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

Shuxin Zhuang,Vijayarajan Rajangam,Wenbin Rao,Alex Noel Joseph Raj,Fenlan Li,Zhemin Zhuang,José Alfredo Hernández-Pérez

doi:10.1155/2021/1360414

Abstract

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.

Highlights

Respiratory signals are important physiological signals commonly used in clinical monitoring. ey are used in the detection of sleep apnoea and in stress tests; they play an important role in the clinical diagnosis of diseases [1]
The proposed ECG-derived respiration (EDR) methods are compared with three traditional EDR methods, including kernel principal component analysis (KPCA)-based [14], R-peaks-interval-based [23], and R-peaks-amplitudebased
The proposed method achieves a better performance than the three traditional EDR methods in extracting poor-quality RESP signals

Summary

Introduction

Respiratory signals are important physiological signals commonly used in clinical monitoring. ey are used in the detection of sleep apnoea and in stress tests; they play an important role in the clinical diagnosis of diseases [1]. E first is to detect the air flow from the human nose, and the second is to detect thoracic deformation or the change in thoracic impedance caused by respiration [2]. Both methods require additional sensors and may interfere with natural breathing. E idea of soft sensors is a one of the solutions to overcome the issues of detecting respiratory signals. Yan et al [5] proposed a framework of data driven soft sensor modeling based on semisupervised regression to estimate the total Kjeldahl nitrogen in a wastewater treatment process

Results

Discussion

Conclusion