Vascular dementia classification based on hilbert huang transform as feature extractor

Wan Siti Nur Shafiqa Wan Musa, Mohd Ibrahim Shapiai, Hilman Fauzi, Aznida Firzah Abdul Aziz

Research output: Contribution to journalArticle

Abstract

Impairment of cognitive and working memory after stroke was common. Vascular dementia (VaD) was a prevalent type of dementia that was caused by an impaired blood supply to the brain because of a series of small strokes. Electroencephalogram (EEG) gives information about brain status and activity, so it had a lot of potential to be used in diagnosing people with dementia. Since the EEG signal is extremely non-linear and non-stationary data, traditional Fourier analysis such as Fast Fourier Transform (FFT) that broadens sinusoidal signals cannot describe the amplitude contribution of each frequency value in specific time. Meanwhile, Hilbert Huang Transform (HHT) was based on the characteristic local time scale of the signal, it can efficiently obtain instantaneous frequency and instantaneous amplitude for nonstationary and nonlinear data. In this paper, HHT was employed as feature extraction method to extract the energy features of frequency bands from post stroke patients and healthy subjects. The extracted features were fed into extreme learning machine (ELM) for classifying post stroke patient with VaD and healthy subjects. The results of classification accuracy using HHT as feature extractor and FFT as feature extractor were compared. The mean accuracy of classification using HHT was 59.14%, respectively, while mean accuracy of classification using FFT was 94.4%, respectively, in classifying post stroke patient with VaD and healthy subjects.

Original languageEnglish
Pages (from-to)968-974
Number of pages7
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume17
Issue number2
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Hilbert-Huang Transform
Dementia
Extractor
Stroke
Mathematical transformations
Fast Fourier transforms
Fast Fourier transform
Electroencephalography
Brain
Fourier analysis
Extreme Learning Machine
Instantaneous Frequency
Working Memory
Frequency bands
Fourier Analysis
Local Time
Learning systems
Feature extraction
Blood
Feature Extraction

Keywords

  • Electroencephalogram
  • Hilbert huang transform
  • Vascular dementia

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

Vascular dementia classification based on hilbert huang transform as feature extractor. / Musa, Wan Siti Nur Shafiqa Wan; Shapiai, Mohd Ibrahim; Fauzi, Hilman; Aziz, Aznida Firzah Abdul.

In: Indonesian Journal of Electrical Engineering and Computer Science, Vol. 17, No. 2, 01.01.2019, p. 968-974.

Research output: Contribution to journalArticle

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