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Medical and Biological Engineering and Computing
Generically Dependent Continuant
RDF description of Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep - http://orbis.uaslp.mx/vivo/individual/scopus3065
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Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep
Cyclic alternating pattern; Machine learning; Neural networks; Sleep; Support vector machines Automatic classification; Automatic classifiers; Automatic detector; Cyclic alternating pattern; Descriptors; Discriminant classifiers; EEG recording; Healthy subjects; Linear discriminants; Machine learning techniques; Objective criteria; Periodic activity; Sleep; Sleep recordings; Visual analysis; Adaptive boosting; Learning systems; Neural networks; Sleep research; Support vector machines; adult; algorithm; article; artificial neural network; electroencephalography; female; human; male; methodology; physiology; polysomnography; signal processing; sleep stage; support vector machine; Adult; Algorithms; Electroencephalography; Female; Humans; Male; Neural Networks (Computer); Polysomnography; Signal Processing, Computer-Assisted; Sleep Stages; Support Vector Machines
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%25 for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%25. The SVM leads to accuracy of 81.9%25. 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 sleepmicrostructure overwhole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement. © International Federation for Medical and Biological Engineering 2012.