A novel EEG compression method combining zigzag and STW achieved a compression ratio of 89.30 and a depression recognition accuracy of 90.2%, occasionally outperforming original signals.
Does a novel EEG compression method preserve depression recognition accuracy compared to uncompressed signals in subjects from the HUSM dataset?
A novel EEG compression method using wavelet transform and adaptive filtering achieves high compression ratios while preserving depression recognition accuracy at 90.2%.
ABSTRACT Electroencephalogram (EEG) signals are critical for diagnosing neurological disorders like depression but require substantial storage, posing challenges for telemedicine. This paper proposes a novel lossy compression method integrating two‐dimensional zigzag and spiral rearrangement, wavelet transform (bior4.4), and adaptive filtering to minimise storage while preserving depression recognition accuracy. Using the Hospital University Sains Malaysia (HUSM) dataset (34 depressed, 30 healthy subjects), EEG signals were converted into 2D matrices, compressed with four wavelet encoders (SPIHT, STW, EZW, LVL‐MMC), and reconstructed. A feedforward artificial neural network (ANN) with 80 features (relative wavelet energy and entropy) classified depressed and healthy individuals. The combination of zigzag and STW methods achieved a compression ratio (CR) of 89.30, a percentage root‐mean‐square distortion (PRD) of 0.23, and a peak signal‐to‐noise ratio (PSNR) of 58.80 dB, with depression recognition accuracy of 90.2%. Classification was performed on both original and reconstructed signals to evaluate the effect of compression on diagnostic performance. Notably, compressed signals occasionally outperformed original signals, likely due to noise reduction. This method offers a robust solution for efficient EEG storage and accurate depression diagnosis in resource‐constrained settings.
Taleghani et al. (Thu,) conducted a other in Depression (n=64). Lossy compression method integrating 2D zigzag rearrangement, wavelet transform, and adaptive filtering vs. Original uncompressed EEG signals was evaluated on Depression recognition accuracy. A novel EEG compression method combining zigzag and STW achieved a compression ratio of 89.30 and a depression recognition accuracy of 90.2%, occasionally outperforming original signals.