medical-8

Spindle Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in spindle-detection-8

Trend

Dataset

Best Model

Actions

MASS SS2

Stanford Sleep Cohort (SSC)

Wisconsin Sleep Cohort (WSC)

Libraries

i

Use these libraries to find spindle-detection-8 models and implementations

Datasets

MaSS

Montreal Archive of Sleep Studies

MODA dataset

Subtasks

No subtasks available.

Most implemented papers

Multichannel sleep spindle detection using sparse low-rank optimization

A. Parekh, I. Selesnick, R. Osorio, A. Varga, D. Rapoport, I. Ayappa•Sun May 14 2017

The proposed method aims to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindles in EEG channels other than the central channel visible on a digital screen.

26

Content

Wisconsin Sleep Cohort (WSC)

MODA dataset

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Paper Graph

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.

18 0

Paper Graph

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.

72 0

Paper Graph

The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation

Nicolas Valenchon, Yann Bouteiller, Hugo R. Jourde, Emily B. J. Coffey, G. Beltrame•Tue Jul 27 2021

The Portiloop is proposed, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations and a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation.

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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.

31 0

Paper Graph

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