Improved ECG-Derived Respiration Using Empirical Wavelet Transform and Kernel Principal Component Analysis

Autor: Fenlan Li, Alex Noel Joseph Raj, Vijayarajan Rajangam, Zhemin Zhuang, Shuxin Zhuang, Wenbin Rao
Rok vydání: 2021
Předmět:
Zdroj: Computational Intelligence and Neuroscience
Computational Intelligence and Neuroscience, Vol 2021 (2021)
ISSN: 1687-5273
1687-5265
Popis: Many methods have been developed to derive respiration signals from electrocardiograms (ECGs). However, traditional methods have two main issues: (1) focusing on certain specific morphological characteristics and (2) not considering the nonlinear relationship between ECGs and respiration. In this paper, an improved ECG-derived respiration (EDR) based on empirical wavelet transform (EWT) and kernel principal component analysis (KPCA) is proposed. To tackle the first problem, EWT is introduced to decompose the ECG signal to extract the low-frequency part. To tackle the second issue, KPCA and preimaging are introduced to capture the nonlinear relationship between ECGs and respiration. The parameter selection of the radial basis function kernel in KPCA is also improved, ensuring accuracy and a reduction in computational cost. The correlation coefficient and amplitude square coherence coefficient are used as metrics to carry out quantitative and qualitative comparisons with three traditional EDR algorithms. The results show that the proposed method performs better than the traditional EDR algorithms in obtaining single-lead-EDR signals.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje