Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare
Geetanjali Sharma, Amit M. Joshi

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…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies · ECG Monitoring and Analysis
MethodsTanh Activation · Sigmoid Activation · Long Short-Term Memory
