According to World Health Organization (WHO) report, every 40 seconds a person attempts suicide globally. Depression, one of the world's most prevailing diseases has become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years various machine learning and advanced neurocomputing techniques are being utilized in Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various Machine Learning and one Deep Learning approach. The majority of previous EEG based MDD decoding research has concentrated on a limited features. It was necessary to conduct in-depth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which is then further compared against the state of the art end to end techniques. The K-nearest neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%. While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention.

According to World Health Organization (WHO) report, every 40 seconds a person attempts suicide globally. Depression, one of the world’s most prevailing diseases has become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years various machine learning and advanced neurocomputing techniques are being utilized in Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various Machine Learning and one Deep Learning approach. The majority of previous EEG based MDD decoding research has concentrated on a limited features. It was necessary to conduct in-depth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which is then further compared against the state of the art end to end techniques. The K-nearest neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%. While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention.

Depression and several other behavioral health disorders are serious public health concerns worldwide. Persistent behavioral health issues have a wide range of consequences that affect people personally, culturally and socially. Major depressive disorder (MDD) is a psychiatric ailment that affects people of all ages worldwide. It has grown into a major global health issue as well as an economic burden. Clinicians are using several medications to limit the growth of this disease at an early stage in young people. The goal of this research is to improve the depression diagnosis by altering Electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99.7%, while Support vector machine (SVM) had acquired 95.7% accuracy. The developed Deep Learning approach, convolution neural network (CNN), achieved 99.6% accuracy. With these promising results, this study establishes the viability of an Electroencephalogram based diagnosis of MDD.

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