Treffer: Non-Invasive Pre-Diagnosis Implementation of Psychiatric Mental Disorders from EEG Bio-Signal Data Using Ensemble Deep Learning: A Comparative Analysis.

Studies about detection of psychiatric diseases area have gained higher importance among science, engineering and medical areas. There have been a lot of different and unique types of mental problems/diseases and some of them could be seen very common among people worldwide. There were some ways to analyze and interpretate the mental disorders such as from neuroimages, EEGs and other outputs of radiological types of imaging systems. This research aims analyzing and pre-diagnosis of Alzheimer's disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD) from an open source and publicly available EEG dataset by performing specific ensemble deep learning models to create an automated medical image analysis Computer Aided Diagnosis (CAD) system in detail. In medical area, fundamentally, the current diagnostic methods were time-consuming, subjective and needed detailed knowledge. To address and overcome these limitations, improving the diagnostic procedure in a fast way, we proposed a developed version of pre-diagnosis with using the development of a deep neural network system capable of accurately and efficiently analyzing biological signal data. Moreover, three different deep learning models of ResNet50, VGG19 and InceptionV3 were applied to the three separate mental disease groups and for this phase EEG signals were converted to the spectrogram images and used in detail. The models were extensively trained on the pre-processed image dataset and evaluated using multiple accuracy metrics. To improve diagnostic accuracy and efficiency, the trained models were combined using an ensemble approach and incorporated into an intuitive MATLAB software version. The most remarkable accomplishment of this study was the InceptionV3 model, which attained an impressive 99.47% for AD and ADHD discrimination via bio-medical signal processing. These findings highlight the significant potential for making the models sufficient to pre-diagnose during the clinical progress for neurologists, brain surgery area and other related doctors/clinicians. [ABSTRACT FROM AUTHOR]

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