Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform
about
Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms.Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource.Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles.Staging Sleep in Polysomnograms: Analysis of Inter-Scorer Variability.
P2860
Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform
description
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name
Automatic Sleep Spindle Detect ...... g Continuous Wavelet Transform
@ast
Automatic Sleep Spindle Detect ...... g Continuous Wavelet Transform
@en
type
label
Automatic Sleep Spindle Detect ...... g Continuous Wavelet Transform
@ast
Automatic Sleep Spindle Detect ...... g Continuous Wavelet Transform
@en
prefLabel
Automatic Sleep Spindle Detect ...... g Continuous Wavelet Transform
@ast
Automatic Sleep Spindle Detect ...... g Continuous Wavelet Transform
@en
P2093
P2860
P356
P1476
Automatic Sleep Spindle Detect ...... g Continuous Wavelet Transform
@en
P2093
Axel Steiger
Elisabeth Friess
Marek Adamczyk
Martin Dresler
P2860
P356
10.3389/FNHUM.2015.00624
P50
P577
2015-11-19T00:00:00Z