Spindle Detection

6 papers with code • 4 benchmarks • 3 datasets

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Most implemented papers

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).

TarekLaj/SPINKY Frontiers in Neuroinformatics 2017

Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders.

Multichannel sleep spindle detection using sparse low-rank optimization

aparek/mcsleep Journal of Neuroscience Methods Volume 288 2017

Using a non-linear signal model, which assumes the input EEG to be the sum of a transient and an oscillatory component, we propose a multichannel transient separation algorithm.

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

Dreem-Organization/dosed 7 Dec 2018

The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD.

RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection

nicolasigor/cmorlet-tensorflow 15 May 2020

The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes.

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

mistlab/portiloop 28 Jul 2021

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely.

Advanced sleep spindle identification with neural networks

dslaborg/sumo Scientific Reports 2022

Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset.