Dataset Information

A hybrid double-density dual-tree discrete wavelet transformation and marginal Fisher analysis for scoring sleep stages from unprocessed single-channel electroencephalogram.

ABSTRACT: Background:We demonstrate an innovative approach of automated sleep recording formed on the electroencephalogram (EEG) with one channel. Methods:In this study, double-density dual-tree discrete wavelet transformation (DDDTDWT) was used for decomposing the image, and marginal Fisher analysis (MFA) was used for reducing the dimension. A proposed model on unprocessed EEG models was used on monitored training of 5-group sleep phase forecasting. Results:Our network includes a 14-row structure, and a 30-s period was extracted as input in order to be categorized which is followed by second and third period prior to the first 30-s period. Another consecutive period for temporal tissue was added which is not required to a signal preprocess and attribute data derivation phase. Our means of evaluating and improving our approach was to use input from the Sleep Heart Health Study (SHHS), which is a large study field aimed at using research from numerous centers and people and which studies the records of specialist-rated polysomnography (PSG). Performance measures could reach the desired level, which is a precision of 0.87 and a Cohen's kappa of 0.81. Conclusions:The use of a large, collaborative study of specialist graders can enhance the likelihood of good globalization. Overall, the novel approach learned by our network showcases the models based on each category.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7136739 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

A hybrid double-density dual-tree discrete wavelet transformation and marginal Fisher analysis for scoring sleep stages from unprocessed single-channel electroencephalogram.

Liu Yan Y Gao Jie J Cao Wei W Wei Longxiao L Mao Yanyang Y Liu Weimin W Wang Wei W Liu Zhenling Z

Quantitative imaging in medicine and surgery 20200301 3

<h4>Background</h4>We demonstrate an innovative approach of automated sleep recording formed on the electroencephalogram (EEG) with one channel.<h4>Methods</h4>In this study, double-density dual-tree discrete wavelet transformation (DDDTDWT) was used for decomposing the image, and marginal Fisher analysis (MFA) was used for reducing the dimension. A proposed model on unprocessed EEG models was used on monitored training of 5-group sleep phase forecasting.<h4>Results</h4>Our network includes a 14 ...[more]

PMID: 32269935

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Project description:PURPOSE:Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns. METHODS:Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients. RESULTS:Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68). CONCLUSIONS:This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

Dataset Information

A hybrid double-density dual-tree discrete wavelet transformation and marginal Fisher analysis for scoring sleep stages from unprocessed single-channel electroencephalogram.

Publications

A hybrid double-density dual-tree discrete wavelet transformation and marginal Fisher analysis for scoring sleep stages from unprocessed single-channel electroencephalogram.

Similar Datasets

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