Scientific Publications Related to Physiological Modeling, Signal Processing and Mathematical Modeling

The publications section presents studies where Firstbeat solutions have been exploited.  The different scientific contexts include for example occupational health care, sleep research, psychotherapy, behavioral research, and sports coaching. Firstbeat methods have been used for research in over 100 universities and research institutions across Europe, North America, and Australia.

Title Author Year Journal/Proceedings Study design & Population Category Physiological parameter(s) Insights on findings
Energy expenditure can be accurately estimated from HR without individual laboratory calibration.
Pulkkinen A, Saalasti S & Rusko H 2005 ACSM congress Cross-sectional study: Adult, n = 32 Validity VO2; Energy expenditure Firstbeat method provided an accurate and practical method for estimating energy expenditure without individual laboratory calibration making it especially suitable for field use
Artefact correction for heart beat and interval data Saalasti S, Seppänen M & Kuusela A 2004 ProBisi Meeting, Jyväskylä 1.10.2004 Mathematical; uses R-R interval data Validity HRV The RR-interval artefact correction method by Firstbeat was proven to be fast, very accurate and reliable as it was able to find the correct HR level even in situations where half of the data were artefacts.
On- and Off Dynamics and Respiration Rate Enhance the Accuracy of Heart Rate Based VO2 Estimation
Pulkkinen A, Kettunen J, Martinmäki K, Saalasti S & Rusko H 2004 ACSM congress Cross-sectional study; Adult, n = 32 Validity VO2 HR-based VO2 estimation can be enhanced using information on On- and Off-dynamics and respiration rate that can be derived reliably using beat-to-beat RR-interval data only
Neural networks for heart rate time series analysis
Saalasti S 2003 Academic Dissertation. Department of Mathematical Information Technology, University of Jyväskylä, Finland Mathematical: uses R-R interval data Academic Dissertation HRV, EPOC, Respiration rate The study introduced methodology utilizing neural network modeling for interpreting heart rate data, and provided several innovations combining human physiology and mathematical modeling
Rusko et al. (2003). Pre-Prediction of EPOC: A Tool for Monitoring Fatigue Accumulation during Exercise?
Rusko H, Pulkkinen A, Saalasti S, Hynynen E & Kettunen J 2003 ACSM congress Meta-analysis of 48 peer-reviewed articles; Adult, n = 32 in validation dataset Validity EPOC EPOC can be pre-predicted by using only heartbeat data during exercise