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Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology
Autor:
Gupta, Teja, Wagh, Neeraj, Rawal, Samarth, Berry, Brent, Worrell, Gregory, Varatharajah, Yogatheesan
Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. The current clinical EEG review process relies heavily on expert visual review, which is unscalable and error-prone. In
Externí odkaz:
http://arxiv.org/abs/2211.13793
The recent availability of large datasets in bio-medicine has inspired the development of representation learning methods for multiple healthcare applications. Despite advances in predictive performance, the clinical utility of such methods is limite
Externí odkaz:
http://arxiv.org/abs/2209.11233
This paper presents a novel graph convolutional neural network (GCNN)-based approach for improving the diagnosis of neurological diseases using scalp-electroencephalograms (EEGs). Although EEG is one of the main tests used for neurological-disease di
Externí odkaz:
http://arxiv.org/abs/2011.12107
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