Validating an automated sleep spindle detection algorithm using an individualized approach.
about
Sleep Spindles as an Electrographic Element: Description and Automatic Detection MethodsAutomatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools.Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).Topography-specific spindle frequency changes in obstructive sleep apnea.Correlations between adolescent processing speed and specific spindle frequenciesA comparison of two sleep spindle detection methods based on all night averages: individually adjusted vs. fixed frequenciesUsing a quadratic parameter sinusoid model to characterize the structure of EEG sleep spindles.Spindles in Svarog: framework and software for parametrization of EEG transients.Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalizationSleep spindle detection based on non-experts: A validation studySleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods.Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis.Effect of emotional and neutral declarative memory consolidation on sleep architecture.Sleep Changes in Adolescents Following Procedural Task Training.Automated detection of sleep spindles in the scalp EEG and estimation of their intracranial current sources: comments on techniques and on related experimental and clinical studies.Identifying sleep spindles with multichannel EEG and classification optimization.Sleep spindle detection using multivariate Gaussian mixture models.fMRI and sleep correlates of the age-related impairment in motor memory consolidation.
P2860
Q26740197-31119F30-ECDB-43B8-8686-5FD30766312BQ28648001-EDC36F04-F94F-4186-B602-07493FD68D12Q30491149-EA619567-9FEF-4960-9608-1A5FC806266EQ34416912-3CAE4D7A-AA70-43A2-B6CF-DC00A58C33CBQ35062915-3C1DCE01-1E06-481C-8AD5-58E33D0A3AECQ35090337-632A28AE-6165-4D1A-BCFA-D4C9E1A7F50CQ35569955-B1D6DD8E-7751-4037-AA2B-7023C5628CD3Q35584974-0DEDE350-39B8-4E10-B793-FD8C534EFECBQ36097982-43EECE61-2DA0-4E3D-A61A-B745C4E65627Q36369058-8E76FCF2-7F29-466A-A0B5-641A4773C316Q37678377-61802C76-5639-464F-9801-C3C31E8598DFQ39032266-249DDA91-2D59-40D9-A870-5B15D80B01E5Q39299155-11A40DDD-9A7E-4739-8E34-A8889384F844Q41348801-9C9CCC49-4C59-4206-B1B1-DE3CA71C3F6AQ42557052-0D0F0E24-A542-499A-8F87-250700737E83Q45945765-5DD4193B-9B5D-4055-8261-B12135A03EB7Q47442437-1D9DF7CC-29FC-441B-AC2D-B76959593ED0Q48139634-07C68F93-9B86-4F4F-ADC2-C9F8570DD471
P2860
Validating an automated sleep spindle detection algorithm using an individualized approach.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
2010年學術文章
@zh
2010年學術文章
@zh-hant
name
Validating an automated sleep ...... ng an individualized approach.
@en
Validating an automated sleep ...... ng an individualized approach.
@nl
type
label
Validating an automated sleep ...... ng an individualized approach.
@en
Validating an automated sleep ...... ng an individualized approach.
@nl
prefLabel
Validating an automated sleep ...... ng an individualized approach.
@en
Validating an automated sleep ...... ng an individualized approach.
@nl
P2093
P2860
P1476
Validating an automated sleep ...... ing an individualized approach
@en
P2093
Carlyle T Smith
Kevin R Peters
Laura B Ray
P2860
P304
P356
10.1111/J.1365-2869.2009.00802.X
P50
P577
2010-02-10T00:00:00Z