Abstract
In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
Highlights
Schizophrenia is a severe mental disorder that compromises significantly many aspects of the quality of life and affects more than 20 million people worldwide (Insel, 2010)
We can mention the use of Interpretable Machine Learning for Schizophrenia long short-term memory recurrent neural networks (RNNs) to classify diagnoses from pediatric intensive care unit data (Lipton et al, 2015), the use of RNNs and Bayesian models to discriminate patients with ovarian cancer (Mariño et al, 2017; Vázquez et al, 2018), the use of support vector machines (SVMs) for attention deficit hyperactivity disorder prediction (Dai et al, 2012), the application of convolutional neural networks (CNNs) to classifying electroencephalogram (EEG) signals for emotion recognition (Luo et al, 2020), or the combination of multilayer perceptrons and SVMs to diagnose major depressive disorders (Saeedi et al, 2020b)
In this work we have tackled the problem of assessing whether a subject suffers from schizophrenia or not by analyzing their recorded EEG signals
Summary
Schizophrenia is a severe mental disorder that compromises significantly many aspects of the quality of life and affects more than 20 million people worldwide (Insel, 2010). While schizophrenia is known to have an effect on the activity of the brain (Rubinov et al, 2009), other mental disorders such as, e.g., obsessive compulsive disorder, attention deficit hyperactivity disorder, or bipolar disorder produce similar variations in the baseline brain activity (Anier et al, 2012). Mental diseases such as bipolar disorder or major depressive disorder are often confused with schizophrenia. We can mention the use of Interpretable Machine Learning for Schizophrenia long short-term memory recurrent neural networks (RNNs) to classify diagnoses from pediatric intensive care unit data (Lipton et al, 2015), the use of RNNs and Bayesian models to discriminate patients with ovarian cancer (Mariño et al, 2017; Vázquez et al, 2018), the use of support vector machines (SVMs) for attention deficit hyperactivity disorder prediction (Dai et al, 2012), the application of convolutional neural networks (CNNs) to classifying electroencephalogram (EEG) signals for emotion recognition (Luo et al, 2020), or the combination of multilayer perceptrons and SVMs to diagnose major depressive disorders (Saeedi et al, 2020b)
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