Literature DB >> 26253282

Analysis of A-phase transitions during the cyclic alternating pattern under normal sleep.

Martin Oswaldo Mendez1, Ioanna Chouvarda2, Alfonso Alba3, Anna Maria Bianchi4, Andrea Grassi5, Edgar Arce-Santana6, Guilia Milioli7, Mario Giovanni Terzano8, Liborio Parrino9.   

Abstract

An analysis of the EEG signal during the B-phase and A-phases transitions of the cyclic alternating pattern (CAP) during sleep is presented. CAP is a sleep phenomenon composed by consecutive sequences of A-phases (each A-phase could belong to a possible group A1, A2 or A3) observed during the non-REM sleep. Each A-phase is separated by a B-phase which has the basal frequency of the EEG during a specific sleep stage. The patterns formed by these sequences reflect the sleep instability and consequently help to understand the sleep process. Ten recordings from healthy good sleepers were included in this study. The current study investigates complexity, statistical and frequency signal properties of electroencephalography (EEG) recordings at the transitions: B-phase--A-phase. In addition, classification between the onset-offset of the A-phases and B-phase was carried out with a kNN classifier. The results showed that EEG signal presents significant differences (p < 0.05) between A-phases and B-phase for the standard deviation, energy, sample entropy, Tsallis entropy and frequency band indices. The A-phase onset showed values of energy three times higher than B-phase at all the sleep stages. The statistical analysis of variance shows that more than 80% of the A-phase onset and offset is significantly different from the B-phase. The classification performance between onset or offset of A-phases and background showed classification values over 80% for specificity and accuracy and 70% for sensitivity. Only during the A3-phase, the classification was lower. The results suggest that neural assembles that generate the basal EEG oscillations during sleep present an over-imposed coordination for a few seconds due to the A-phases. The main characteristics for automatic separation between the onset-offset A-phase and the B-phase are the energy at the different frequency bands.

Keywords:  Border identification; CAP; EEG; Nonlinear analysis; Sleep

Mesh:

Year:  2015        PMID: 26253282     DOI: 10.1007/s11517-015-1349-9

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  30 in total

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Journal:  Am J Physiol Heart Circ Physiol       Date:  2000-06       Impact factor: 4.733

2.  An automatic method for the recognition and classification of the A-phases of the cyclic alternating pattern.

Authors:  Carlo Navona; Umberto Barcaro; Enrica Bonanni; Fabio Di Martino; Michelangelo Maestri; Luigi Murri
Journal:  Clin Neurophysiol       Date:  2002-11       Impact factor: 3.708

3.  Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep.

Authors:  Sara Mariani; Elena Manfredini; Valentina Rosso; Andrea Grassi; Martin O Mendez; Alfonso Alba; Matteo Matteucci; Liborio Parrino; Mario G Terzano; Sergio Cerutti; Anna M Bianchi
Journal:  Med Biol Eng Comput       Date:  2012-03-20       Impact factor: 2.602

4.  Regional scalp EEG slow-wave synchronization during sleep cyclic alternating pattern A1 subtypes.

Authors:  Raffaele Ferri; Francesco Rundo; Oliviero Bruni; Mario G Terzano; Cornelis J Stam
Journal:  Neurosci Lett       Date:  2006-06-27       Impact factor: 3.046

5.  On arousal from sleep: time-frequency analysis.

Authors:  M O Mendez; A M Bianchi; N Montano; V Patruno; E Gil; C Mantaras; S Aiolfi; S Cerutti
Journal:  Med Biol Eng Comput       Date:  2008-02-12       Impact factor: 2.602

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Authors:  M G Terzano; L Parrino; M Boselli; M C Spaggiari; G Di Giovanni
Journal:  J Clin Neurophysiol       Date:  1996-03       Impact factor: 2.177

7.  Cyclic alternating pattern (CAP) in normal sleep: polysomnographic parameters in different age groups.

Authors:  L Parrino; M Boselli; M C Spaggiari; A Smerieri; M G Terzano
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1998-12

8.  Automatic detection of sleep spindles by analysis of harmonic components.

Authors:  G Sciarretta; A Bricolo
Journal:  Med Biol Eng       Date:  1970-09

9.  Application of Tsallis entropy to EEG: quantifying the presence of burst suppression after asphyxial cardiac arrest in rats.

Authors:  Xiaofeng Jia; Haiyan Ding; Datian Ye; Nitish V Thakor
Journal:  IEEE Trans Biomed Eng       Date:  2009-08-18       Impact factor: 4.538

10.  CAP variables and arousals as sleep electroencephalogram markers for primary insomnia.

Authors:  Mario Giovanni Terzano; Liborio Parrino; Maria Cristina Spaggiari; Vincenzo Palomba; Mariano Rossi; Arianna Smerieri
Journal:  Clin Neurophysiol       Date:  2003-09       Impact factor: 3.708

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  1 in total

1.  A-phase classification using convolutional neural networks.

Authors:  Edgar R Arce-Santana; Alfonso Alba; Martin O Mendez; Valdemar Arce-Guevara
Journal:  Med Biol Eng Comput       Date:  2020-03-02       Impact factor: 2.602

  1 in total

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