Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both ), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.