Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Department of Computer Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
Comput Biol Med. 2023 Jul;161:107031. doi: 10.1016/j.compbiomed.2023.107031. Epub 2023 May 17.
In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.
在本文中，我们提出了一种使用集成学习和 1D-PDCovNN（一种新的深度学习技术）来诊断和分类帕金森病（PD）的新方法。PD 是一种神经退行性疾病；早期检测和正确分类对于更好的疾病管理至关重要。本研究的主要目的是使用 EEG 信号开发一种强大的 PD 诊断和分类方法。作为数据集，我们使用了圣地亚哥静息状态 EEG 数据集来评估我们提出的方法。所提出的方法主要包括三个阶段。在第一阶段，使用独立成分分析（ICA）方法作为预处理方法，从 EEG 信号中滤除眨眼噪声。此外，还研究了在 EEG 信号中，与运动皮层活动相关的频段（7-30 Hz）对从 EEG 信号中诊断和分类帕金森病的影响。在第二阶段，使用共空间模式（CSP）方法作为特征提取方法，从 EEG 信号中提取有用信息。最后，在第三阶段，采用了一种集成学习方法，即修正局部精度（MLA）中的动态分类器选择（DCS），其中包含七个不同的分类器。作为分类器方法，使用 DCS in MLA、XGBoost 和 1D-PDCovNN 分类器将 EEG 信号分类为 PD 和健康对照（HC）。我们首先使用动态分类器选择来从 EEG 信号中诊断和分类帕金森病（PD），并获得了有前途的结果。通过在提出的模型中使用分类准确性、F1 分数、kappa 分数、Jaccard 分数、ROC 曲线、召回率和精度值来评估所提出方法在 PD 分类中的性能。在 PD 的分类中，DCS in MLA 的组合实现了 99.31%的准确性。这项研究的结果表明，所提出的方法可以作为 PD 早期诊断和分类的可靠工具。
