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
Deep learning techniques for suicide and depression detection from online social media: A scoping review
Anshu Malhotra,Rajni Jindal +1 more
TL;DR: In this paper , the authors conducted a systematic literature review (SLR) of 96 relevant research studies published until date that have applied deep learning techniques for detecting depression and suicide or self-harm behavior from social media content.
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A survey of brain network analysis by electroencephalographic signals
Cuihua Luo,Cuihua Luo,Fali Li,Peiyang Li,Chanlin Yi,Chunbo Li,Qin Tao,Xiabing Zhang,Yajing Si,Dezhong Yao,Gang Yin,Pengyun Song,Pengyun Song,Huazhang Wang,Huazhang Wang,Peng Xu +15 more
TL;DR: The relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances are explored and some innovations and future challenges in the end are discussed.
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Model uncertainty quantification for diagnosis of each main coronary artery stenosis
Roohallah Alizadehsani,Mohamad Roshanzamir,Moloud Abdar,Adham Beykikhoshk,Mohammad Hossein Zangooei,Abbas Khosravi,Saeid Nahavandi,Ru San Tan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +10 more
- 29 May 2020
TL;DR: High diagnostic performance of the proposed method for diagnosis of stenosis in individual RCA, LCX, and LAD is demonstrated, achieving accuracy rates of 82.67%, 83.67% and 86.43%, respectively, which is the best performance of ML techniques applied to the Z-Alizadeh Sani dataset.
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
TL;DR: A review of the state-of-the-art research on machine learning techniques used for detection and classification of Alzheimer's disease with a focus on neuroimaging and primarily journal articles published since 2016 can be found in this paper .
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Deep learning for depression recognition with audiovisual cues: A review
Chenguang Guo,Lang He,Mingyue Niu,Marten Dooper,Prayag Tiwari,Pekka Marttinen,Rui Su,Jiewei Jiang,Chenguang Guo,Hongyu Wang,Songtao Ding,Zhongmin Wang,Xiaoying Pan,Wei Dang +13 more
TL;DR: In this article, a review of the DL methods for automatic detection of depression to extract a representation of depression from audio and video is presented. And the challenges and promising directions related to the automatic diagnoses of depression using DL are discussed.
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