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Automated ASD detection using hybrid deep lightweight features extracted from EEG signals.

Mehmet Baygin1, Sengul Dogan2, Turker Tuncer2

  • 1Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey.

Computers in Biology and Medicine
|June 13, 2021
PubMed
Summary

This study developed an automated autism detection model using electroencephalogram (EEG) signals. The hybrid deep learning approach achieved 96.44% accuracy, offering a valuable tool for early autism diagnosis.

Keywords:
1D_LBP-STFTAutism classificationHybrid lightweight deep feature generatorReliefF(2)Transfer learning

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Area of Science:

  • Neuroscience
  • Medical Technology
  • Artificial Intelligence

Background:

  • Autism spectrum disorder affects approximately 1 in 54 children.
  • Electroencephalogram (EEG) signals exhibit distinct patterns in children with autism.
  • Automated detection models can aid in identifying autism spectrum disorder.

Purpose of the Study:

  • To design and implement an automated autism detection model using EEG signals.
  • To develop a hybrid lightweight deep feature extractor for high classification performance.
  • To create an adjunct tool for neurologists in autism diagnosis.

Main Methods:

  • A novel signal-to-image conversion using 1D_LBP and STFT to generate spectrograms.
  • Hybrid deep feature extraction combining MobileNetV2, ShuffleNet, and SqueezeNet.
  • Feature selection using a two-layered ReliefF algorithm and shallow classifiers with 10-fold cross-validation.

Main Results:

  • A Support Vector Machine (SVM) classifier achieved 96.44% accuracy.
  • The proposed model demonstrated high classification performance on a large EEG dataset.
  • Features extracted by the hybrid deep lightweight feature extractor were highly discriminative.

Conclusions:

  • The hybrid deep lightweight feature extractor is effective for autism detection via EEG signals.
  • The developed model shows potential as an adjunct diagnostic tool in clinical settings.
  • This approach facilitates earlier and more accurate autism diagnosis.