一种基于改进经验模态分解算法的脉搏波无损检测方法及系统
By improving the empirical mode decomposition algorithm, a heartbeat and breathing model is established and noise is added to decompose and distinguish the pulse wave signal. The wavelet soft thresholding method is used to remove noise, which solves the problems of invasiveness and large error in traditional pulse wave detection and achieves efficient and accurate non-destructive detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HARBIN ENG UNIV
- Filing Date
- 2024-05-22
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional pulse wave detection methods are mostly invasive, increasing patient discomfort and having low accuracy. Non-invasive methods have large errors, and traditional filtering methods are computationally intensive and lack adaptability, making it difficult to effectively process complex nonlinear signals.
An improved empirical mode decomposition algorithm is adopted. By establishing a heartbeat and breathing model, noise is added to simulate the pulse wave signal, which is decomposed into IMFs components. The correlation coefficient and permutation entropy are used to distinguish between signal and noise. The wavelet soft thresholding method is combined to remove noise and reconstruct the denoised pulse wave signal.
It improves the accuracy and efficiency of pulse wave detection, reduces noise interference, retains more effective signal features, has a high signal-to-noise ratio and a small mean square error, and is suitable for physiological parameter extraction and health status assessment.
Smart Images
Figure CN118585748B_ABST