Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data

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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Bibliographic Details
Main Authors: Wanting Xiang, Gang Zhu, Yichong Hou, Zhandong Mei, Lin Wan, Li Zhang, Guang Yang, Jian Zu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Tic disorders
severity
electroencephalogram
machine learning
interpretability
Online Access:https://ieeexplore.ieee.org/document/11036828/
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