medical-11

K-complex detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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MASS SS2

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Datasets

MaSS

Montreal Archive of Sleep Studies

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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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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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