Neuron's Spikes Noise Level Classification Using Hidden Markov Models

被引：0

作者：

Haggag, Sherif ^{[1
]}

Mohamed, Shady ^{[1
]}

Bhatti, Asim ^{[1
]}

Haggag, Hussein ^{[1
]}

Nahavandi, Saeid ^{[1
]}

机构：

[1] Deakin Univ, Ctr Intelligent Syst Res, Geelong, Vic 3217, Australia

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III | 2014年 / 8836卷

关键词：

Hidden Markov Model; Mel-Frequency Cepstrum Coefficient; Multichannel systems; neural signal; SYSTEM;

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：

Considering that the uncertainty noise produced the decline in the quality of collected neural signal, this paper proposes a signal quality assessment method for neural signal. The method makes an automated measure to detect the noise levels in neural signal. Hidden Markov Models were used to build a classification model that classifies the neural spikes based on the noise level associated with the signal. This neural quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information.

引用

页码：501 / 508

页数：8

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