Ítem
Solo Metadatos
An improved ECG-derived respiration method using kernel principal component analysis
Título de la revista
Autores
Caicedo Dorado, Alexander
Varon, Carolina
Van Huffel, Sabine
Widjaja, Devy
Fecha
2011-09-18
Directores
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Editor
Engineering in Medicine and Biology Society
Citations
Métricas alternativas
Resumen
Abstract
Recent studies show that principal component analysis (PCA) of heart beats generates well-performing ECG-derived respiratory signals (EDR). This study aims at improving the performance of EDR signals using kernel PCA (kPCA). Kernel PCA is a generalization of PCA where nonlinearities in the data are taken into account for the decomposition. The performance of PCA and kPCA is evaluated by comparing the EDR signals to the reference respiratory signal. Correlation coefficients of 0.630 ± 0.189 and 0.675 ± 0.163, and magnitude squared coherence coefficients at respiratory frequency of 0.819 ± 0.229 and 0.894 ± 0.139 were obtained for PCA and kPCA respectively. The Wilcoxon signed rank test showed statistically significantly higher coefficients for kPCA than for PCA for both the correlation (p = 0.0257) and coherence (p = 0.0030) coefficients. To conclude, kPCA proves to outperform PCA in the extraction of a respiratory signal from single lead ECGs.
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Keywords
Principal component analysis , Kernel , Electrocardiography , Correlation , Coherence , Sensors , Optimization
Citations
