the QRS complex in the electrocardiogram (ECG), corresponding to the ventricles depolarization. Univariate time domain, frequency domain and entropy measures were applied. In addition, Transfer Entropy was considered as a bivariate approach able to quantify the bidirectional information flow from one signal (respiration) to another (RR series). Results confirm the validity of the proposed approach. Overall, HRV is higher in active sleep, while high frequency (HF) power characterizes more quiet sleep. Entropy analysis provides higher indices for SampEn and Quadratic Sample entropy (QSE) in quiet sleep. Transfer Entropy values were higher in quiet sleep and point to a major influence of respiration on the RR series. At 1 month of age, time domain parameters show an increase in HR and a decrease in variability. No entropy differences were found across ages. The parameters employed in this study help to quantify the potential for infants to adapt their cardiorespiratory responses as they mature. Thus, they could be useful as early markers of risk for infant cardiorespiratory vulnerabilities. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders)
Figures

Figure 1

Open AccessArticle A New Kind of Permutation Entropy Used to Classify Sleep Stages from Invisible EEG Microstructure
Entropy 2017, 19(5), 197; doi:10.3390/e19050197
Received: 31 March 2017 / Revised: 21 April 2017 / Accepted: 26 April 2017 / Published: 28 April 2017
Cited by 1 | PDF Full-text (607 KB) | HTML Full-text | XML Full-text
Abstract
Permutation entropy and order patterns in an EEG signal have been applied by several authors to study sleep, anesthesia, and epileptic absences. Here, we discuss a new version of permutation entropy, which is interpreted as distance to white noise. It has a scale
[...] Read more.
Permutation entropy and order patterns in an EEG signal have been applied by several authors to study sleep, anesthesia, and epileptic absences. Here, we discuss a new version of permutation entropy, which is interpreted as distance to white noise. It has a scale similar to the well-known χ 2 distributions and can be supported by a statistical model. Critical values for significance are provided. Distance to white noise is used as a parameter which measures depth of sleep, where the vigilant awake state of the human EEG is interpreted as “almost white noise”. Classification of sleep stages from EEG data usually relies on delta waves and graphic elements, which can be seen on a macroscale of several seconds. The distance to white noise can anticipate such emerging waves before they become apparent, evaluating invisible tendencies of variations within 40 milliseconds. Data segments of 30 s of high-resolution EEG provide a reliable classification. Application to the diagnosis of sleep disorders is indicated. Full article
(This article belongs to the Special Issue Entropy and Sleep Disorders)
Figures

Figure 1

Back to Top