Wavelet-based Autoencoder and EfficientNet for Schizophrenia Detection from EEG Signals

TL;DR

This paper introduces a novel EEG-based framework combining wavelet-derived spectral scalograms with EfficientNet and autoencoders to improve schizophrenia detection accuracy, reducing computational complexity and capturing transient features.

Contribution

It presents a new approach integrating spectral scalograms with EfficientNet and convolutional autoencoders for enhanced schizophrenia classification from EEG signals.

Findings

Achieved 98.5% accuracy with CAE and soft voting classifier.

Achieved 99% accuracy using spectral scalograms with EfficientNet.

Outperformed traditional deep learning and transfer learning methods.

Abstract

Schizophrenia (SZ) is a complex mental disorder that necessitates accurate and timely diagnosis for effective treatment. Traditional methods for SZ classification often struggle to capture transient EEG features and face high computational complexity. This study proposes a convolutional autoencoder (CAE) to address these challenges by reducing dimensionality and computational complexity. Additionally, we introduce a novel approach utilizing spectral scalograms (SS) combined with EfficientNet (ENB) architectures. The SS, obtained through continuous wavelet transform, reveals temporal and spectral information of EEG signals, aiding in the identification of transient features. ENB models, through transfer learning (TL), extract discriminative features and improve SZ classification accuracy. Experimental evaluation on a comprehensive dataset demonstrates the efficacy of our approach, achieving a five-fold mean cross-validation accuracy of 98.5\% using CAE with a soft voting classifier and 99\% employing SS with the ENB7 model. These results suggest the potential of our methods to enhance SZ diagnosis, surpassing traditional deep learning (DL) and TL techniques. By leveraging CAE and ENBs, this research offers a robust framework for objective SZ classification, promoting early intervention and improved patient outcomes.