Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

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, behavior, 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-series 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 in Internet of Medical Things (IoMT) framework for smart healthcare and remote monitoring applications.

Schizophrenia Detection on EEG Signals Using an Ensemble of a Lightweight Convolutional Neural Network

“…Traditionally, neurologists review EEG signals manually, which is time-consuming and subjective. Our approach simplifies this by using a type of deep learning model known as a convolutional neural network (CNN), specifically designed to handle the data format of EEG signals, which has shown promising results over traditional methods based on hand-crafted features [11,15,25].…”

“…The best accuracy was achieved with 1D-CNN-LSTM involving ReLU activation. Sharma et al [11] also introduced a method based on the CNN and LSTM models. Supakar et al [12] built an LSTM model to classify EEG signals as healthy control or schizophrenia.…”

“…A close look at the existing methods reveals that, firstly, the complex deep models were used with relatively small datasets, which is an indicator of the overfitting problem; the methods in [11,14,15,22,24,25] achieved accuracies of 98.07%, 99%, 99.25%, 99.9%, 96.83%, and 99.5%, respectively. Moreover, some studies [11][12][13][14]16,21,22,24,27] used EEG trials of long temporal lengths in a range between 20 to 60 s, which requires more computation and time. Also, in [23], the authors adopted only two channels, FP1 and FP2, without providing enough information on why they chose them.…”

“…To train our method, we used a benchmark dataset of EEG recordings at a resting state, which is publicly available [32] and has been used in recent works for schizophrenia detection [11,[14][15][16]. This dataset contains EEG signals of 84 subjects.…”

“…The limitations of traditional machine learning techniques and the outstanding performance of deep learning in various applications motivated the use of deep learning for schizophrenia detection, and many deep learning-based techniques have been proposed [11][12][13][14][15][16]. The performance of these techniques is good; they are prone to overfitting because they have highly complex architectures involving a large number of learnable parameters, but the available data for schizophrenia detection are limited.…”