Analysis of EEG sleep spindle parameters from apnea patients using massive computing and decision tree
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Abstract
In this study, Matching Pursuit (MP) procedure is applied to the detection and analysis of EEG sleep spindles in patients evaluated for suspected OSAS. Elements having the frequency of EEG sleep spindles are selected from different dictionary sizes, with and without a frequency modulation function (chirp) for signal description. This procedure was done with high computational cost in order to find best parameters for real EEG data description. At the end we used the atom parameters as input for a decision tree-based classifier, making possible to obtain a classification according to apnea-hypopnea index group and allowing to see how atom parameters such as frequency and amplitude are affected by the presence of sleep apnea.
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Keywords
EEG, Signal analysis, Matching pursuit, Obstructive apnea, Machine learning, Decision tree
Language
English
Citation
Scientia Cum Industria, v. 2, n. 1, p. 15-18, 2014.
