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

页码：483 / 493

页数：11

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