Literature DB >> 17119795

Visual and automatic cyclic alternating pattern (CAP) scoring: inter-rater reliability study.

Agostinho Rosa1, Gabriela Rodrigues Alves, Magneide Brito, Maria Cecília Lopes, Sérgio Tufik.   

Abstract

The classification of short duration events in the EEG during sleep, as the A stage of the cyclic alternating pattern (CAP) is a tedious and error prone task. The number of events under normal conditions is large (several hundreds), and it is necessary to mark the limits of the events with precision, otherwise the time sensitive classification of the CAP phases (A and B) and specially the scoring of different types of A phases will be compromised. The objective of this study is to verify the feasibility of visual CAP scoring with only one channel of EEG, the evaluation of the inter-scorer agreement in a variety of recordings, and the comparison of the visual scorings with a known automatic scoring system. Sixteen hours of one channel (C4-A1 or C3-A2) of NREM sleep were extracted from eight whole night recordings in European Data Format and presented to the different scorers. The average inter-scorer agreement for all scorers is above 70%, the pair wise inter-scorer agreement found was between 69% up to 77.5%. These values are similar to what has been reported in different type studies. The automatic scoring system has similar performance of the visual scorings. The study also has shown that it is possible to classify the CAP using only one channel of EEG.

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Year:  2006        PMID: 17119795     DOI: 10.1590/s0004-282x2006000400008

Source DB:  PubMed          Journal:  Arq Neuropsiquiatr        ISSN: 0004-282X            Impact factor:   1.420


  8 in total

1.  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

Review 2.  A review of signals used in sleep analysis.

Authors:  A Roebuck; V Monasterio; E Gederi; M Osipov; J Behar; A Malhotra; T Penzel; G D Clifford
Journal:  Physiol Meas       Date:  2013-12-17       Impact factor: 2.833

3.  Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram.

Authors:  Qiao Li; Qichen Li; Chengyu Liu; Supreeth P Shashikumar; Shamim Nemati; Gari D Clifford
Journal:  Physiol Meas       Date:  2018-12-21       Impact factor: 2.833

4.  Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection.

Authors:  Fábio Mendonça; Sheikh Shanawaz Mostafa; Diogo Freitas; Fernando Morgado-Dias; Antonio G Ravelo-García
Journal:  Entropy (Basel)       Date:  2022-05-13       Impact factor: 2.738

5.  Chronic widespread musculoskeletal pain, fatigue, depression and disordered sleep in chronic post-SARS syndrome; a case-controlled study.

Authors:  Harvey Moldofsky; John Patcai
Journal:  BMC Neurol       Date:  2011-03-24       Impact factor: 2.474

6.  Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches.

Authors:  Maria Paola Tramonti Fantozzi; Ugo Faraguna; Adrien Ugon; Gastone Ciuti; Andrea Pinna
Journal:  PLoS One       Date:  2021-12-02       Impact factor: 3.240

7.  Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG.

Authors:  Fábio Mendonça; Sheikh Shanawaz Mostafa; Diogo Freitas; Fernando Morgado-Dias; Antonio G Ravelo-García
Journal:  Int J Environ Res Public Health       Date:  2022-09-01       Impact factor: 4.614

8.  Disturbed EEG sleep, paranoid cognition and somatic symptoms identify veterans with post-traumatic stress disorder.

Authors:  Harvey Moldofsky; Lorne Rothman; Robert Kleinman; Shawn G Rhind; J Donald Richardson
Journal:  BJPsych Open       Date:  2016-11-09
  8 in total

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