Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome
Expert Systems with Applications: An International Journal
Reducing dimensionality in a database of sleep EEG arousals
Expert Systems with Applications: An International Journal
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Sleep Apnea/Hypopnea Syndrome (SAHS) is a very common sleep disorder characterized by the repeated occurrence of involuntary breathing pauses during sleep. Cessation in breathing often causes Electroencephalographic (EEG) arousals as a response, and therefore detection of arousals is important since they provide important evidence for localization of apneic events and their number is directly related with SAHS severity. Arousals result in fragmented sleep and so they are one of the most important causes of daytime sleepiness. In this paper we present an approach to detect these arousals over polysomnographic recordings based on the machine learning paradigm. First a signal processing technique is proposed for the construction of learning patterns. Subsequently classifiers based on Fisher's linear and quadratic discriminates, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are compared for the learning process. The more suitable model was chosen finally showing an accuracy of 0.92.