Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

TL;DR

This paper introduces a hybrid deep learning model combining CNN and LSTM for EEG-based schizophrenia detection, achieving high accuracy and suitability for IoMT-enabled remote healthcare monitoring.

Contribution

A novel hybrid neural network model (SzHNN) that improves EEG schizophrenia detection accuracy and reduces electrode requirements, integrated within an IoMT framework for smart healthcare.

Findings

Achieved 99.9% classification accuracy with the hybrid model.

Demonstrated effective detection with only 5 electrodes, 91% accuracy.

Validated the model's performance across multiple datasets and parameters.

Abstract

In the field of neuroscience, Brain activity analysis is always considered as an important area. Schizophrenia(Sz) is a brain disorder that severely affects the thinking, behaviour, and feelings of people all around the world. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection. EEG is a non-linear time-seriesi signal and utilizing it for investigation is rather crucial due to its non-linear structure. This paper aims to improve the performance of EEG based Sz detection using a deep learning approach. A novel hybrid deep learning model known as SzHNN (Schizophrenia Hybrid Neural Network), a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) has been proposed. CNN network is used for local feature extraction and LSTM has been utilized for classification. The proposed model has been compared with CNN only, LSTM only, and machine learning-based models. All the models have been evaluated on two different datasets wherein Dataset 1 consists of 19 subjects and Dataset 2 consists of 16 subjects. Several experiments have been conducted for the same using various parametric settings on different frequency bands and using different sets of electrodes on the scalp. Based on all the experiments, it is evident that the proposed hybrid model (SzHNN) provides the highest classification accuracy of 99.9% in comparison to other existing models. The proposed model overcomes the influence of different frequency bands and even showed a much better accuracy of 91% with only 5 electrodes. The proposed model is also evaluated on the Internet of Medical Things (IoMT) framework for smart healthcare and remote monitoring applications.