An early intelligent classification method and device for alzheimer's disease

By filtering, reducing dimensions, and removing drift from EEG signals, and combining this with the Riemann machine learning algorithm, a standard feature set for early Alzheimer's disease is extracted. This solves the problems of high cost or insufficient accuracy in existing technologies, and enables low-cost, high-precision early Alzheimer's disease diagnosis.

CN115374830BActive Publication Date: 2025-10-17HAINAN UNIV +1
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Patent Information

Application Number
CN202211166337.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-23
Publication Date
2025-10-17
Estimated Expiration
2042-09-23

AI Technical Summary

Technical Problem

Existing methods for early screening of Alzheimer's disease, such as MRI imaging and cognitive scale assessments, suffer from high costs or insufficient diagnostic accuracy, and in particular, require a high level of education from the test subjects.

Method used

The Riemannian machine learning algorithm based on EEG signals is used to extract multi-frequency, cross-individual standard feature sets by filtering, dimensionality reduction and drift removal of EEG signals. The results are then classified using a hard voting mechanism to generate the final early Alzheimer's disease diagnosis.

Benefits of technology

It enables low-cost, high-precision early diagnosis of Alzheimer's disease, reduces the educational requirements for test subjects, and improves the accuracy of the test.

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Abstract

The present application relates to the technical field of life and health, in particular to an early intelligent classification method and device for Alzheimer's disease, according to the brain-computer interface stimulation paradigm of the language understanding of the spatial direction, the tested person makes an operation, and the preset brain interface machine extracts the electroencephalogram of the tested person; according to the characteristics of the multi-frequency band, cross-individual and insignificant difference of the electroencephalogram of the early Alzheimer's disease patient, the electroencephalogram is subjected to data dimension reduction processing and classification, the whole process has low detection cost, the cultural level requirement of the tested person is relatively low, and the detection precision is relatively high.
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