Diagnosis of Parkinson's disease through EEG signals based on artificial neural network and cuckoo search algorithm

Rzgar Sirwan Raza, Adil Hussein Mohammed
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Abstract

Parkinson's disease is a degenerative nervous system condition that impairs mobility. If the condition is not detected early enough, it might have permanent effects for the sufferer. A novel approach for identifying Parkinson's disease is provided in this research, which employs machine optimization and learning techniques. The suggested method's diagnosis procedure may be broken down into three primary steps: "preprocessing," "feature extraction," and "classification." Preprocessing the EEG data is the initial stage in the suggested technique. Database samples are treated using discrete wavelet analysis to remove the destructive influence of noise on the input signals using signal analysis for this aim. The suggested method's second phase will employ principal component analysis to remove duplicate features and minimize data dimensionality. The artificial neural network model is trained and the classification model is built using the retrieved features. The effectiveness of the suggested technique is examined in terms of criteria such as accuracy, sensitivity, and specificity during the experimentation phase, and the results are compared to existing learning models. The findings revealed that the suggested technique enhances illness diagnostic accuracy by at least 8.25% and may be utilized as a useful tool in disease diagnosis.
基于人工神经网络和布谷鸟搜索算法的脑电信号诊断帕金森病
帕金森氏症是一种神经系统退行性疾病,会损害活动能力。如果没有及早发现这种情况,可能会对患者产生永久性影响。本研究提供了一种新的识别帕金森病的方法,该方法采用机器优化和学习技术。该方法的诊断过程可分为三个主要步骤:“预处理”、“特征提取”和“分类”。脑电信号的预处理是该方法的初始阶段。采用离散小波分析对数据库样本进行处理,消除噪声对输入信号的破坏性影响。该方法的第二阶段将采用主成分分析来去除重复特征并最小化数据维度。对人工神经网络模型进行训练,利用检索到的特征建立分类模型。在实验阶段,根据准确性、灵敏度和特异性等标准来检验所建议技术的有效性,并将结果与现有的学习模型进行比较。结果表明,该方法可使疾病诊断准确率提高至少8.25%,可作为疾病诊断的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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