title: ECG-derived respiration methods: adapted ICA and PCA.
abstract: Respiration 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.
authors: Department of Computer Science and Engineering, University of Oulu, PO Box 4500, 90014 Oulu, Finland. Electronic address: suvi.tiinanen@ee.oulu.fi.
authors: Suvi
authors: S
authors: Tiinanen
authors: Department of Computer Science and Engineering, University of Oulu, PO Box 4500, 90014 Oulu, Finland.
authors: Kai
authors: K
authors: Noponen
authors: Department of Exercise and Medical Physiology, Verve Research, Oulu, Finland; Department of Applied Sciences, London South Bank University, London, UK.
authors: Mikko
authors: M
authors: Tulppo
authors: Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland.
authors: Antti
authors: A
authors: Kiviniemi
authors: Department of Computer Science and Engineering, University of Oulu, PO Box 4500, 90014 Oulu, Finland.
authors: Tapio
authors: T
authors: Seppänen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016014:Linear Models; D025341:Principal Component Analysis; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: ECG-derived respiration; ICA; PCA