Neural Network Performance Comparison in Infant Pain Expression Classifications

机译：婴儿疼痛表达分类中的神经网络性能比较

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Infant pain is a non-stationary made by infants in response to certain situations. This infant facial expression can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of features in infant pain classification. Fast Fourier Transform (FFT), and Singular value Decomposition (SVD) features are computed at different classifier. Two different case studies such as normal and pain are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant pain. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant pain.

机译：婴儿疼痛是婴儿的非静止性，以应对某些情况。这种婴儿表达式可用于识别婴儿的身体或心理学状态。这项工作的目的是比较婴儿疼痛分类中的特征的表现。快速傅里叶变换（FFT）和奇异值分解（SVD）特征在不同的分类器上计算。进行两种不同的案例研究，如正常和疼痛。两种不同类型的径向基础人工神经网络即概率神经网络（PNN）和一般回归神经网络（GRNN）用于对婴儿疼痛进行分类。结果强调，所提出的特征和分类算法可用于帮助医学专业人员诊断婴儿疼痛的病理状态。

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《International Conference on Sensors, Measurement and Intelligent Materials》|2014年||共6页
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作者
Muhammad Naufal Mansor; Mohd Nazri Rejab;
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中图分类 TP212-532;
关键词
Infant Pain; FFT; DCT; PNN; GRNN Classifier.;

机译：婴儿疼痛;FFT;DCT;PNN;GRNN分类器。;

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4. Neural Network Performance Comparison in Infant Pain Expression Classifications [C] . Muhammad Naufal Mansor, Mohd Nazri Rejab International Conference on Sensors, Measurement and Intelligent Materials . 2014

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Neural Network Performance Comparison in Infant Pain Expression Classifications

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