DiBa: A Data-Driven Bayesian Algorithm for Sleep Spindle Detection

被引：28
|
作者：
Babadi, Behtash [2 ,3 ]
McKinney, Scott M. [4 ]
Tarokh, Vahid [5 ]
Ellenbogen, Jeffrey M. [1 ,6 ]
机构：
[1] Harvard Univ, Sch Med, Div Sleep Med, Boston, MA 02215 USA
[2] Massachusetts Gen Hosp, Dept Anesthesia Crit Care & Pain Med, Boston, MA 02114 USA
[3] MIT, Dept Brain & Cognit Sci, Cambridge, MA 02139 USA
[4] Stanford Univ, Inst Computat & Math Engn, Stanford, CA 94305 USA
[5] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[6] Massachusetts Gen Hosp, Dept Neurol, Boston, MA 02114 USA
来源：
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2012年 / 59卷 / 02期
关键词：
Bayesian methods; electroencephalography (EEG); Karhunen-Loeve (KL) transform; medical signal detection; sleep spindles; AUTOMATED DETECTION; EEG; AMPLITUDE; AGE;
D O I：
10.1109/TBME.2011.2175225
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Although the spontaneous brain rhythms of sleep have commanded much recent interest, their detection and analysis remains suboptimal. In this paper, we develop a data-driven Bayesian algorithm for sleep spindle detection on the electroencephalography (EEG). The algorithm exploits the Karhunen-Loeve transform and Bayesian hypothesis testing to produce the instantaneous probability of a spindle's presence with maximal resolution. In addition to possessing flexibility, transparency, and scalability, this algorithm could perform at levels superior to standard methods for EEG event detection.
引用
收藏
页码：483 / 493
页数：11
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