Journal Article10.1016/J.CMPB.2018.04.012
Automated EEG-based screening of depression using deep convolutional neural network.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli,D. P. Subha +7 more
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TL;DR: It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere, consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere.
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About: This article is published in Computer Methods and Programs in Biomedicine. The article was published on 01 Jul 2018. The article focuses on the topics: Electroencephalography & Deep learning.
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Citations
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
Shu Lih Oh,Yuki Hagiwara,U. Raghavendra,Rajamanickam Yuvaraj,N. Arunkumar,Murugappan Murugappan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +8 more
TL;DR: An automated detection system for Parkinson’s disease employing the convolutional neural network (CNN) employing the thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is proposed.
Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis
Haijun Lin,Jing Fang,Junpeng Zhang,Xuhui Zhang,Weiying Piao,Yukun Liu +5 more
TL;DR: This comparative analysis reviews traditional machine learning and deep learning methods for diagnosing depression using resting-state electroencephalograms, highlighting their effectiveness, challenges, and potential solutions to enhance diagnostic accuracy in computational psychiatry.
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges
Dimitrios Kollias
- 22 Feb 2022
TL;DR: The fourth Affective Behavior Analysis in-the-wild Competition, held in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2022 is described and the obtained results of the baseline systems and of all participating teams are illustrated.
Multitask-Based Temporal-Channelwise CNN for Parameter Prediction of Two-Phase Flows
TL;DR: This article develops a novel deep learning based soft measure technique to predict the gas void fraction, which is one key parameter in a gas–liquid two-phase flow.
Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
TL;DR: A regression model is built to predict the severity score of depressed patients as an extension of the deep-asymmetry method, which has shown promising performance in depression classification.
References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
•Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 07 Dec 2015
TL;DR: In this paper, a Parametric Rectified Linear Unit (PReLU) was proposed to improve model fitting with nearly zero extra computational cost and little overfitting risk, which achieved a 4.94% top-5 test error on ImageNet 2012 classification dataset.
•Posted Content
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.
15.1K
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin,Holger R. Roth,Mingchen Gao,Le Lu,Ziyue Xu,Isabella Nogues,Jianhua Yao,Daniel J. Mollura,Ronald M. Summers +8 more
TL;DR: Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
5.7K