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

Authors

  • Gunther J. L. Gerhardt Universidade de Caxias do Sul
  • Ney Lemke Department of Physics and Biophysics, Institute of Biosciences, Universidade Estadual Paulista Julio de Mesquita Filho
  • Diego Z. Carvalho Sleep Laboratory, Neurology Medicine Division, Hospital de Clínicas de Porto Alegre
  • Emerson L. de Santa-Helena Department of Physics, Universidade Federal de Sergipe
  • Suzana V. Schönwald Sleep Laboratory, Neurology Medicine Division, Hospital de Clínicas de Porto Alegre
  • Guilherme Dellagustin Sleep Laboratory, Neurology Medicine Division, Hospital de Clínicas de Porto Alegre
  • José L. Rybarczyk Filho Department of Physics and Biophysics, Institute of Biosciences, Universidade Estadual Paulista Julio de Mesquita Filho

DOI:

https://doi.org/10.18226/23185279.v2iss1p15

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.

 

http://dx.doi.org/10.18226/23185279.v2iss1p15

Author Biography

Gunther J. L. Gerhardt, Universidade de Caxias do Sul

CCET, Department of Physics and Chemistry, Universidade de Caxias do Sul, Brazil

Published

2014-08-16

How to Cite

Gerhardt, G. J. L., Lemke, N., Carvalho, D. Z., de Santa-Helena, E. L., Schönwald, S. V., Dellagustin, G., & Rybarczyk Filho, J. L. (2014). Analysis of EEG Sleep Spindle Parameters from Apnea Patients Using Massive Computing and Decision Tree. Scientia Cum Industria, 2(1), 15–18. https://doi.org/10.18226/23185279.v2iss1p15

Issue

Section

Science, Education and Engineering