Classifying Parkinson's Disease Based on Acoustic Measures Using Artificial Neural Networks
Classifying Parkinson's Disease Based on Acoustic Measures Using Artificial Neural Networks
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DOI：
10.3390/s19010016
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Ficko, Mirko
中科院分区：
文献类型：
--
作者：
Berus, Lucijano;Klancnik, Simon;Ficko, Mirko
近年来，神经网络在各种预测问题中变得非常流行。在本文中，多个前馈人工神经网络（ANN）与各种配置用于预测帕金森氏病（PD）的测试个人，从26个不同的语音样本每个人提取的特征的基础上。通过留一受试者（LOSO）方案验证结果。基于Pearson相关系数、Kendall相关系数、主成分分析和自组织映射的一些特征选择程序已被用于提高算法的性能和减少数据。基于Kendall相关系数的特征选择方法获得了最佳的测试精度，并识别出了最相关的语音样本。多个人工神经网络已被证明是诊断PD的最佳分类技术，而无需使用特征选择程序（对原始数据）。最后，神经网络进行微调，并取得了86.47%的测试精度。
In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson's disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson's correlation coefficient, Kendall's correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall's correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.