K-complex detection
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Benchmarks
MASS SS2
Most implemented
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
Papers
K-complex Detection Using Fourier Spectrum Analysis In EEG
K-complexes are an important marker of brain activity and are used both in clinical practice to perform sleep scoring, and in research. However, due to the size of electroencephalography (EEG) records, as well as the sub…
EEGK-complex 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+5Meet 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