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Brainwaves could hold key to depressive disorder diagnosis

AI algorithm to help streamline diagnosis of Major Depressive Disorder
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Affecting millions of people each year, Major Depressive Disorder is a debilitating illness that is often hard to diagnose. 

Now, a University of Southern Queensland researcher has joined a team of international scientists who are designing an AI algorithm to test for it. 

Information Systems Professor Raj Gururajan lent his expertise to the program in the hopes of making the diagnosis process less subjective. 

“With many of the mental health conditions, it can take time to find a diagnosis, and even when two professionals provide an expert opinion, they can arrive at different conclusions,” Professor Gururajan said. 

“The aim is to minimise this subjectivity and improve the accuracy of diagnosis.”

For the experiment, researchers analysed the electrical brain signals of 64 subjects who had undergone EEG testing in an earlier study.

Of the patients, 34 were known to have Major Depressive Disorder. 

These EEG results were then run through an AI model, which used a melamine pattern and discrete wavelet transform to find features that differentiated the two groups.

The most relevant features were then selected using neighbourhood component analysis and used as a classification system within a computer model. 

This model was then tested using the EEG data and was able to detect MDD with an accuracy greater than 95 per cent.

“This study is all about the accuracy of machine learning in detecting a particular condition,” Professor Gururajan said. 

“These results mean that in the future, a model similar to this could be used in clinics to help confirm the diagnosis of a psychiatrist.

“We still haven’t used the model on real-time patient data yet. That will be the next step.”

Professor Gururajan worked on the paper alongside Emrah Aydemir (Turkey), Turker Tuncer (Turkey), Sengul Dogan (Turkey) and U. Rajendra Acharya (Singapore).
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Raj Gururajan
Information Systems Professor Raj Gururajan.