Wavelet feature extraction and J48 decision tree classification of auditory late response (ALR) elicited by transcranial magnetic stimulation

Wan Amirah W Azlan, Siaw Hong Liew, Yun Huoy Choo, Hazli Zakaria, Yin Fen Low

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Nowadays, transcranial magnetic stimulation (TMS) has been used to treat major depression and migraine. Integrating transcranial magnetic stimulation and electroencephalogram (TMS - EEG) may provide beneficial information. This paper introduces the experimental design, experimental setup and experimental procedures to differentiate the repetitive transcranial magnetic stimulation (rTMS) and without TMS over N100 (N1) and P200 (P2) peaks with regards to auditory attention. New experimental design, setup and procedures are developed to elicit N1 and P2 through the recording of EEG signal with the excitation of neurons from TMS and pure tones. Wavelet transform is implemented as feature extraction for the selected data. Four features are used for the classification. The classification is based on J48 decision tree performed using WEKA to distinguish between without TMS and rTMS. The result between without TMS and rTMS (in attention condition) showed 98.85% accuracy meanwhile between without TMS and rTMS (no attention condition) showed 99.46% accuracy.

Original languageEnglish
Pages (from-to)6319-6323
Number of pages5
JournalARPN Journal of Engineering and Applied Sciences
Volume11
Issue number10
Publication statusPublished - 20 May 2016

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Decision trees
Electroencephalography
Design of experiments
Feature extraction
Wavelet transforms
Neurons

Keywords

  • J48 decision tree
  • N100
  • P200
  • TMS-EEG
  • Wavelet transform

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Wavelet feature extraction and J48 decision tree classification of auditory late response (ALR) elicited by transcranial magnetic stimulation. / Azlan, Wan Amirah W; Liew, Siaw Hong; Choo, Yun Huoy; Zakaria, Hazli; Low, Yin Fen.

In: ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 10, 20.05.2016, p. 6319-6323.

Research output: Contribution to journalArticle

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