medical-6

Sleep Micro-event detection

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal

Alexandre Gramfort, Valentin Thorey, E. Mignot, Stanislas Chambon, P. Arnal•Thu Dec 06 2018

A novel deep learning architecture called Dreem One Shot Event Detector (DOSED) is proposed, inspired by object detectors developed for computer vision such as YOLO and SSD, which predicts locations, durations and types of events in EEG time series.

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DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

Y. Guan, Hongyang Li•Wed Aug 28 2019

DeepSleep is presented, which ranked first in the 2018 PhysioNet Challenge for automatically segmenting sleep arousal regions based on polysomnographic recordings, and features accurate, high-resolution, and fast delineation of sleep arousals.

2 0

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RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection

Nicol'as I. Tapia, P. Est'evez•Thu May 14 2020

A deep learning approach based on convolutional and recurrent neural networks for sleep EEG event detection called Recurrent Event Detector (RED), which is event-agnostic and can be used directly to detect other types of sleep events.

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DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning

R. Fonod•Mon Feb 28 2022

Results suggest a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable in DeepSleep 2.0.

4 0

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Advanced sleep spindle identification with neural networks

S. Bialonski, L. Kaulen, J. Schwabedal, Jules Schneider, P. Ritter•Sat Feb 05 2022

A U-Net-type deep neural network model is presented that exceeds that of the state-of-the-art detector and of most experts in the MODA dataset and shows improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably.

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