一种基于深度学习的阿尔兹海默症患者脑熵监测方法及系统
By using deep learning-based methods to perform signal denoising, feature extraction, and pattern recognition on brain signals from Alzheimer's patients, this approach solves the problem of accurately monitoring disease progression in existing technologies. It achieves fully automated analysis from raw signals to precise early warnings, improving the accuracy and personalization of disease progression monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ANHUI UNIV OF SCI & TECH
- Filing Date
- 2025-11-03
- Publication Date
- 2026-07-17
AI Technical Summary
Existing Alzheimer's disease monitoring methods are unable to fully capture the dynamic changes in brain activity. In particular, when faced with individual differences and the diversity of disease development, they cannot accurately distinguish the characteristics of different stages of the disease, resulting in inaccurate judgment of disease progression and missing the critical opportunity for early intervention.
Employing a deep learning-based approach, this method achieves fully automated analysis from raw signals to precise warnings through signal denoising, feature extraction, pattern recognition, and classification-based early warning. This includes collecting brain signal data, denoising, extracting brain entropy index sequences, pattern recognition, classification, and key node evaluation, ultimately outputting personalized progression warning signals.
It improves the accuracy and personalization of disease progression monitoring, providing patients with timely early warnings and personalized intervention plans.
Smart Images
Figure CN121460128B_ABST