The use of wavelet transform as a preprocessor for the neural network detection of EEG spikes

T. Kalayci, O. Ozdamar, N. Erdol
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引用次数: 21

Abstract

In this study, the wavelet transform is used to process EEG data as input to a feed forward neural network for the detection of epileptogenic transient waveforms. The compression capability of wavelet transform provided the inclusion of data before and after the spike for contextual information without increasing input size of the neural network. The network is trained for the detection of spikes and non-spikes. The results show that wavelet transform can be used to provide more relevant information for improving the detection of epileptogenic spikes for automated EEG monitoring of seizure patients.<>
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利用小波变换作为预处理,实现了神经网络对脑电图峰值的检测
在本研究中,采用小波变换对脑电数据进行处理,作为前馈神经网络的输入,用于检测致痫瞬态波形。小波变换的压缩能力在不增加神经网络输入大小的情况下,提供了在峰值前后包含上下文信息的数据。该网络被训练用于检测尖峰和非尖峰。结果表明,小波变换可以为癫痫患者的自动脑电图监测提供更多的相关信息,以提高对致痫性峰的检测。
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