A parkinson's disease dyskinesia identification system based on cross-frequency coupling tensor decomposition

By employing cross-frequency coupled tensor decomposition and linear discriminant analysis, the problems of subjectivity and data imbalance in the identification of dyskinesia in Parkinson's disease are solved, achieving high accuracy in dyskinesia identification, which is applicable to closed-loop deep brain stimulation systems.

CN122030894BActive Publication Date: 2026-07-03ZHEJIANG UNIV
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Patent Information

Application Number
CN202610489478.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-14
Publication Date
2026-07-03
Estimated Expiration
2046-04-14

AI Technical Summary

Technical Problem

Existing methods for identifying dyskinesia in Parkinson's disease suffer from problems such as high subjectivity, poor specificity for dyskinesia, susceptibility to motion artifacts, identification bias due to data imbalance, and insufficient generalization ability.

Method used

A method based on cross-frequency coupling tensor decomposition is adopted. Through signal acquisition, cross-frequency coupling calculation, signal-to-noise ratio balancing processing and tensor decomposition feature extraction, a third-order tensor is constructed and decomposed, and then identified by a linear discriminant analysis model.

Benefits of technology

It effectively overcomes frequency drift, improves recognition accuracy, eliminates the effects of data imbalance, and achieves highly robust and high-purity feature extraction, meeting the real-time recognition requirements of closed-loop deep brain stimulation systems.

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Abstract

The application discloses a Parkinson's disease dyskinesia identification system based on cross-frequency coupling tensor decomposition, comprising: a signal acquisition and processing module, which is used for synchronously collecting local field potential signals of multiple sites of the brain and performing pretreatment; a cross-frequency coupling calculation module, which is used for calculating a cross-frequency coupling atlas of each data segment; a signal-to-noise ratio balance processing module, which is used for generating an average cross-frequency coupling atlas of each subject; a tensor decomposition feature extraction module, which is used for aggregating the average cross-frequency coupling atlas of all subjects, constructing a third-order tensor, and decomposing to obtain three tensor components; and when the average cross-frequency coupling atlas of a to-be-tested subject is input, a weight vector corresponding to the three tensor components is output; and a classification and identification module, which is used for inputting the weight vector of the to-be-tested subject into a pre-trained machine learning classification model, and outputting an identification result of Parkinson's disease dyskinesia. The application can overcome the frequency drift problem existing between different individuals and improve the identification accuracy.
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