Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals

Lee, Seung-Bo; Kim, Yong Jeong; Hwang, Sungeun; Son, Hyoshin; Lee, Sang Kun; Park, Kyung‐Il; Kim, Young-Gon

doi:10.1016/j.parkreldis.2022.01.011

Cited by 36 publications

(24 citation statements)

References 30 publications

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Section: Discussionmentioning

confidence: 92%

“…There are currently few studies using the Iowa dataset (14 PD patients and 14 controls), for which our proposed MCNN model has a large improvement in classification performance compared to other related studies ( Anjum et al, 2020 ; Lee et al, 2022 ). E.g., our accuracy is about 14.12% higher than the linear-predictive-coding EEG algorithm proposed by Anjum et al (2020) , and about 10.52% higher than the Hjorth parameter and the gradient boosting decision tree algorithm proposed by Lee et al (2022) . For the UC San Diego dataset (15 PD patients and 16 controls), the classification performance (accuracy, sensitivity, specificity, and AUC are above 93% in γ band) of our proposed MCNN model is comparable, although not the highest, compared to other related studies ( Khare et al, 2021a , b ; Loh et al, 2021 ; Shaban, 2021 ; Shaban and Amara, 2022 ).…”

Section: Discussionmentioning

confidence: 92%

“…Most recently, Anjum et al (2020) developed a linear-predictive-coding EEG Algorithm to encode EEG time series into features for PD detection, and obtained reliable performance with 85.7% diagnostic accuracy, 85.2% area under curve (AUC) of receiver operating characteristics (ROC), 85.7% sensitivity, and 85.7% specificity. Lee et al (2022) proposed a PD prediction method using the Hjorth parameter and gradient boosting decision tree algorithm, which differentiated PD patients from HCs with 89.3% accuracy and 0.912 AUC of ROC.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks

Qiu

Pan

2022

Front. Neurosci.

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Parkinson’s disease (PD) is a complex neurodegenerative disease. At present, the early diagnosis of PD is still extremely challenging, and there is still a lack of consensus on the brain characterization of PD, and a more efficient and robust PD detection method is urgently needed. In order to further explore the features of PD based on brain activity and achieve effective detection of PD patients (including OFF and ON medications), in this study, a multi-pattern analysis based on brain activation and brain functional connectivity was performed on the brain functional activity of PD patients, and a novel PD detection model based on multi-scale convolutional neural network (MCNN) was proposed. Based on the analysis of power spectral density (PSD) and phase-locked value (PLV) features of multiple frequency bands of two independent resting-state electroencephalography (EEG) datasets, we found that there were significant differences in PSD and PLV between HCs and PD patients (including OFF and ON medications), especially in the β and γ bands, which were very effective for PD detection. Moreover, the combined use of brain activation represented by PSD and functional connectivity patterns represented by PLV can effectively improve the performance of PD detection. Furthermore, our proposed MCNN model shows great potential for automatic PD detection, with cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve all above 99%. Our study may help to further understand the characteristics of PD and provide new ideas for future PD diagnosis based on spontaneous EEG activity.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks

Qiu

Pan

2022

Front. Neurosci.

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“…We also compare the machine learning methods. As shown in Table 2 , the classification accuracy of XGBoost [ 46 ] is 79.58%, the classification accuracy of graded CatBoost [ 47 ] is 94.14%, the classification accuracy of random forest (RF) [ 48 ] is 87.98%, and the classification accuracy of the support vector machine (SVM) [ 24 ] is 95.63%. The classification accuracy of the K-nearest neighbor (KNN) algorithm [ 27 ] is 94.23%.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

EEG Classification of Normal and Alcoholic by Deep Learning

2022

Brain Sciences

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Alcohol dependence is a common mental disease worldwide. Excessive alcohol consumption may lead to alcoholism and many complications. In severe cases, it will lead to inhibition and paralysis of the centers of the respiratory and circulatory systems and even death. In addition, there is a lack of effective standard test procedures to detect alcoholism. EEG signals are data obtained by measuring brain changes in the cerebral cortex and can be used for the diagnosis of alcoholism. Existing diagnostic methods mainly employ machine learning techniques, which rely on human intervention to learn. In contrast, deep learning, as an end-to-end learning method, can automatically extract EEG signal features, which is more convenient. Nonetheless, there are few studies on the classification of alcohol’s EEG signals using deep learning models. Therefore, in this paper, a new deep learning method is proposed to automatically extract and classify EEG’s features. The method first adopts a multilayer discrete wavelet transform to denoise the input data. Then, the denoised data are used as input, and a convolutional neural network and bidirectional long short-term memory network are used for feature extraction. Finally, alcohol EEG signal classification is performed. The experimental results show that the method proposed in this study can be utilized to effectively diagnose patients with alcoholism, achieving a diagnostic accuracy of 99.32%, which is better than most current algorithms.

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“…For example, it forms a basis for diagnosing stroke in the elderly ( Choi et al, 2021 ), intending to reduce neural damage through timely intervention, or alleviating the financial burden of patients compared with other neuroimaging techniques such as computed tomography. Furthermore, a wide range of medical applications have been flourishing along with advances in technology, enabling medical professionals to utilize EEG for intelligent diagnosis of various neurological and neuropsychiatric disorders such as depression ( Jiang et al, 2021 ), epilepsy ( Subasi et al, 2017 ; Amin et al, 2020 ) and Parkinson’s disease (PD) ( Lee et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of abnormal EEG signals using multiscale features with ensemble learning

Kong

Zhong

et al. 2022

Front. Hum. Neurosci.

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Electroencephalogram (EEG) is an economical and convenient auxiliary test to aid in the diagnosis and analysis of brain-related neurological diseases. In recent years, machine learning has shown great potential in clinical EEG abnormality detection. However, existing methods usually fail to consider the issue of feature redundancy when extracting the relevant EEG features. In addition, the importance of utilizing the patient age information in EEG detection is ignored. In this paper, a new framework is proposed for distinguishing an unknown EEG recording as either normal or abnormal by identifying different types of EEG-derived significant features. In the proposed framework, different hierarchical salient features are extracted using a time-wise multi-scale aggregation strategy, based on a selected group of statistical characteristics calculated from the optimum discrete wavelet transform coefficients. We also fuse the age information with multi-scale features for further improving discrimination. The integrated features are classified using three ensemble learning classifiers, CatBoost, LightGBM, and random forest. Experimental results show that our method with CatBoost classifier can yield superior performance vis-a-vis competing techniques, which indicates the great promise of our methodology in EEG pathology detection.

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Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals

Cited by 36 publications

References 30 publications

Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks

Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks

EEG Classification of Normal and Alcoholic by Deep Learning

Automatic detection of abnormal EEG signals using multiscale features with ensemble learning

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