Spindle Detection
7 papers with code • 4 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Spindle Detection
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MaSS
Montreal Archive of Sleep Studies
MODA dataset
Latest papers
Unveil Sleep Spindles with Concentration of Frequency and Time
We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs).
Advanced sleep spindle identification with neural networks
dslaborg/sumo
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Scientific Reports 2022
Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset.
The Portiloop: a deep learning-based open science tool for closed-loop brain stimulation
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.
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
nicolasigor/cmorlet-tensorflow
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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.
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
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.
Multichannel sleep spindle detection using sparse low-rank optimization
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.
Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).
Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders.
