Most Discriminative Atom Selection for Apnea-hypopnea Events Detection
| Autor: | Hugo Leonardo Rufiner, Ruben D. Spies, L.E. Di Persia, R.E. Rolon |
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| Rok vydání: | 2015 |
| Předmět: |
Artificial neural network
medicine.diagnostic_test business.industry Computer science Pattern recognition Polysomnography medicine.disease respiratory tract diseases Correlation Discriminative model Test set Pattern recognition (psychology) medicine Artificial intelligence business Hypopnea Selection (genetic algorithm) |
| Zdroj: | IFMBE Proceedings ISBN: 9783319131160 |
| DOI: | 10.1007/978-3-319-13117-7_146 |
| Popis: | The sleep apnea-hypopnea syndrome is characterized by repetitive episodes of upper airway obstruction that occur while sleeping, usually associated with a reduction in blood oxygen saturation (SaO2). This work presents a novel most discriminative atom selection method to predict the occurrence of apnea-hypopnea (AH) events. First two types of dictionaries (one using class information and the other without it) are estimated, then a greedy pursuit algorithm is used in order to obtain the activation coefficients. The SHHS polysomnography database which includes nearly 1000 polysomnograms, is used for training and testing. A subset of the most discriminative coefficients is then selected for each dictionary, training a pattern recognition neural network to detect the AH events. Finally these events from a test set of 64 studies with different grades of illness are detected. Correlation coefficients of 0.90 and 0.74 are obtained for the dictionaries trained with and without class information, respectively. |
| Databáze: | OpenAIRE |
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