Continuous blood pressure estimation from PPG signal

Blood pressure (BP) is an indicator of hypertension. We developed a system in which photoplethysmogram (PPG), which is commonly integrated in modern wearables, is used to continuously estimate BP. A preprocessing module was developed and used for cleaning the PPG signal of noise and artefacts, and segmenting it into cycles. A set of features describing the PPG signal was then computed to be used in regression models. The RReliefF algorithm was used to select a subset of relevant features and personalization of the models was considered to further improve the performance of the models. The approach was validated using two distinct datasets, one from a hospital environment, and the other collected during every-day activities. Using the clinical dataset (MIMIC database), the best achieved mean absolute errors (MAE) in a leave-one-subject-out (LOSO) experiment were 5.61 mmHg for systolic and 3.82 mmHg for diastolic BP, at maximum personalization. For everyday-life dataset, the lowest errors were 8.40 mmHg for systolic and 4.20 mmHg for diastolic BP. Deep learning regression and Random Forest algorithm achieved the best results. Our results borderline meet the requirements of the two most well-established standards for BP estimation devices

Continuous blood pressure estimation from PPG signal

Blood pressure (BP) is an indicator of hypertension. We developed a system in which photoplethysmogram (PPG), which is commonly integrated in modern wearables, is used to continuously estimate BP. A preprocessing module was developed and used for cleaning the PPG signal of noise and artefacts, and segmenting it into cycles. A set of features describing the PPG signal was then computed to be used in regression models. The RReliefF algorithm was used to select a subset of relevant features and personalization of the models was considered to further improve the performance of the models. The approach was validated using two distinct datasets, one from a hospital environment, and the other collected during every-day activities. Using the clinical dataset (MIMIC database), the best achieved mean absolute errors (MAE) in a leave-one-subject-out (LOSO) experiment were 5.61 mmHg for systolic and 3.82 mmHg for diastolic BP, at maximum personalization. For everyday-life dataset, the lowest errors were 8.40 mmHg for systolic and 4.20 mmHg for diastolic BP. Deep learning regression and Random Forest algorithm achieved the best results. Our results borderline meet the requirements of the two most well-established standards for BP estimation devices

Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study

Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT). Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly. Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor. Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy \u3e90% and correlations \u3e0.90 (P \u3c .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations

Comparison of pulse rate variability and morphological features of photoplethysmograms in estimation of blood pressure

Photoplethysmography is an optical technique that produces a wealth of information about cardiovascular health. Therefore, the technology has become an integral part of personal health monitoring devices. Given the importance of blood pressure measurement and control in physical and mental health, in recent years, the estimation of blood pressure from photoplethysmography has been an active area of research with promising results. Most studies on the subject rely on the morphological features of the photoplethysmogram. These features are highly prone to noise, changes in sensor placement, and skin properties; including skin colour. To address these limitations, we investigated the feasibility of using pulse rate variability features which are known to be less prone to the aforementioned limitations. To this end, we collected high quality photoplethysmograms using a bespoke, research-grade device from 18 healthy subjects. Approximately 15 min of photoplethysmograms and continuous blood pressure waveforms were collected from each subject. We trained machine learning models based on different feature sets and compared their performances. The model with morphological features alone outperformed the model with pulse rate variability features, root mean squared error (RMSE) of 6.32 vs 7.23 mmHg. However, the best performance was obtained using the combined set of features (RMSE: 5.71 mmHg). Combined, the evidence shows that the estimation of BP from PRV, alone or in conjunction with morphological features, is feasible. In light of the limitations of morphological features in estimation of blood pressure, our findings lend support to further research on the use of pulse rate variability features

Comparative analysis of photoplethysmography signal quality from right and left index fingers

Photoplethysmography (PPG) has emerged as an increasingly attractive signal for non-invasive physiological measurements, owing to its simplicity, cost-effectiveness, and broad applicability spanning cardiovascular to respiratory systems. The burgeoning interest in PPG signal processing has facilitated its extensive incorporation in wearable devices, thus stimulating active research in this field. The present study undertakes a comprehensive evaluation to discern the optimal index finger (right or left) for PPG data acquisition and subsequent filtration, appraised through the lens of the signal-to-noise ratio (SNR) of the filtered signal. An analysis conducted on signals contaminated with white Gaussian noise unveiled that the Savitzky-Golay filter (a polynomial filter) with a window size of three outperformed other window lengths, rendering the highest SNR. Among the Infinite Impulse Response (IIR) filters compared; the Chebyshev I filter emerged as superior. Interestingly, the right index finger consistently demonstrated a higher mean SNR across filters: 0.49% for the Savitzky-Golay filters, 4.32% for the Butterworth (order 6), 7.71% for the Chebyshev I (order 10), and 4.02% for the Chebyshev II (order 4), relative to the left index finger for PPG signals perturbed by white Gaussian noise. These findings provide an insightful perspective for future research and development in wearable devices, suggesting potential superiority of the right index finger for PPG signal acquisition and filtration

Wearable Technology Devices: Heart Rate and Step Count Analysis

The overarching purpose of this dissertation was to evaluate and analyze heart rate and/or step count measurements for six popular wearable technology devices: the Samsung Gear 2, FitBit Surge, Polar A360, Garmin Vivosmart HR+, Leaf Health Tracker, and the Scosche Rhythm+ in four separate conditions: free motion walking, free motion jogging, treadmill walking, and treadmill jogging. The four studies presented here utilized one test design and data collection protocol in which many measurements could be addressed simultaneously. Currently, there is no accepted standardized protocol to evaluate wearable technology devices. The test design utilized for this research series was introduced as a potential foundation for the establishment of a common procedure. There were three purposes for the first study in this series of four research projects. First, this study looked at whether the tested devices that recorded heart rate were reliable and valid in each of the four stated conditions. Only the Garmin Vivosmart HR+ and the Scosche Rhythm+ were significantly acceptable for all four conditions. Secondly, while all the tested devices used photoplethysmography to record heart rate, this technique has not been thoroughly validated for this purpose. Limited research indicates that devices that use this method as a measurement technique and are worn on the forearm are more accurate than those worn elsewhere on the body. Results from our study supported this conclusion. The Scosche Rhythm+, being a fore arm worn device, did produce more significantly acceptable results than the wrist worn Garmin Vivosmart HR+. Third, a standardized heart rate testing protocol has been introduced by the Consumer Technology Association. However, their recommended measurement criteria (a measurement every 1-5 seconds which would require special software to record) can be viewed as financially prohibitive, restrictive, and over compensating. The protocol used in our research presented evidence that ours, which used an average of several minutes of heart rate values, was easier to implement and did not required a financial investment to perform. The second study had two purposes. First, this study looked at whether the tested devices that recorded step count were reliable and valid in each of the four conditions. Only the FitBit Surge, Garmin Vivosmart HR+ and the Leaf Health Tracker were significantly acceptable for all four conditions. Secondly, the Consumer Technology Association has recommended a standardized step count protocol which would require the videotaping of an activity with separate tape reviews by two persons at a future time. This protocol is not feasible in certain conditions such as outside testing. Additionally, both reviewers would need to produce the exact same step count. Our testing used two manual counters where the mean of the two were used as the criterion measure. We provided strong evidence that this is an acceptable criterion measure for step counting that does not require additional time or resources. The third study compared heart rate and step count values measured by the tested devices between the different conditions. Measurements taken during free motion walking were compared to treadmill walking and those taken during free motion jogging were compared to treadmill jogging. It is generally believed that most wearable technology device companies perform device testing on a treadmill in a laboratory. Our conclusion was that there was no significant interaction or main effects for walking heart rate value comparisons. Jogging heart rate values saw significant main effects from both the environment and between the devices. Walking step count values had a significant interaction between the devices and the environment. Jogging step count values had a significant main effect between the devices. When utilizing wearable technology devices for the measurement of heart rate during walking or jogging, the Garmin Vivosmart HR+ and Rhythm Scosche Rhythm+ provided acceptable measures both in the laboratory as well as in a free motion environment. The FitBit Surge, Garmin Vivo Smart HR+, and the Leaf Health Tracker produced similar results for step count. The fourth study evaluated whether there was a correlation between both body composition percentages and body mass index values and the percent error calculated between a manual step count and that recorded by the wearable technology devices. Our results gave evidence that there are no significant correlations between body mass index and the calculated percent error. For body composition, only two conditions for the wrist worn devices had a positive significant correlation; the Samsung Gear 2 when free motion walking and the Garmin Vivosmart HR+ when free motion walking. The waist worn Leaf Activity Tracker had positive significant correlations for both treadmill walking and treadmill jogging. Even though our study produced four conditions with significant correlations, all were low to moderate in value

Continuous blood pressure estimation from PPG signal

Krvni tlak je pomemben pokazatelj hipertenzije. Razvili smo sistem, ki krvni tlak ocenjuje iz fotopletizmograma (PPG), kakršen je že vgrajen v večino modernih senzorskih zapestnic. Zaradi šuma in motenj, ki se v signalu PPG pojavijo kot posledica uporabe zapestnice, smo razvili metodo čiščenja in segmentiranja signala PPG na cikle. Nato smo izračunali množico značilk, ki smo jih uporabili v regresijskih modelih. Sistem smo izboljšali z uporabo algoritma RReliefF za izbor najboljših značilk in z uporabo dela podatkov vsake osebe za učenje personaliziranih napovednih modelov. Sistem smo vrednotili na dveh podatkovnih množicah, eni iz kliničnega okolja in drugi zbrani med rutinskimi dnevnimi aktivnostmi posameznikov. V poizkusu, kjer model vsakič naučimo na vseh osebah razen eni in ga nato testiramo na izpuščeni osebi, smo z uporabo klinične množice (podatkovna baza MIMIC) dosegli najnižjo povprečno absolutno napako (MAE) 5,61 mmHg za sistolični in 3,82 mmHg za diastolični krvni tlak, oboje pri največji stopnji personalizacije. Za množico, zbrano med rutinskimi dnevnimi aktivnostmi, smo dosegli najnižjo MAE 8.40 mmHg za sistolični in 4.20 mmHg za diastolični krvni tlak, ponovno pri največji stopnji personalizacije. Najbolje sta se obnesla algoritma globoka regresija in ``naključni gozd\u27\u27. Rezultati skoraj dosegajo zahteve dveh glavnih standardov za ocenjevanje krvenga tlaka.Blood pressure (BP) is an indicator of hypertension. We developed a system in which photoplethysmogram (PPG), which is commonly integrated in modern wearables, is used to continuously estimate BP. A preprocessing module was developed and used for cleaning the PPG signal of noise and artefacts, and segmenting it into cycles. A set of features describing the PPG signal was then computed to be used in regression models. The RReliefF algorithm was used to select a subset of relevant features and personalization of the models was considered to further improve the performance of the models. The approach was validated using two distinct datasets, one from a hospital environment, and the other collected during every-day activities. Using the clinical dataset (MIMIC database), the best achieved mean absolute errors (MAE) in a leave-one-subject-out (LOSO) experiment were 5.61 mmHg for systolic and 3.82 mmHg for diastolic BP, at maximum personalization. For everyday-life dataset, the lowest errors were 8.40 mmHg for systolic and 4.20 mmHg for diastolic BP. Deep learning regression and Random Forest algorithm achieved the best results. Our results borderline meet the requirements of the two most well-established standards for BP estimation devices