Analysis of EEG Sleep Spindle Parameters from Apnea Patients Using Massive Computing and Decision Tree

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.

Theta-Beta Ratios Are Prominent EEG Features for Visual Tracking Tasks

“…Decision trees were utilized by Gerhardt et al (2014) for feature selection of sleep spindle classification in Apnea (Temporary cessation of breathing, especially during sleep) patients. EEG signal recordings were done through a 64channel EEG cap with 256Hz sampling rate.…”