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

Scientia cum Industria

Jose L. Rybarczyk Filho¹  Ney Lemke¹  Emerson L. de Santa-Helena²  Suzana V. Schonwald³  Guilherme Dellagustin³  Diego Z. Carvalho³  Gunther J. L. Gerhardt⁴ 

[1] Department of Physics and Biophysics, Institute of Biosciences, Universidade Estadual Paulista Julio de Mesquita Filho, Brazil;Department of Physics, Universidade Federal de Sergipe, Brazi;Sleep Laboratory, Neurology Medicine Division, Hospital de Clínicas de Porto Alegre;Universidade de Caxias do Sul;

关键词: EEG;    Signal Analysis;    Matching Pursuit;    Obstructive apnea;    Machine Learning;    Decision tree;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown