TY - JOUR A2 - Hernández-Pérez, José Alfredo AU - Zhuang, Shuxin AU - Li, Fenlan AU - Zhuang, Zhemin AU - Rao, Wenbin AU - Joseph Raj, Alex Noel AU - Rajangam, Vijayarajan PY - 2021 DA - 2021/10/15 TI - Improved ECG-Derived Respiration Using Empirical Wavelet Transform and Kernel Principal Component Analysis SP - 1360414 VL - 2021 AB - 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. SN - 1687-5265 UR - https://doi.org/10.1155/2021/1360414 DO - 10.1155/2021/1360414 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -