A review of methods for sleep arousal detection using polysomnographic signals
Multiple types of sleep arousal account for a large proportion of the causes of sleep
disorders. The detection of sleep arousals is very important for diagnosing sleep disorders …
disorders. The detection of sleep arousals is very important for diagnosing sleep disorders …
Deep Learning provides exceptional accuracy to ECoG-based Functional Language Mapping for epilepsy surgery
The success of surgical resection in epilepsy patients depends on preserving functionally
critical brain regions, while removing pathological tissues. Being the gold standard, electro …
critical brain regions, while removing pathological tissues. Being the gold standard, electro …
RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application
W Lu, Z Li, Y Li, J Li, Z Chen, Y Feng, H Wang… - Frontiers in …, 2022 - frontiersin.org
Symptoms of vertigo are frequently reported and are usually accompanied by eye-
movements called nystagmus. In this article, we designed a three-dimensional nystagmus …
movements called nystagmus. In this article, we designed a three-dimensional nystagmus …
DeepSleep 2.0: automated sleep arousal segmentation via deep learning
R Fonod - AI, 2022 - mdpi.com
DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully
convolutional deep neural network, which achieved the highest unofficial score in the 2018 …
convolutional deep neural network, which achieved the highest unofficial score in the 2018 …