Application of entropy for automated detection of neurological disorders with electroencephalogram signals: a review of the last decade (2012-2022)
An automated Neurological Disorder detection system can be considered as a cost-effective
and resource efficient tool for medical and healthcare applications. In automated …
and resource efficient tool for medical and healthcare applications. In automated …
A multi-modal open dataset for mental-disorder analysis
According to the WHO, the number of mental disorder patients, especially depression
patients, has overgrown and become a leading contributor to the global burden of disease …
patients, has overgrown and become a leading contributor to the global burden of disease …
Artificial intelligence for brain disease diagnosis using electroencephalogram signals
S Shang, Y Shi, Y Zhang, M Liu, H Zhang… - Journal of Zhejiang …, 2024 - Springer
Brain signals refer to electrical signals or metabolic changes that occur as a consequence of
brain cell activity. Among the various non-invasive measurement methods …
brain cell activity. Among the various non-invasive measurement methods …
Benchmarks for machine learning in depression discrimination using electroencephalography signals
Diagnosis of depression using electroencephalography (EEG) is an emerging field of study.
When mental health facilities are unavailable, the use of EEG as an objective measure for …
When mental health facilities are unavailable, the use of EEG as an objective measure for …
Emotion Recognition from Few-Channel EEG Signals by Integrating Deep Feature Aggregation and Transfer Learning
Electroencephalogram (EEG) signals have been widely studied in human emotion
recognition. The majority of existing EEG emotion recognition algorithms utilize dozens or …
recognition. The majority of existing EEG emotion recognition algorithms utilize dozens or …
Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and …
In this day and age, depression is still one of the biggest problems in the world. If left
untreated, it can lead to suicidal thoughts and attempts. There is a need for proper …
untreated, it can lead to suicidal thoughts and attempts. There is a need for proper …
EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
B Wang, Y Kang, D Huo, G Feng, J Zhang… - Frontiers in physiology, 2022 - frontiersin.org
Depression is an undetectable mental disease. Most of the patients with depressive
symptoms do not know that they are suffering from depression. Since the novel Coronavirus …
symptoms do not know that they are suffering from depression. Since the novel Coronavirus …
Privacy Preserving Collaboratively Training Framework for Classification of Major Depressive Disorder using Non-IID Three Channel Electroencephalogram
Abstract Major Depressive Disorder (MDD) is characterized by low mood, loss of interest
and even suicidal ideation. Electroencephalogram based diagnosis of a variety of …
and even suicidal ideation. Electroencephalogram based diagnosis of a variety of …
Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012-2022)
An automated Neurological Disorder detection system can be considered as a cost-effective
and resource efficient tool for medical and healthcare applications. In automated …
and resource efficient tool for medical and healthcare applications. In automated …
EEG-Based Depression Detection: A Temporal Domain Feature-Centric Machine Learning Approach
Depression stands as a significant mental health ailment impacting countless individuals
globally. It is a mental condition, but it can harm the physical well-being of an individual …
globally. It is a mental condition, but it can harm the physical well-being of an individual …