Non-invasive EEG based Feature Extraction framework for Major Depressive Disorder analysis

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. Full Tex

Non-invasive EEG based Feature Extraction framework for Major Depressive Disorder analysis

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. Full Tex

Flexible Fluidic-Type Strain Sensors for Wearable and Robotic Applications Fabricated with Novel Conductive Liquids: A Review

Flexible strain sensors with high sensitivity, wide sensing range, and excellent long-term stability are highly anticipated due to their promising potential in user-friendly electronic skins, interactive wearable systems, and robotics. Fortunately, there have been more flexible sensing materials developed during the past few decades, and some important milestones have been reached. Among the various strain sensing approaches, liquid-type (fluidic type) sensing has attracted great attention due to its appealing qualities, including its high flexibility, broad electrochemical window, variety in design, minimal saturated vapor pressure, and outstanding solubility. This review provides the comprehensive and systematic development of fluidic-type flexible strain sensors, especially in the past 10 years, with a focus on various types of liquids used, fabrication methods, channel structures, and their wide-range applications in wearable devices and robotics. Furthermore, it is believed that this work will be of great help to young researchers looking for a detailed study on fluidic strain sensors