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Analysis of exercise ventilation with autoregressive model and Hilbert-Huang transform
Hsueh-Ting Chu, Tieh-Cheng Fu
185 - 192
10.3233/978-1-61499-484-8-185
Frontiers in Artificial Intelligence and Applications
Volume 274: Intelligent Systems and Applications
Abstract

In this study, we propose a method for the quantitative analysis of exercise ventilation signal that is an important diagnostic tool to evaluate the prognosis of chronic heart failure (CHF) patients. An autoregressive (AR) model is used to filter the breath-by-breath measurement of ventilation. Then the signals before reaching the most ventilation are decomposed into intrinsic mode functions (IMF) by Hilbert-Huang transform (HHT). The average amplitude of the second IMF component AmpC2 is used as an indicator about pulmonary function for chronic heart failure patients.

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