Modeling of ambulatory heart rate using linear and neural network approaches

Kolodyazhniy, Vitaliy and Pfaltz, Monique C. and Wilhelm, Frank H.. (2007) Modeling of ambulatory heart rate using linear and neural network approaches. In: Pattern Recognition in Biology. New York, pp. 191-206.

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Official URL: http://edoc.unibas.ch/dok/A5250618

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This chapter presents new results on modeling 24 hour (circadian) human heart rate data collected with the LifeShirt system using a variety of linear regression and neural network models. Such modeling is important in biopsychology, chronobiology, and chronomedicine where signals collected continuously from human subjects for one or several days need to be interpreted. Ambulatory heart rate is influenced by a variety of factors, including physical activity, posture, and respiration, and our models try to predict heart rate based on these factors. The analyses described in the chapter indicate that neural and especially neuro-fuzzy techniques provide better results in the modelling of human heart rate at the circadian scale than conventional linear regression. The advantages of the neuro-fuzzy approaches consist in their computational efficiency, better interpretability, and the possibility of incorporation of prior knowledge for easier model construction.
Faculties and Departments:07 Faculty of Psychology
UniBasel Contributors:Pfaltz, Monique Christine and Wilhelm, Frank H
Item Type:Book Section, refereed
Book Section Subtype:Further Contribution in a Book
Publisher:Nova Science
Note:Publication type according to Uni Basel Research Database: Book item
edoc DOI:
Last Modified:27 Dec 2018 13:07
Deposited On:08 Jun 2012 06:52

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