Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals
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SCIENTIFIC PAPER
Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals Ahmad Shalbaf1 · Sara Bagherzadeh2 · Arash Maghsoudi2 Received: 9 July 2020 / Accepted: 8 September 2020 © Australasian College of Physical Scientists and Engineers in Medicine 2020
Abstract Schizophrenia (SZ) is a severe disorder of the human brain which disturbs behavioral characteristics such as interruption in thinking, memory, perception, speech and other living activities. If the patient suffering from SZ is not diagnosed and treated in the early stages, damage to human behavioral abilities in its later stages could become more severe. Therefore, early discovery of SZ may help to cure or limit the effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as SZ due to having high temporal resolution information, and being a noninvasive and inexpensive method. This paper introduces an automatic methodology based on transfer learning with deep convolutional neural networks (CNNs) for the diagnosis of SZ patients from healthy controls. First, EEG signals are converted into images by applying a time–frequency approach called continuous wavelet transform (CWT) method. Then, the images of EEG signals are applied to the four popular pre-trained CNNs: AlexNet, ResNet-18, VGG-19 and Inception-v3. The output of convolutional and pooling layers of these models are used as deep features and are fed into the support vector machine (SVM) classifier. We have tuned the parameters of SVM to classify SZ patients and healthy subjects. The efficiency of the proposed method is evaluated on EEG signals from 14 healthy subjects and 14 SZ patients. The experiments showed that the combination of frontal, central, parietal, and occipital regions applied to the ResNet-18-SVM achieved best results with accuracy, sensitivity and specificity of 98.60% ± 2.29, 99.65% ± 2.35 and 96.92% ± 2.25, respectively. Therefore, the proposed method as a diagnostic tool can help clinicians in detection of the SZ patients for early diagnosis and treatment. Keywords Schizophrenia · Electroencephalogram · Transfer learning · Convolutional neural network · Continuous wavelet transform
Introduction Schizophrenia (SZ) is a severe disorder of the brain which affects the thinking, memory, understanding, speech, and the behavioral characteristics of an individual [1, 2]. This chronic psychiatric disorder affects the employment, marriage and lifestyle of the person [3, 4] and consequently quality of life is then compromised, being unable to function in workplaces, with 20–40% attempting suicide at least once [5]. The World Health Organization (WHO) reports that 20 * Arash Maghsoudi [email protected] 1
Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Department of Biomedical Engineering, Science and Research Brach, Islamic Azad University, Tehran, Iran
2
million people worldwide are affected by this
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