Bridging the Gap: Deep Learning EEG-based applications for Schizophrenia Classification and Management

Abstract

Schizophrenia, a multifaceted and debilitating mental disorder, demands early and accurate diagnosis to enhance treatment outcomes. This paper presents a comprehensive study exploring the potential of deep learning (DL) models for automating schizophrenia diagnosis using electroencephalography (EEG) data. The research encompasses EEG signal acquisition, preprocessing involving normalization and filtering, and the deployment of cutting-edge DL techniques, including 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) networks, and their fusion in a CNN-LSTM architecture. The paper also presents the benefits and implications of the personalized management of schizophrenia based on remote health monitoring which may improve treatment effectiveness and the overall well-being of patients.

Authors

* External Author