A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features

Neslihan Baki; Nurhan Gürsel Özmen

Makale detayları

A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features

Yazarlar:

Neslihan Baki | Nurhan Gürsel Özmen

Yayıncı:

Sakarya University

Ana Başlıklar:

Machine Learning (Other)

Dergi:

Sakarya University Journal of Science Vol.28 Issue 5, pp.912-923

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329630

pISSN:

2147-835X

Anahtar kelimeler:

Özet:

 Parkinsons disease PD is a serious neurological disease that is threatening the whole world population. The devolution of the neurons located in the substanstia nigra of the brain causes, bradykinesia, rigidity and resting tremor, which are characteristic motor symptoms, occuring in advanced stages. Currently, there is not an effective treatment for PD, it is just controlled by some prescribtions. Early detection of this disease affects the choice of treatment. Recent studies on early diagnosis by analyzing electroencephalography EEG recordings have provided a glimmer of hope. Therefore, in this study, an efficient PD detection method from EEG data by using a new set of features is searched. An opensource resting state data of 28 subjects divided as Parkinson and control gorups were anlyzed. PSDs of the EEG frequency bands that are delta, theta, alpha, beta and gamma and Median Spectral Frequency MSF, Spectral Entropy SE, Kolmogorov Algorithmic Complexity KAC and Weighted Symbolic Mutual Information wSMI were extracted as features. The performance of the PD and control group was evaluated with Gradient Boosting GB, Gaussian Naive Bayes GNB, and K-nearest Neighbor KNN, Support Vector Machines SVM, Logistic Regression LR, Categorical Boosting CatBoost and Extreme Gradient Boosting XGBoost Algorithms. A 85 accuracy was achieved with the XGBoost algorithm, using 31 channels and 13 features which outperforms the results of previous studies using this dataset in the literature.

 Parkinsons disease PD is a serious neurological disease that is threatening the whole world population. The devolution of the neurons located in the substanstia nigra of the brain causes, bradykinesia, rigidity and resting tremor, which are characteristic motor symptoms, occuring in advanced stages. Currently, there is not an effective treatment for PD, it is just controlled by some prescribtions. Early detection of this disease affects the choice of treatment. Recent studies on early diagnosis by analyzing electroencephalography EEG recordings have provided a glimmer of hope. Therefore, in this study, an efficient PD detection method from EEG data by using a new set of features is searched. An opensource resting state data of 28 subjects divided as Parkinson and control gorups were anlyzed. PSDs of the EEG frequency bands that are delta, theta, alpha, beta and gamma and Median Spectral Frequency MSF, Spectral Entropy SE, Kolmogorov Algorithmic Complexity KAC and Weighted Symbolic Mutual Information wSMI were extracted as features. The performance of the PD and control group was evaluated with Gradient Boosting GB, Gaussian Naive Bayes GNB, and K-nearest Neighbor KNN, Support Vector Machines SVM, Logistic Regression LR, Categorical Boosting CatBoost and Extreme Gradient Boosting XGBoost Algorithms. A 85 accuracy was achieved with the XGBoost algorithm, using 31 channels and 13 features which outperforms the results of previous studies using this dataset in the literature.

 Parkinsons disease PD is a serious neurological disease that is threatening the whole world population. The devolution of the neurons located in the substanstia nigra of the brain causes, bradykinesia, rigidity and resting tremor, which are characteristic motor symptoms, occuring in advanced stages. Currently, there is not an effective treatment for PD, it is just controlled by some prescribtions. Early detection of this disease affects the choice of treatment. Recent studies on early diagnosis by analyzing electroencephalography EEG recordings have provided a glimmer of hope. Therefore, in this study, an efficient PD detection method from EEG data by using a new set of features is searched. An opensource resting state data of 28 subjects divided as Parkinson and control gorups were anlyzed. PSDs of the EEG frequency bands that are delta, theta, alpha, beta and gamma and Median Spectral Frequency MSF, Spectral Entropy SE, Kolmogorov Algorithmic Complexity KAC and Weighted Symbolic Mutual Information wSMI were extracted as features. The performance of the PD and control group was evaluated with Gradient Boosting GB, Gaussian Naive Bayes GNB, and K-nearest Neighbor KNN, Support Vector Machines SVM, Logistic Regression LR, Categorical Boosting CatBoost and Extreme Gradient Boosting XGBoost Algorithms. A 85 accuracy was achieved with the XGBoost algorithm, using 31 channels and 13 features which outperforms the results of previous studies using this dataset in the literature.

 Parkinsons disease PD is a serious neurological disease that is threatening the whole world population. The devolution of the neurons located in the substanstia nigra of the brain causes, bradykinesia, rigidity and resting tremor, which are characteristic motor symptoms, occuring in advanced stages. Currently, there is not an effective treatment for PD, it is just controlled by some prescribtions. Early detection of this disease affects the choice of treatment. Recent studies on early diagnosis by analyzing electroencephalography EEG recordings have provided a glimmer of hope. Therefore, in this study, an efficient PD detection method from EEG data by using a new set of features is searched. An opensource resting state data of 28 subjects divided as Parkinson and control gorups were anlyzed. PSDs of the EEG frequency bands that are delta, theta, alpha, beta and gamma and Median Spectral Frequency MSF, Spectral Entropy SE, Kolmogorov Algorithmic Complexity KAC and Weighted Symbolic Mutual Information wSMI were extracted as features. The performance of the PD and control group was evaluated with Gradient Boosting GB, Gaussian Naive Bayes GNB, and K-nearest Neighbor KNN, Support Vector Machines SVM, Logistic Regression LR, Categorical Boosting CatBoost and Extreme Gradient Boosting XGBoost Algorithms. A 85 accuracy was achieved with the XGBoost algorithm, using 31 channels and 13 features which outperforms the results of previous studies using this dataset in the literature.

Makale detayları

A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features

Yazarlar:

Neslihan Baki | Nurhan Gürsel Özmen

Yayıncı:

Sakarya University

Ana Başlıklar:

Machine Learning (Other)

Dergi:

Sakarya University Journal of Science Vol.28 Issue 5, pp.912-923

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329630

pISSN:

2147-835X

Parkinson

Electroencephalography

Weighted Symbolic Mutual Information (wSMI)

Kolmogorov Algorithmic Complexity (KAC)

Extreme gradient boosting algorithm

Sayı

Makale detayları

A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features

Yazarlar: Neslihan Baki | Nurhan Gürsel Özmen

Yayıncı: Sakarya University

Ana Başlıklar: Machine Learning (Other)

Dergi: Sakarya University Journal of Science Vol.28 Issue 5, pp.912-923

Belge Tipi: Makale

Yayın Tarihi: 10/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001329630

pISSN: 2147-835X

Parkinson

Electroencephalography

Weighted Symbolic Mutual Information (wSMI)

Kolmogorov Algorithmic Complexity (KAC)

Extreme gradient boosting algorithm

Sayı

Yazarlar:

Neslihan Baki | Nurhan Gürsel Özmen

Yayıncı:

Sakarya University

Ana Başlıklar:

Machine Learning (Other)

Dergi:

Sakarya University Journal of Science Vol.28 Issue 5, pp.912-923

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329630

pISSN:

2147-835X