Wavelet analysis for detection of phasic electromyographic activity in sleep: influence of mother wavelet and dimensionality reduction.
Wavelet analysis for detection of phasic electromyographic activity in sleep: influence of mother wavelet and dimensionality reduction.
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DOI：
10.1016/j.compbiomed.2013.12.011
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发表时间：
2014-05
影响因子：
7.7
通讯作者：
Bliwise, Donald L.
中科院分区：
文献类型：
--
作者：
Fairley, Jacqueline A.;Georgoulas, George;Smart, Otis L.;Dimakopoulos, George;Karvelis, Petros;Stylios, Chrysostomos D.;Rye, David B.;Bliwise, Donald L.
关键词：
睡眠期间的肌电活动以短暂的肌肉抽搐为特征（持续时间100-500毫秒，幅度是背景活动的4倍）。此类活动的高发生率可能具有临床相关性。本文介绍了小波分析来检测相肌电图，检查了Symlet和Daubechies方法。特征提取包括对24个基于wt的特征进行1秒epoch处理，降维涉及比较两种技术：主成分分析和特征/变量选择算法。使用线性分类器进行分类。与专家判断相比，在11/12个数据集上获得了有效的自动检测，分类性能高（>90%）。
Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100–500 msec, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1 sec epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets.
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影响因子：
6.9
作者：
Gargour, Christian;Gabrea, Marcel;Lina, Jean-Marc
通讯作者：
Lina, Jean-Marc
影响因子：
5.1
作者：
Fairley, Jacqueline A.;Georgoulas, George;Mehta, Nishant A.;Gray, Alexander G.;Bliwise, Donald L.
通讯作者：
Bliwise, Donald L.
影响因子：
2.4
作者：
Bliwise, DL;He, LQ;Rye, DB
通讯作者：
Rye, DB
影响因子：
5.7
作者：
Hand, David J.
通讯作者：
Hand, David J.
影响因子：
5.1
作者：
PUDIL, P;NOVOVICOVA, J;KITTLER, J
通讯作者：
KITTLER, J