Neonates' EEG spike detection using a time-frequency approach

P. Zarjam, G. Azemi, B. Boashash
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引用次数: 1

Abstract

This paper proposes a new time-frequency (TF) based approach for detecting spikes in newborns' EEG signals. Since the automated detection of spikes in the EEG is an important deal in the diagnosis of epilepsy and is a goal sought by many researchers. in the proposed method, first a TF representation of the EEG epoch is computed and then its average over all frequencies is used to distinguish between the EEG spikes and the background. The method is used in conjunction with a few time-frequency distributions (TFDs) to detect spikes in the EEG signals acquired from 5 neonates, with ages ranging from two days to two weeks. The obtained results show that the short-time Fourier transform (STFT) outperforms the other TFDs with the average detection rate of 96%.
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基于时频法的新生儿脑电图尖峰检测
本文提出了一种新的基于时频的新生儿脑电图信号尖峰检测方法。由于脑电图峰的自动检测是癫痫诊断的一个重要方面,也是许多研究人员所追求的目标。在该方法中,首先计算脑电信号epoch的TF表示,然后使用其在所有频率上的平均值来区分脑电信号峰值和背景。该方法与一些时间频率分布(TFDs)结合使用,以检测从5名新生儿(年龄从2天到2周)获得的脑电图信号中的峰值。结果表明,短时傅里叶变换(STFT)的平均检出率为96%,优于其他时域变换。
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