Gaussian mixture models for classification of neonatal seizures using EEG

被引：46

作者：

Thomas, E. M. ^{[1
]}

Temko, A. ^{[1
]}

Lightbody, G. ^{[1
]}

Marnane, W. P. ^{[1
]}

Boylan, G. B. ^{[2
]}

机构：

[1] Univ Coll Cork, Dept Elect & Elect Engn, Cork, Ireland

[2] Univ Coll Cork, Dept Paediat & Child Hlth, Cork, Ireland

来源：

PHYSIOLOGICAL MEASUREMENT | 2010年 / 31卷 / 07期

基金：

英国惠康基金; 爱尔兰科学基金会;

关键词：

Neonatal EEG; seizure detection; Gaussian mixture models; EPILEPTIC SEIZURES; SYSTEM; FEATURES;

D O I：

10.1088/0967-3334/31/7/013

中图分类号：

Q6 [生物物理学];

学科分类号：

071011 ;

摘要：

A real-time neonatal seizure detection system is proposed based on a Gaussian mixture model classifier. The system includes feature transformation techniques and classifier output postprocessing. The detector was evaluated on a database of 20 patients with 330 h of recordings. A detailed analysis of the choice of parameters for the detector is provided. A mean good detection rate of 79% was obtained with only 0.5 false detections per hour. Athorough review of all misclassified events was performed, from which a number of patterns causing false detections were identified.

引用

页码：1047 / 1064

页数：18

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