• Suvi Tiinanen, Kai Noponen, Mikko Tulppo, Antti Kiviniemi, and Tapio Seppänen.
• Department of Computer Science and Engineering, University of Oulu, PO Box 4500, 90014 Oulu, Finland. Electronic address: suvi.tiinanen@ee.oulu.fi.
• Med Eng Phys. 2015 May 1; 37 (5): 512-7.

AbstractRespiration is an important signal in early diagnostics, prediction, and treatment of several diseases. Moreover, a growing trend toward ambulatory measurements outside laboratory environments encourages developing indirect measurement methods such as ECG derived respiration (EDR). Recently, decomposition techniques like principal component analysis (PCA), and its nonlinear version, kernel PCA (KPCA), have been used to derive a surrogate respiration signal from single-channel ECG. In this paper, we propose an adapted independent component analysis (AICA) algorithm to obtain EDR signal, and extend the normal linear PCA technique based on the best principal component (PC) selection (APCA, adapted PCA) to improve its performance further. We also demonstrate that the usage of smoothing spline resampling and bandpass-filtering improve the performance of all EDR methods. Compared with other recent EDR methods using correlation coefficient and magnitude squared coherence, the proposed AICA and APCA yield a statistically significant improvement with correlations 0.84, 0.82, 0.76 and coherences 0.90, 0.91, 0.85 between reference respiration and AICA, APCA and KPCA, respectively. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

17 keywords...

hide…