A nonlinear dynamic model for heart rate response to treadmill walking exercise

A nonlinear dynamic model for heart rate response to treadmill walking exercise, Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, 22-26 Aug. 2007]. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Technology, Sydney's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it A nonlinear dynamic model for heart rate response to treadmill walking exercise Teddy M. Cheng, Andrey V. Savkin, Branko G. Celler, Lu Wang, Steven W. Su Abstract-A dynamic model of the heart rate response to treadmill walking exercise is presented. The model is a feedback interconnected system; the subsystem in the forward path represents the neural response to exercise, while the subsystem in the feedback path describes the peripheral local response. The parameters of the model were estimated from 5 healthy adult male subjects, each undertaking 3 sets of walking exercise at different speeds. Simulated responses from the model closely match the experimental data both in the exercise and the recovery phases. The model will be useful in explaining the cardiovascular response to exercise and in the design of exercise protocols for individuals

Estimation of Walking Energy Expenditure by Using Support Vector Regression

Abstract-This paper develops a new predictor of walking energy expenditure from wireless measurements of body movements using triaxial accelerometers. Reliable data were collected from repeated walking experiments in different conditions on a treadmill with simultaneous measurement of expired oxygen and carbon dioxide. Support vector regression, a powerful non-linear regression method, was used to process and model the data. This novel processing method sets this investigation apart from existing papers. Good results were achieved in the robust estimation of walking related energy expenditure from a number of variables derived from triaxial accelerometer and treadmill speed

Automatic detection of left ventricular ejection time from a finger photoplethysmographic pulse oximetry waveform : comparison with Doppler aortic measurement

Left ventricular ejection time (LVET) is a useful measure of ventricular performance and preload. The present study explores a novel method of continuous LVET monitoring using a noninvasive finger hotoplethysmographic pulse oximetry waveform (PPG-POW). A method for the automatic beat-to-beat detection of LVET from the finger PPG-POW is presented based on a combination of derivative analysis, waveform averaging and rule-based logic. The performance of the detection method was evaluated on 13 healthy subjects during graded head-up tilt. Overall, the correlation between the PPG-POW derived LVET and the aortic flow derived LVET was high and significant (r = 0.897, p < 0.05). The bias was −14 ± 14 ms (mean ± SD), and the percentage error was 9.7%. Although these results would not be sufficient to satisfy the requirement for clinical evaluation of LVET when absolute accuracy was demanded, the strong correlation between the PPG-POW LVET and the aortic LVET on an intra-subject basis (r = 0.945 ± 0.043, mean ± SD) would support the application of PPG-POW to detect the directional change in LVET of an individual. This could be very useful for the early identification of progressive hypovolaemia or blood loss. The present study has demonstrated a promising approach to extract potentially useful information from a noninvasive, easy-to-obtain signal that could be readily acquired either from existing patient monitoring equipment or from inexpensive instrumentation. More extensive investigation is necessary to evaluate the applicability of the present approach in clinical care monitoring