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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 (2 papers)

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Research

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
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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)
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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
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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)
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