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A new near-lossless EEG compression method using ANN-based reconstruction technique.

Behzad Hejrati1, Abdolhossein Fathi1, Fardin Abdali-Mohammadi1

  • 1Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.

Computers in Biology and Medicine
|May 31, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive transform using artificial neural networks (ANNs) for compressing electroencephalogram (EEG) signals in telemedicine. The novel method achieves higher compression rates than existing techniques for non-stationary medical data.

Keywords:
AutoencoderDimensionality reductionEEG signalsNear-lossless compression

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Telemedicine systems require efficient compression of large medical signals.
  • Fixed transforms like DCT and wavelet struggle with non-stationary signals such as EEG.
  • Existing methods lack optimal redundancy extraction for complex medical data.

Purpose of the Study:

  • To develop a novel, near-lossless compression method for electroencephalogram (EEG) signals.
  • To improve compression rates for non-stationary medical data in telemedicine.
  • To introduce a learning-based adaptive transform for enhanced signal compression.

Main Methods:

  • Proposed a learning-based adaptive transform combining Discrete Cosine Transform (DCT) and Artificial Neural Network (ANN) reconstruction.
  • Applied the adaptive ANN-based transform to DCT coefficients of EEG data for dimensionality reduction and coefficient estimation.
  • Quantized the difference between original and estimated DCT coefficients, coding the error with Arithmetic coding.

Main Results:

  • The proposed adaptive transform demonstrated higher compression rates compared to state-of-the-art methods.
  • The method effectively reduced dimensionality and estimated original DCT coefficients.
  • Near-lossless compression was achieved with efficient coding of quantized errors.

Conclusions:

  • The developed adaptive ANN-based transform offers superior compression performance for EEG signals.
  • This approach enhances the efficiency of telemedicine systems by improving medical signal compression.
  • The method shows significant potential for handling non-stationary signals in medical data transmission.