Effects of polymorphisms in beta1-adrenoceptor and alpha-subunit of G protein on heart rate and blood pressure during exercise test. The finnish cardiovascular study.

J Appl Physiol vol 100, no 2, pp 507-511, 2006

Heart rate variability derived from exercise ECG in the detection of coronary artery disease.

Physiol Meas. 2007 Oct;28(10):1189-200. Epub 2007 Sep 18.

Intrapartum hypoxia and power spectral analysis of fetal heart rate variability

Reliable detection of intrapartum fetal acidosis is crucial for preventing morbidity. Hypoxia-related changes of fetal heart rate variability (FHRV) are controlled by the autonomic nervous system. Subtle changes in FHRV that cannot be identified by inspection can be detected and quantified by power spectral analysis. Sympathetic activity relates to low-frequency FHRV and parasympathetic activity to both low- and high-frequency FHRV. The aim was to study whether intra partum fetal acidosis can be detected by analyzing spectral powers of FHRV, and whether spectral powers associate with hypoxia-induced changes in the fetal electrocardiogram and with the pH of fetal blood samples taken intrapartum. The FHRV of 817 R-R interval recordings, collected as a part of European multicenter studies, were analyzed. Acidosis was defined as cord pH ≤ 7.05 or scalp pH ≤ 7.20, and metabolic acidosis as cord pH ≤ 7.05 and base deficit ≥ 12 mmol/l. Intrapartum hypoxia increased the spectral powers of FHRV. As fetal acidosis deepened, FHRV decreased: fetuses with significant birth acidosis had, after an initial increase, a drop in spectral powers near delivery, suggesting a breakdown of fetal compensation. Furthermore, a change in excess of 30% of the low-to-high frequency ratio of FHRV was associated with fetal metabolic acidosis. The results suggest that a decrease in the spectral powers of FHRV signals concern for fetal wellbeing. A single measure alone cannot be used to reveal fetal hypoxia since the spectral powers vary widely intra-individually. With technical developments, continuous assessment of intra-individual changes in spectral powers of FHRV might aid in the detection of fetal compromise due to hypoxia.

Heart rate variability in young adults - Reference values and associations with cardiometabolic risk factors and vascular properties. The Cardiovascular Risk in Young Finns Study.

Background: The function of the autonomic nervous system (ANS) can be evaluated with heart rate variability (HRV). Decreased HRV is associated with aging, the male sex, increased heart rate, and overall increased cardiometabolic risk. It has been hypothesized that early atherosclerotic vascular changes and ANS function are related. Aims: The aims were to assess reference values on HRV in young adults, and examine associations with HRV and cardiometabolic risk factors and metabolic syndrome (MetS) and to study relations between HRV and ultrasonographically measured vascular properties. Participants and methods: The present thesis is part of the Cardiovascular Risk in Young Finns Study. The thesis is based on the follow-up study in 2001, when the study individuals were 24-39 years of age. HRV data were available on 1 956 individuals. Results: HRV was inversely associated with age and heart rate (for all p<0.001). Highfrequency HRV (HF) was higher, and low-frequency HRV (LF) lower in women than men (p<0.0001 for both). MetS was associated with 11% decreased HF and 12% increased LF/HF-ratio in women, and 8% decreased HF and 4% increased LF/HF-ratio in men. Carotid artery distensibility was independently associated with HF and total HRV (for both p<0.05). Conclusions: The reference values in young adults were generated. Decreased HRV was associated with age, the male sex and increased heart rate. Women had higher HF and lower LF variability than men. MetS was related to decrease in HRV. The observed associations between carotid elasticity and HRV, supports the hypothesis that reduction in carotid elasticity may lead to decrease in autonomic cardiac control.

IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.