Dizziness electroencephalogram signal detection and classification method based on deep learning

Through a deep learning-based method, combined with the vestibular electrical stimulation and vertigo disorder scale, the advanced features of EEG signals are extracted, and the problem of difficulty in accurately distinguishing different vertigo status in the prior art is solved, achieving a more accurate and efficient vertigo diagnosis.

CN120189132AInactive Publication Date: 2025-06-24HEBEI UNIV OF TECH
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Patent Information

Application Number
CN202510404501.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-01
Publication Date
2025-06-24
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The prior art is difficult to accurately distinguish and identify different vertigo states through a single EEG frequency reference standard, and there is a lack of deep learning detection and classification methods specifically for vertigo-related EEG signals.

Method used

Using a deep learning-based method, combined with the vestibular electrical stimulation and vertigo disorder scale, the advanced features of EEG signals are extracted through deep learning algorithms, and a personalized model is established to achieve accurate detection and classification of EEG signals of vertigo.

Benefits of technology

This method can more comprehensively and meticulously analyze the EEG signals of patients with vertigo, improve the accuracy and efficiency of diagnosis, provide more appropriate treatment plans, and reduce treatment costs.

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Abstract

The invention relates to the technical field of biomedical engineering, and discloses a deep learning-based dizziness electroencephalogram signal detection and classification method, which comprises the following steps of: 1, helping a subject to wear and use electroencephalogram acquisition equipment by a professional; 2, determining a skin sensing threshold value, performing vestibular electrical stimulation by using current which is one time, two times and four times of the intensity of the skin sensing threshold value to trigger transient dizziness, and recording an electroencephalogram signal; 3, requiring a testee to fill in a dizziness disorder scale before and after stimulation; 4, the collected dizziness electroencephalogram signals are processed; 5, dizziness grades of the processed dizziness electroencephalogram signals are divided into different types according to the result of the dizziness disorder scale. According to the dizziness electroencephalogram signal detection and classification method based on deep learning, dizziness electroencephalograms are classified through the deep learning technology, manual errors are reduced, and meanwhile the clinical diagnosis accuracy and efficiency of dizziness symptoms are remarkably improved.
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