Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration
- 3 February 2012
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 59 (4), 1169-1176
- https://doi.org/10.1109/tbme.2012.2186448
Abstract
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.Keywords
This publication has 12 references indexed in Scilit:
- Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECGIEEE Transactions on Information Technology in Biomedicine, 2009
- Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived RespirationIEEE Transactions on Biomedical Engineering, 2009
- Comparison of Respiratory Rates Derived from Heart Rate Variability, ECG Amplitude, and Nasal/Oral AirflowAnnals of Biomedical Engineering, 2008
- A comparison of algorithms for estimation of a respiratory signal from the surface electrocardiogramComputers in Biology and Medicine, 2007
- Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoeaIEEE Transactions on Biomedical Engineering, 2003
- Least Squares Support Vector MachinesPublished by World Scientific Pub Co Pte Ltd ,2002
- Nonlinear Component Analysis as a Kernel Eigenvalue ProblemNeural Computation, 1998
- The Nature of Statistical Learning TheoryPublished by Springer Science and Business Media LLC ,1995
- The effect of respiration-induced heart movements on the ECGIEEE Transactions on Biomedical Engineering, 1989
- A Real-Time QRS Detection AlgorithmIEEE Transactions on Biomedical Engineering, 1985