Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data
Accurate diagnosis of Tic disorders (TD) and its severity based on electroencephalogram (EEG) data were of great clinical importance. This study analyzed EEG data from 90 children with TD and 88 healthy controls (HC). A two-stage progressive diagnosis framework based on EEG data and machine learning...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11036828/ |
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| author | Wanting Xiang Gang Zhu Yichong Hou Zhandong Mei Lin Wan Li Zhang Guang Yang Jian Zu |
| author_facet | Wanting Xiang Gang Zhu Yichong Hou Zhandong Mei Lin Wan Li Zhang Guang Yang Jian Zu |
| author_sort | Wanting Xiang |
| collection | DOAJ |
| description | Accurate diagnosis of Tic disorders (TD) and its severity based on electroencephalogram (EEG) data were of great clinical importance. This study analyzed EEG data from 90 children with TD and 88 healthy controls (HC). A two-stage progressive diagnosis framework based on EEG data and machine learning methods was developed. To achieve individualized prediction and reduce the feature dimension, we proposed a novel individual-based feature-weighted integration method in machine learning, as well as a new SHAP-driven feature selection and weighting (SFSW) strategy to improve the prediction accuracy. Based on 13 weighted features, Logistic Regression model achieved an average accuracy of 94.2% (95% CI, 90.6%-97.9%) in diagnosing TD, with a sensitivity of 92.4% (95% CI, 85.3%-99.5%) and a specificity of 96.1% (95% CI, 92.9%-99.2%). The Decision Tree model attained an average accuracy of 81.5% (95% CI, 68.6%-94.5%) in predicting severity, with a sensitivity of 81.5% (95% CI, 68.6%-94.5%) and a specificity of 89.9% (95% CI, 82.1%-97.6%). In the hold-out set validation, the method demonstrated accuracy rates of 95.7% in diagnosing TD and 83.3% in predicting severity. Interpretability analysis revealed that the top three main features affecting TD diagnosis were the mean frequency (MNF) of P3 channel <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> band, age and MNF of C3 channel <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> band. This work offered a more efficient approach to individualized diagnosis of TD and had substantial practical value for clinical auxiliary diagnosis and intervention. |
| format | Article |
| id | doaj-art-443ce35eead4436bb750070a0dbad36f |
| institution | OA Journals |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-443ce35eead4436bb750070a0dbad36f2025-08-20T02:22:15ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01332416242710.1109/TNSRE.2025.357976311036828Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram DataWanting Xiang0Gang Zhu1Yichong Hou2Zhandong Mei3Lin Wan4Li Zhang5Guang Yang6Jian Zu7https://orcid.org/0000-0003-3633-0725School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaSchool of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, ChinaSchool of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaChinese Institute for Brain Research, Beijing, ChinaDepartment of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaSchool of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, ChinaAccurate diagnosis of Tic disorders (TD) and its severity based on electroencephalogram (EEG) data were of great clinical importance. This study analyzed EEG data from 90 children with TD and 88 healthy controls (HC). A two-stage progressive diagnosis framework based on EEG data and machine learning methods was developed. To achieve individualized prediction and reduce the feature dimension, we proposed a novel individual-based feature-weighted integration method in machine learning, as well as a new SHAP-driven feature selection and weighting (SFSW) strategy to improve the prediction accuracy. Based on 13 weighted features, Logistic Regression model achieved an average accuracy of 94.2% (95% CI, 90.6%-97.9%) in diagnosing TD, with a sensitivity of 92.4% (95% CI, 85.3%-99.5%) and a specificity of 96.1% (95% CI, 92.9%-99.2%). The Decision Tree model attained an average accuracy of 81.5% (95% CI, 68.6%-94.5%) in predicting severity, with a sensitivity of 81.5% (95% CI, 68.6%-94.5%) and a specificity of 89.9% (95% CI, 82.1%-97.6%). In the hold-out set validation, the method demonstrated accuracy rates of 95.7% in diagnosing TD and 83.3% in predicting severity. Interpretability analysis revealed that the top three main features affecting TD diagnosis were the mean frequency (MNF) of P3 channel <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> band, age and MNF of C3 channel <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> band. This work offered a more efficient approach to individualized diagnosis of TD and had substantial practical value for clinical auxiliary diagnosis and intervention.https://ieeexplore.ieee.org/document/11036828/Tic disordersseverityelectroencephalogrammachine learninginterpretability |
| spellingShingle | Wanting Xiang Gang Zhu Yichong Hou Zhandong Mei Lin Wan Li Zhang Guang Yang Jian Zu Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data IEEE Transactions on Neural Systems and Rehabilitation Engineering Tic disorders severity electroencephalogram machine learning interpretability |
| title | Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data |
| title_full | Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data |
| title_fullStr | Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data |
| title_full_unstemmed | Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data |
| title_short | Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data |
| title_sort | development and validation of an interpretable machine learning model for predicting tic disorders and severity in children based on electroencephalogram data |
| topic | Tic disorders severity electroencephalogram machine learning interpretability |
| url | https://ieeexplore.ieee.org/document/11036828/ |
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