BioMediTech Research Groups

Physiological Measurement Systems and Methods

Group Leader: PhD Jari Viik


About Us

Currently, our group consists of five members: there are one associate professor and four PhD researchers in our group. Expertise of our group members cover the major aspects of research and development of non-invasive physiological measurement systems, including electronics design, linear and nonlinear signal processing, statistical analysis, as well as, clinical test planning and product commercialization.

Research interests and expertise

Our main interest is to develop more accurate and well-aimed non-invasive measurements and analysis methods for the detection of cardiopulmonary and wound diseases. The utilization is intended for hospitals and health centers, but also for outpatients. The aim in the outpatient studies has been to evaluate changes in physiological and psychological effects arising from specific interventions. The most applied measurement modalities are based on, but not limited to, electrical and bioimpedance techniques.

Our group continuously, intensively and productively co-operates with the Department of Clinical Physiology, the Department of Cardiology, the Heart Center and the Department of Clinical Chemistry of Tampere University Hospital. We also have a close co-operation with the Medical School, the School of Public Health in the University of Tampere, and The Allergy Unit in Helsinki University Hospital.


We have developed a novel parameter, the ST/HR hysteresis, for the detection of ischemia during the clinical exercise test. The ST/HR hysteresis is implemented on exercise ECG workstation (CardioSoft™ Diagnostic System), which is manufactured by the market leader GE Healthcare. We have also developed a novel method based on impedance pneumography (IP), which provides new opportunities to evaluate respiratory symptoms and diseases, especially with children and infants. The Finnish healthcare company Revenio has launched a commercialization project based on the licenses of our asthma detection innovations and patents.


We have a laboratory for physiological measurements. It is equipped with all the most important non-invasive devices for physiological measurements. We have the BioPac with extensive number of sensors for various purposes, several devices for the measurement of ECG, EEG, EMG, EOG, heart rate, respiratory functions, movement, steps, etc. We also have a fully portable polysomnography system, the Nox A1 PSG System, for sleep study measurements at home.

Collaboration offer and requests

We can offer expertise in physiological signal measurements and analysis. We have conducted several projects with local and international partners. We also provide professionalism for the commercialization of research innovations. We are open to collaborations to develop new diagnostic and monitoring methods, including outpatient measurements as well.

Major Publications

  1. Svart K, Lehtinen R, Nieminen T, Nikus K, Lehtimäki T, Kööbi T, Niemelä K, Niemi M, Turjanmaa V, Kähönen M, Viik J. Exercise Electrocardiography Detection of Coronary Artery Disease by ST-segment Depression/Heart Rate Hysteresis in Women: The Finnish Cardiovascular Study. International Journal of Cardiology 2010;140(2):182-8. Doi: 10.1016/j.ijcard.2008.11.038
  2. Gracia J, Seppä V-P, Viik J, Hyttinen J. Multilead Measurement System for the Time-Domain Analysis of Bioimpedance Magnitude. IEEE Transactions on Biomedical Engineering 2012;59(8);2273-2280. Doi: 10.1109/TBME.2012.2202318
  3. Seppä V-P, Pelkonen A, Kotaniemi-Syrjänen A, Viik J, Mäkelä M, Malmberg P. Tidal Flow Variability Measured by Impedance Pneumography Relates to Childhood Asthma Risk. European Respiratory Journal 2016;47(6)1687-96. Doi: 10.1183/13993003.00989-2015
  4. Kekonen A, Bergelin M, Eriksson J-E, Vaalasti A, Ylänen H, Viik J. Bioimpedance Measurement Based Evaluation of Wound Healing. Physiological Measurements 2017;38(7):1373-1383. Doi: 10.1088/1361-6579/aa63d6
  5. Perez-Macias JM, Tenhunen M, Värri A, Himanen S-L, Viik J. Detection of Snores Using Source Separation on an Emfit Signal. IEEE Journal of Biomedical and Health Informatics 2017. Doi: 10.1109/JBHI.2017.2757530. [Epub ahead of print]
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