Key result
The EEMD-ICA fusion method improved signal denoising (SNR increased from 2.15 to 11.36) and revealed significant differences in multi-scale percussion entropy between healthy and diabetic patients (p<0.01).
Why the study?
PPG and ECG signals reflect autonomic and vascular effects of diabetes, motivating a novel fusion multi-scale percussion entropy index method to analyze cardiovascular function in diabetic patients.
Does the EEMD-ICA fusion MSPEI method improve signal denoising and differentiation of cardiovascular function between diabetic and healthy patients compared to standard methods?
Does the EEMD-ICA fusion MSPEI method improve signal denoising and differentiation of cardiovascular function between diabetic and healthy patients compared to standard methods?
p-value: p=< 0.01
The proposed EEMD-ICA fusion MSPEI algorithm improves denoising of physiological signals and effectively differentiates autonomic and vascular changes in diabetic patients.
Hypothesis-generating for PPG-ECG fusion in diabetes detection; leaves open clinical utility pending larger trials.
BACKGROUND: Diabetes is a chronic disease that can lead to a variety of complications and even cause death. The signal characteristics of the photoplethysmography signals (PPG) and electrocardiogram signals (ECG) can reflect the autonomic and vascular aspects of the effects of diabetes on the body. OBJECTIVE: Based on the complex mechanism of interaction between PPG and ECG, a set of ensemble empirical mode decomposition-independent component analysis (EEMD-ICA) fusion multi-scale percussion entropy index (MSPEI) method was proposed to analyze cardiovascular function in diabetic patients. METHODS: Firstly, the original signal was decomposed into multiple Intrinsic Mode Function (IMFs) by ensemble empirical mode decomposition EEMD, principal components of IMF were extracted by independent component analysis (ICA), then the extracted principal components were reconstructed to eliminate the complex high and low frequency noise of physiological signals. In addition, the MSPEI was calculated for the ECG R-R interval and PPG amplitude sequence.(RRI and Amp) The results showed that, compared with EEMD method, the SNR of EEMD-ICA method increases from 2.1551 to 11.3642, and the root mean square error (RMSE) decreases from 0.0556 to 0.0067. This algorithm can improve the performance of denoising and retain more feature information. The large and small scale entropy of MSPEI (RRI,Amp) was significantly different between healthy and diabetic patients (p< 0.01). RESULTS: Compared with arteriosclerosis index (AI) and multi-scale cross-approximate entropy (MCAE): MSPEISS (RRI,Amp) indicated that diabetes can affect the activity of human autonomic nervous system, while MSPEILS (RRI,Amp) indicated that diabetes can cause or worsen arteriosclerosis. CONCLUSION: Multi-scale Percussion Entropy algorithm has more advantages in analyzing the influence of diabetes on human cardiovascular and autonomic nervous function.
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Li et al. (2023) studied Diabetes. EEMD-ICA fusion multi-scale percussion entropy index (MSPEI) vs. EEMD method / Healthy controls was evaluated on Signal-to-noise ratio (SNR) and root mean square error (RMSE) (p=< 0.01). The EEMD-ICA fusion method improved signal denoising (SNR increased from 2.15 to 11.36) and revealed significant differences in multi-scale percussion entropy between healthy and diabetic patients (p<0.01).
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