Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A decision based one-against-one method for multi-class support vector machine
Pattern Analysis & Applications
Asynchrony and cyclic variability in pressure support noninvasive ventilation
Computers in Biology and Medicine
Sleep staging based on signals acquired through bed sensor
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Genetic fuzzy classifier for sleep stage identification
Computers in Biology and Medicine
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging
Computers in Biology and Medicine
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
IEEE Transactions on Neural Networks
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Non-invasive ventilation (NIV), a recognized treatment for chronic hypercapnic respiratory failure, is predominantly applied at night. Nevertheless, the quality of sleep is rarely evaluated due to the required technological complexity. A new technique for automatic sleep staging is here proposed for patients treated by NIV. This new technique only requires signals (airflow and hemoglobin oxygen saturation) available in domiciliary ventilators plus a photo-plethysmogram, a signal already managed by some ventilators. Consequently, electroencephalogram, electrooculogram, electromyogram, and electrocardiogram recordings are not needed. Cardiorespiratory features are extracted from the three selected signals and used as input to a Support Vector Machine (SVM) multi-class classifier. Two different types of sleep scoring were investigated: the first type was used to distinguish three stages (wake, REM sleep and nonREM sleep), and the second type was used to evaluate five stages (wake, REM sleep, N1, N2 and N3 stages). Patient-dependent and patient-independent classifiers were tested comparing the resulting hypnograms with those obtained from visual/manual scoring by a sleep specialist. An average accuracy of 91% (84%) was obtained with three-stage (five-stage) patient-dependent classifiers. With patient-independent classifiers, an average accuracy of 78% (62%) was obtained when three (five) sleep stages were scored. Also if the PPG-based and flow features are left out, a reduction of 4.5% (resp. 5%) in accuracy is observed for the three-stage (resp. five-stage) cases. Our results suggest that long-term sleep evaluation and nocturnal monitoring at home is feasible in patients treated by NIV. Our technique could even be integrated into ventilators.