Papers Spindle Detection
“Spindle Detection” 태그가 달린 논문 9편 · 필터 해제
From Sleep Staging to Spindle Detection: Evaluating End-to-End Automated Sleep Analysis
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater inco…
Privacy PreservingSleep StagingSpindle DetectionUnveil Sleep Spindles with Concentration of Frequency and Time
Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated al…
Deep LearningEEGSpindle DetectionAdvanced sleep spindle identification with neural networks
Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during …
DiagnosticEEGElectroencephalogram (EEG)Sleep Micro-event detection+4The 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 stimulatio…
EEGElectroencephalogram (EEG)Spindle DetectionRED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
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. These events have b…
EEGElectroencephalogram (EEG)Event DetectionK-complex detection+3DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned…
EEGElectroencephalogram (EEG)K-complex detectionSleep apnea detection+5A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET.
BACKGROUND: Studies on sleep-spindles are typically based on visual-marks performed by experts, however this process is time consuming and presents a low inter-expert agreement, causing the data to be limited in quantit…
EEGElectroencephalogram (EEG)General ClassificationSensitivity+2Multichannel sleep spindle detection using sparse low-rank optimization
BACKGROUND: Automated single-channel spindle detectors, for human sleep EEG, are blind to the presence of spindles in other recorded channels unlike visual annotation by a human expert. NEW METHOD: We propose a mult…
EEGElectroencephalogram (EEG)Spindle DetectionMeet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).
Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive…
BenchmarkingEEGElectroencephalogram (EEG)K-complex detection+1