title: Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, 20133 Milan, Italy. sara1.mariani@mail.polimi.it
authors.forename: Sara
authors.initials: S
authors.lastname: Mariani
authors.forename: Elena
authors.initials: E
authors.lastname: Manfredini
authors.forename: Valentina
authors.initials: V
authors.lastname: Rosso
authors.forename: Andrea
authors.initials: A
authors.lastname: Grassi
authors.forename: Martin O
authors.initials: MO
authors.lastname: Mendez
authors.forename: Alfonso
authors.initials: A
authors.lastname: Alba
authors.forename: Matteo
authors.initials: M
authors.lastname: Matteucci
authors.forename: Liborio
authors.initials: L
authors.lastname: Parrino
authors.forename: Mario G
authors.initials: MG
authors.lastname: Terzano
authors.forename: Sergio
authors.initials: S
authors.lastname: Cerutti
authors.forename: Anna M
authors.initials: AM
authors.lastname: Bianchi
mesh_terms: D000328:Adult; D000465:Algorithms; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D017286:Polysomnography / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*; D060388:Support Vector Machine