A Gaussian mixture model based statistical classification system for neonatal seizure detection

被引：0

作者：

Thomas, Eoin M. ^{[1
]}

Temko, Andriy ^{[1
]}

Lightbody, Gordon ^{[1
]}

Marnane, William P. ^{[1
]}

Boylan, Geraldine B. ^{[2
]}

机构：

[1] UCC, Dept Elect Engn, Cork, Ireland

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

来源：

2009 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING | 2009年

基金：

爱尔兰科学基金会;

关键词：

Neonatal Seizure Detection; Linear Discriminant Analysis; Principal Component Analysis; Gaussian Mixture Models;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A neonatal seizure detection system is proposed based on a Gaussian mixture model classifier. Linear discriminant analysis and principal component analysis are compared for the task of feature vector preprocessing. A postprocessing scheme is developed from the probability of seizure estimate in order to improve the performance of the system. Results are reported on a dataset of 17 patients with a total duration of 267.9 hours, the average ROC area of the system is 95.6%.

引用

页码：446 / +

页数：2

共 22 条

[1] Automated neonatal seizure detection: A multistage classification system through feature selection basedon relevance and redundancy analysis [J].