  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.
