Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Abstract

Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression. Resting-state EEG signals were recorded from 41 patients with depression and 34 healthy controls. Multiple dimensional characteristics were extracted, including power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI). These three different dimensional characteristics with statistical differences between two groups were ranked by three machine learning algorithms. Then, the ranked characteristics were placed into the classifiers according to the importance of features to obtain the optimal feature subset with the highest classification accuracy. The results showed that the optimal feature subset contained 86 features with the highest classification accuracy of 98.54% ± 0.21%. According to the statistics of the optimal feature subset, PLI had the largest number of features among the three categories, and the number of beta features was bigger than other rhythms. Moreover, compared to the healthy controls, the PLI values in the depression group increased in theta and beta rhythms, but decreased in alpha1 and alpha2 rhythms. The PSD of theta and beta rhythms were significantly greater in depression group than that in healthy controls, and the FE of beta rhythm showed the same trend. These findings indicate that the distribution of abnormal multidimensional features is potentially useful for the diagnosis of depression and understanding of neural mechanisms.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

“…Previous research indicated that individuals with DD have altered functional connectivity in the frontal cortex [19]. Based on the findings of our preceding research, this study proposed to use frontal six-channel EEG signals in conjunction with a deep learning algorithm to diagnose DD, which considerably simplifies data collection efforts and improves the practicability of DD screening.…”

“…It has been reported that EEG signals have apparent changes in different frequency bands and regions in patients with DD [27][28][29]. Based on our brain functional mechanism of DD [19], it has been found that the important neuro-electrophysiological characteristics of DD are mainly distributed in the frontal region of the brain. Meanwhile, the response effects of antidepressant drugs are also related to the dynamic change in EEG power in the frontal region [30,31].…”

“…Coryell et al evaluated the volume of the left side of the frontal cortex in 10 DD patients and 10 HCs and concluded that patients with severe depressive disorder were more likely to have an increase in the posterior frontal cortex [46]. There is rising evidence that functional connections in the frontal regions of DD are dramatically altered [19]. Therefore, this study advocated for DD diagnosis using the brain's six frontal EEG channels in conjunction with a current sophisticated deep learning algorithm to attain high accuracy and practicability.…”

“…The results of this study showed that the accuracy obtained in beta rhythm was significantly higher than those in theta rhythm, alpha1 rhythm, and alpha2 rhythm for DD recognition, which revealed that beta rhythm was significantly changed in patients with DD. It is well known that beta rhythms are commonly associated with brain alertness, attentional states, and emotions [19,61]. It has been found that beta EEG power is increased in DD patients compared to HCs and plays an important role in the pathogenesis of DD [62].…”

“…Neuroimaging techniques have been widely used to explore the alterations in brain functions in recent years, such as electroencephalogram (EEG) [19], magnetoencephalogram (MEG) [20], functional magnetic resonance imaging (fMRI) [21], etc. These brain imaging techniques have also been applied to understanding the neuromechanisms and realizing the automated intelligent diagnosis of mental disorders [22][23][24].…”