Major depressive disorder diagnosis based on PSD imaging of electroencephalogram EEG and AI

One of the most common causes of functional frailty is major depressive disorder (MDD). MDD is a chronic condition that requires long-term therapy and professional assistance. Additionally, MDD effective treatment requires early detection. Unfortunately, it has intricated clinical characteristics that make early diagnosis and treatment difficult for clinicians. Furthermore, there are currently no clinically effective diagnostic biomarkers that can confirm an MDD diagnosis. However, electroencephalogram (EEG) data from the brain have recently been used to make a quantitative diagnosis of MDD. In addition, As being among the most cutting-edge artificial intelligence (AI) technologies, deep learning (DL) has exhibited superior performance in a wide range of real-world applications, from computer vision to healthcare. However, an additional challenge could be the extraction of information from the ECG raw data. This paper presents a method for converting EEG data to power spectral density (PSD) images, and then they were classified as healthy or MDD using a deep neural network for feature extraction and a machine learning (ML) classifier. When employing the proposed approach, the images formed from the PSD show a considerably improved performance in classification results.

Artificial intelligence; Electroencephalogram; Machine learning; Major depressive disorder; Power spectral density