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Optical heart rate monitoring (OHR) with reflective wrist photoplethysmography is a technique mainly used in the wellness application domain for monitoring heart rate levels during exercise. In the absence of motion, OHR technique is also able to estimate individual beat-to-beat intervals relatively well and can therefore also be used, for example, in monitoring of cardiac arrhythmias, stress, or sleep quality through heart rate variability (HRV) analysis. HRV analysis has also potential in monitoring the recovery of patients, e.g. after a medical intervention. However, in order to detect subtle changes, the calculated HRV parameters should be sufficiently accurate and very few studies exist that asses the accuracy of OHR derived HRV in non-healthy subjects. In this paper, we present a method to estimate beat-to-beat-intervals (BBIs) from reflective wrist PPG signal and evaluated the accuracy of the proposed method in estimating BBIs in a cross-sectional study with 29 hospitalized patients (mean age 70.6 years) in 24-h recordings performed after peripheral vascular surgery or endovascular interventions. Finally, we evaluate the accuracy of more than 30 commonly used HRV parameters and find that the accuracy of certain metrics, for example SDNN and triangular index, shown in the literature to be associated with the deterioration of the status of the patients during recovery from surgical intervention, could be adequate for patient monitoring. On the other hand, the parameters more affected by the high-frequency content of the HRV and especially the LF/HF-ratio should be used with caution. publishedVersion

Objective: The aim of the study was to show if pulse rise times (PRTs) extracted from photoplethysmographic (PPG) pulse waves (PWs) have an association with peripheral arterial disease (PAD) or its endovascular treatment, percutanoeus transluminal angioplasty (PTA) of the superficial femoral artery. Methods: Lower and upper limb PPG PWs were recorded and analyzed from 24 patients who suffered from PAD. The measurements were conducted before and after the treatment, and one month later by using transmission-mode PPG-probes placed in the index finger and second toe. Ankle-to-brachial pressure index and toe pressures were used as references in clinical patient measurements. PRTs, i.e., the time from the foot point to the peak point of the PW, were extracted from the PWs and compared bilaterally. The results from the PAD patients were also compared with 31 same-aged and 34 younger control subjects. Results: Statistically significant differences were found between the pretreatment PRTs of the treated limb of the PAD patients and the same-aged control subjects ( $p< 10^{-9}$ , Mann–Whitney U-test). The changes in the PRT of the treated lower limb were observed immediately after the PTA ( $p< 0.001$ , Student's t -test), and after one month ( $p< 0.0005$ ), whereas the PRTs of the non-treated lower limb and upper limb did not indicate changes between different examinations. Conclusion: Results show that a PRT greater than 240 ms indicates PAD-lesions in the lower limb. Significance: This proof-of-concept study suggests that the PRT could be an effective and easy-to-use indicator for PAD and monitoring the effectiveness of its treatment. acceptedVersion

Impedance plethysmographic signals recorded with electrodes attached on the human body provide information on hemodynamics and thus the condition of the arteries. The main objective of the thesis is to study if the quantitative analysis of the bioimpedance signals provides additional information on the risk for cardiovascular diseases compared with clinical parameters currently used in the assessment of the cardiovascular risk of an individual. This thesis aims to answer three main research questions: 1) are pulse wave parameters able to evaluate the condition of the arteries, 2) could pulse wave parameters provide information equal to the clinical data, and 3) could the impedance measurements be utilized for cardiovascular risk stratification. This thesis analyzes the bioimpedance signals and clinical data collected in the Cardiovascular Risk in Young Finns Study (YFS). The test subjects were 30–45 years old when the data was collected. In this thesis, both frequency and time domain features including pulse wave decompositions are computed from the pulse waves extracted from the bioimpedance signals and their dependence on clinical phenotypes based on YFS data is evaluated. The YFS data contains demographic information (sex, age), anthropometric data (body mass index (BMI)), clinical information (smoking, hypertension, antihypertensive medication), clinical physiologic data (pulse wave velocity (PWV), blood pressure, heart rate, flow-mediated dilation (FMD)), laboratory analyses (fasting insulin and glucose, lipids of the blood) and imaging data (intima-media thickness (IMT), presence of atherosclerotic plaques in the internal carotid artery). The data was measured from 1853 test subjects, but after removal of test subjects with interrupted measurement or with low signal-to-noise ratio, there are 1738 test subjects used in this thesis. Besides the linear regression analysis, which was implemented to study the association between individual pulse wave signal derived features and clinical reference values, following supervised machine learning methods: linear and quadratic discriminant analysis, support vector machines, naïve Bayes, AdaBoost, Random Forest and k-nearest neighbor are applied to answer the objectives of this thesis. A cross-validation and forward selection are applied to find the most relevant pulse wave features that most accurately classify the test subjects. The results are evaluated with receiver operating characteristics (ROC) curve analysis. This thesis uses three different labeling methods to determine the ground truth for each subject being at low or high risk for cardiovascular diseases: 1) selected cardiovascular risk factors, 2) abnormal body mass index (BMI), blood glucose, triglycerides, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol or total cholesterol, and 3) existence of atherosclerotic plaque, hypertension or antihypertensive medication. As a result, the calculated pulse wave parameters provide independent information from the clinical data about the condition of the arteries because the combination of pulse wave parameters and clinical data provided the best classification results in most of the cases. However, the calculated pulse wave parameters alone do not provide as good information as the clinical data, which is shown by the fact that the classifying result with only clinical data was better than the classifying result with only pulse wave parameters. As a conclusion, risk stratification improves when the clinical data and the pulse wave parameters are combined. However, the analysis methods of signal processing should be optimized for the bioimpedance measurements. Further in order to verify the classification performance of the developed methods, the data should contain wider spectrum of people, from those who have diagnosed cardiovascular diseases to those who do not have such diseases.