Med Biol Eng Comput - Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep.

Tópicos

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Resumo

This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity descriptor and a variance descriptor were extracted and used to train different machine-learning algorithms. A visual scoring provided by an expert clinician was used as golden standard. Four alternative mathematical machine-learning techniques were implemented: (1) discriminant classifier, (2) support vector machines, (3) adaptive boosting, and (4) supervised artificial neural network. The results of the classification, compared with the visual analysis, showed average accuracies equal to 84.9 and 81.5% for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%. The SVM leads to accuracy of 81.9%. The performance achieved by the automatic classification is encouraging, since an efficient automatic classifier would benefit the practice in everyday clinics, preventing the physician from the time-consuming activity of the visually scoring of the sleep microstructure over whole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement.

Resumo Limpo

studi aim develop automat detector phase cyclic altern pattern period activ general occur nonrem nrem sleep eight polysomnograph record healthi subject examin eeg record five band descriptor activ descriptor varianc descriptor extract use train differ machinelearn algorithm visual score provid expert clinician use golden standard four altern mathemat machinelearn techniqu implement discrimin classifi support vector machin adapt boost supervis artifici neural network result classif compar visual analysi show averag accuraci equal linear discrimin neural network respect adaboost slight lower accuraci equal svm lead accuraci perform achiev automat classif encourag sinc effici automat classifi benefit practic everyday clinic prevent physician timeconsum activ visual score sleep microstructur whole h sleep record final classif base learn algorithm provid object criterion overcom problem interscor disagr

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