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Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional

Berna Ari1, Nebras Sobahi2, Ömer F Alçin3

  • 1Firat University, Technology Faculty, Electrical and Electronics Engineering Department, Elazig, Turkey.

Computers in Biology and Medicine
|February 14, 2022
PubMed
Summary

This study introduces an automated method using deep learning and signal processing to detect Autism Spectrum Disorder (ASD) from EEG data, achieving high accuracy. The novel approach offers a faster, more reliable alternative to manual screening for early ASD identification.

Keywords:
Autism spectrum disorderDeep learningDouglas-Peucker algorithmEEG signals

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Autism Spectrum Disorder (ASD) presents complex neurological challenges evident in early childhood.
  • Electroencephalogram (EEG) signals are crucial for monitoring brain activity.
  • Manual ASD screening from EEG is error-prone, time-consuming, and labor-intensive.

Purpose of the Study:

  • To develop and validate a novel automated method for detecting ASD using EEG signals.
  • To improve the efficiency and accuracy of ASD diagnosis through advanced signal processing and machine learning.

Main Methods:

  • Utilized the Douglas-Peucker (DP) algorithm for EEG signal simplification.
  • Applied wavelet transform for EEG rhythm extraction and sparse coding with the matching pursuit algorithm.
  • Employed Extreme Learning Machines (ELM)-based autoencoders for data augmentation.
  • Classified EEG signals using pre-trained deep convolutional neural networks (CNNs).

Main Results:

  • Achieved a high accuracy of 98.88% in automated ASD detection.
  • Demonstrated excellent performance with 100% sensitivity and 96.4% specificity.
  • Obtained a significant F1-score of 99.19%, indicating robust classification.

Conclusions:

  • The proposed automated method effectively detects ASD from EEG recordings with high precision.
  • This AI-driven approach offers a promising, efficient, and accurate tool for clinical ASD screening.
  • Further validation with larger EEG datasets is recommended before widespread clinical implementation.