Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques

被引：81
|
作者：
Hassanpour, H [1 ]
Mesbah, M [1 ]
Boashash, B [1 ]
机构：
[1] Queensland Univ Technol, Lab Signal Proc Res, Brisbane, Qld 4001, Australia
来源：
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING | 2004年 / 2004卷 / 16期
关键词：
detection; time-frequency distribution; singular value decomposition; probability distribution function;
D O I：
10.1155/S1110865704406167
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The nonstationary and multicomponent nature of newborn EEG seizures tends to increase the complexity of the seizure detection problem. In dealing with this type of problems, time-frequency-based techniques were shown to outperform classical techniques. This paper presents a new time-frequency-based EEG seizure detection technique. The technique uses an estimate of the distribution function of the singular vectors associated with the time-frequency distribution of an EEG epoch to characterise the patterns embedded in the signal. The estimated distribution functions related to seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns.
引用
收藏
页码：2544 / 2554
页数：11
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