Sleep spindles detecton using short time fourier transform and neural networks

GORUR D., Halici U., AYDIN H., ONGUN G., ÖZGEN F., Leblebicioglu K.

International Joint Conference on Neural Networks (IJCNN 02), Hawaii, United States Of America, 12 - 17 May 2002, pp.1631-1636 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ijcnn.2002.1007762
  • City: Hawaii
  • Country: United States Of America
  • Page Numbers: pp.1631-1636
  • Keywords: sleep spindles, short time Fourier transform, Multi Layer Perceptron, Support Vector Machine, ORGANIZATION, BRAIN
  • Middle East Technical University Affiliated: Yes

Abstract

Sleep spindles are a hallmark of the stage 2 sleep. Their distribution over the non-REM sleep is clinically important. In this paper, a method that detects the sleep spindles in sleep EEG is proposed. Short time Fourier transform is used for feature extraction. Both multilayer perceptron and Support Vector Machine are utilized in detection of the spindles in sleep EEG for comparison. The classification performance of MLP is found to be 88.7% and that of SVM as 95.4%. It should be noted that there might be differences also in visual scoring by experts, so the results obtained are quite satisfactory.