output
20092026
most citedA Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

2.7k citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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4 papers · 1 filter

eess.SP2023★ 1 cited

EEG-based Sleep Staging with Hybrid Attention

Xinliang Zhou, Chenyu Liu, Jiaping Xiao +1

Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, capturing both the spatial and temporal relationships within electroencephalogram (EE…

eess.SP2021★ 154 cited

Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars +7

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electr…

eess.SP2021★ 42 cited

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Javad Hassannataj Joloudari, Sanaz Mojrian, Issa Nodehi +13

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals…

eess.SP2019★ 533 cited

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya

Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep expert…