Integration of a Heart Rate Prediction Model into a Personal Health Record to Support the Telerehabilitation Training of Cardiopulmonary Patients - Biomedical Engineering Systems and Technologies - page 301

Integration of a Heart Rate Prediction Model into a
Personal Health Record to Support the Telerehabilitation
Training of Cardiopulmonary Patients
Axel Helmer 1 , Riana Deparade 2 , Friedrich Kretschmer 3 , Okko Lohmann 1 ,
Andreas Hein 1 , Michael Marschollek 4 , and Uwe Tegtbur 2
1 R&D Division Health, OFFIS Institute for Information Technology,
Escherweg 2, D-26121 Oldenburg, Germany
2 Institute of Sports Medicine, Medical School Hannover,
Carl-Neuberg-Strasse 1, D-30625 Hannover, Germany
3 Computational Neuroscience, University of Oldenburg,
Carl-von-Ossietzky-Strasse 9-11, D-26129 Oldenburg, Germany
4 Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig,
Institute of Technology and Hannover Medical School, Mühlenpfordtstr. 23,
D-38106 Braunschweig and Carl-Neuberg-Str. 1, D-30625 Hannover, Germany
{axel.helmer,okko.lohmann,andreas.hein}@offis.de,
{deparade.riana,tegtbur.uwe}@mh-hannover.de,
[e-mail],
[e-mail]
Abstract. Chronic obstructive pulmonary disease (COPD) and coronary ar-
tery disease are severe diseases with increasing prevalence. Studies show
that regular endurance exercise training affects the health state of patients
positively. Heart Rate (HR) is an important parameter that helps physicians
and (tele-) rehabilitation systems to assess and control exercise training in-
tensity and to ensure the patients' safety during the training. On the basis of
668 training sessions (325 F, 343 M), we created linear models predicting
the training HR during five application scenarios. Personal Health Records
(PHRs) are tools to support users to enter, manage and share their own
health data, but usage of current products suffers under interoperability and
acceptance problems. To overcome these problems, we implemented a PHR
that is physically localized in the user's home environment and that uses the
predictive linear models to support physicians during the training plan crea-
tion process. The prediction accuracy of the model varies with a median root
mean square error (RMSE) of ≈11 during the training plan creation scenario
up to ≈3.2 in the scenario where the prediction takes place at the beginning
of a training phase.
Keywords: Modeling, Heart rate, Prediction, Personal health record,
Cardiopulmonary rehabilitation.