Automatic recognition of vigilance state by using a wavelet-based artificial neural network

  • Authors:

  • Abdulhamit Subasi;Kemal Kiymik;Mehmet Akin;Osman Erogul

  • Affiliations:

  • Kahramanmaraş Sütçü İmam University, Department of Electrical and Electronics Engineering, 46100, Kahramanmaras, Turkey;Kahramanmaraş Sütçü İmam University, Department of Electrical and Electronics Engineering, 46100, Kahramanmaras, Turkey;Dicle University, Department of Electrical and Electronics Engineering, 46100, Diyarbakir, Turkey;Biomedical Engineering Center, Gulhane Military Medicine Academy, 46100, Ankara, Turkey

  • Venue:
  • Neural Computing and Applications
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this study, 5-s long sequences of full-spectrum electroencephalogram (EEG) recordings were used for classifying alert versus drowsy states in an arbitrary subject. EEG signals were obtained from 30 healthy subjects and the results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron (MLP), was used for the classification of EEG signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg–Marquardt algorithm was used to discriminate the alertness level of the subject. In order to determine the MLPNN inputs, spectral analysis of EEG signals was performed using the discrete wavelet transform (DWT) technique. The MLPNN was trained, cross-validated, and tested with training, cross-validation, and testing sets, respectively. The correct classification rate was 93.3% alert, 96.6% drowsy, and 90% sleep. The classification results showed that the MLPNN trained with the Levenberg–Marquardt algorithm was effective for discriminating the vigilance state of the subject.