Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review
Depression is the most common mental illness, which has become the major cause of fear
and suicidal mortality or tendencies. Currently, about 10% of the world population has been …
and suicidal mortality or tendencies. Currently, about 10% of the world population has been …
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features
Schizophrenia is a mental disorder that causes adverse effects on the mental capacity of a
person, emotional inclinations, and quality of personal and social life. The official statistics …
person, emotional inclinations, and quality of personal and social life. The official statistics …
Alcoholic EEG signals recognition based on phase space dynamic and geometrical features
Alcoholism is a severe disorder that leads to brain problems and associated cognitive,
emotional and behavioral impairments. This disorder is typically diagnosed by a …
emotional and behavioral impairments. This disorder is typically diagnosed by a …
EEG based depression recognition using improved graph convolutional neural network
J Zhu, C Jiang, J Chen, X Lin, R Yu, X Li… - Computers in Biology and …, 2022 - Elsevier
Depression is a global psychological disease that does serious harm to people. Traditional
diagnostic method of the doctor-patient communication, is not objective and accurate …
diagnostic method of the doctor-patient communication, is not objective and accurate …
Automated detection of Alzheimer's via hybrid classical quantum neural networks
Deep Neural Networks have offered numerous innovative solutions to brain-related
diseases including Alzheimer's. However, there are still a few standpoints in terms of …
diseases including Alzheimer's. However, there are still a few standpoints in terms of …
Resting-state EEG signal for major depressive disorder detection: a systematic validation on a large and diverse dataset
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes
of disability. Machine learning combined with non-invasive electroencephalography (EEG) …
of disability. Machine learning combined with non-invasive electroencephalography (EEG) …
Early-stage Alzheimer's disease categorization using PET neuroimaging modality and convolutional neural networks in the 2D and 3D domains
Alzheimer's Disease (AD) is a health apprehension of significant proportions that is
negatively impacting the ageing population globally. It is characterized by neuronal loss and …
negatively impacting the ageing population globally. It is characterized by neuronal loss and …
Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain
Electroencephalography (EEG) signals are considered one of the oldest techniques for
detecting disorders in medical signal processing. However, brain complexity and the non …
detecting disorders in medical signal processing. However, brain complexity and the non …
Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features
SI Khan, SM Qaisar, RB Pachori - Biomedical Signal Processing and …, 2022 - Elsevier
The growing prevalence and high mortality rate due to valvular heart diseases (VHD) are
concerned. Therefore, its accurate, rapid, and early diagnosis is important. This study …
concerned. Therefore, its accurate, rapid, and early diagnosis is important. This study …