Sleep spindle abnormalities related to Alzheimer's disease: a systematic mini-review

YY Weng, X Lei, J Yu - Sleep medicine, 2020 - Elsevier
Accumulating evidence supports a bidirectional relationship between sleep disruption and
Alzheimer's disease (AD) pathology. Among various sleep electroencephalography …

Sleep spindles as an electrographic element: description and automatic detection methods

D Coppieters't Wallant, P Maquet, C Phillips - Neural plasticity, 2016 - Wiley Online Library
Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a
number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual …

Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods

SC Warby, SL Wendt, P Welinder, EGS Munk… - Nature …, 2014 - nature.com
Sleep spindles are discrete, intermittent patterns of brain activity observed in human
electroencephalographic data. Increasingly, these oscillations are of biological and clinical …

Sleep oscillation-specific associations with Alzheimer's disease CSF biomarkers: novel roles for sleep spindles and tau

K Kam, A Parekh, RA Sharma, A Andrade… - Molecular …, 2019 - Springer
Background Based on associations between sleep spindles, cognition, and sleep-
dependent memory processing, here we evaluated potential relationships between levels of …

A deep learning approach for real-time detection of sleep spindles

PM Kulkarni, Z **ao, EJ Robinson… - Journal of neural …, 2019 - iopscience.iop.org
Objective. Sleep spindles have been implicated in memory consolidation and synaptic
plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection …

Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization

A Parekh, IW Selesnick, DM Rapoport… - Journal of neuroscience …, 2015 - Elsevier
Background This paper addresses the problem of detecting sleep spindles and K-
complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 …

Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing

A Tsanas, GD Clifford - Frontiers in human neuroscience, 2015 - frontiersin.org
Sleep spindles are critical in characterizing sleep and have been associated with cognitive
function and pathophysiological assessment. Typically, their detection relies on the …

DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal

S Chambon, V Thorey, PJ Arnal, E Mignot… - Journal of Neuroscience …, 2019 - Elsevier
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 …

The case for using digital EEG analysis in clinical sleep medicine

M Younes - Sleep Science and Practice, 2017 - Springer
Abstract Evaluation of sleep in clinical polysomnograms continues to rely almost exclusively
on visual scoring that implements rules proposed by Rechtschaffen and Kales nearly 50 …

A deep learning architecture to detect events in EEG signals during sleep

S Chambon, V Thorey, PJ Arnal… - 2018 IEEE 28th …, 2018 - ieeexplore.ieee.org
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various
neurological disorders. In sleep medicine, it is relevant to detect macro-events (≥ 10s) such …