Cuff-less continuous blood pressure monitoring system using pulse transit time techniques

This paper describes the development of a continuous cuff-less blood pressure system based on the pulse transit time (PTT) technique. In this study, PTT is defined by two different approaches denoted as PTT1 and PTT2. PTT1 is the time difference between the R-wave peak of the Electrocardiogram (ECG) and the peak of the Photoplethysmogram (PPG). PTT2 is the time difference between two peak PPG signals on same cardiac cycle at different positions on the body. The ECG is acquired on the chest using 3 lead electrodes and a reflection mode optical sensor is deployed on brachial artery and fingertip to monitor the PPGs. These data were synchronized using a National Instruments data acquisition card along with Matlab software for subsequent analysis. A wrist-type cuff-based blood pressure device was used to measure blood pressure on the right hand. Brachial blood pressure was measured on the upper left arm using oscillometric blood pressure monitor. Experiments were conducted by elevating the right hand at different position to investigate variability of PTT under the effects of hydrostatic pressure. Next the variability of PTT due to blood pressure changes during a Valsalva maneuver was investigated. The result shows that the PTT1 is inversely proportional to blood pressure in both experiments. Meanwhile, there is weak correlation between PTT2 and blood pressure measurement which suggests that by excluding the pre-ejection period (PEP) time in PTT calculation may reduce the accuracy of PTT for blood pressure measurement. In conclusion, PTT measurement between ECG and PPG signals has potential to be a reliable technique for cuff-less blood pressure measurement

Innovative technologies in hypertension control A telemonitored cuff-less blood pressure device

Introduction: Known as the most prevalent cardiovascular risk factor, hypertension, or High Blood Pressure (HBP), affects up to 1.13 billion people worldwide. It is responsible for up to 45% of deaths from cardiovascular disease and 51% of deaths from stroke. It is currently known that one of the reasons is that patients with hypertension are at greater risk of developing atrial fibrillation (AF) and consequent arterial thromboembolism to the cerebral circulation. It has been showed that reducing and maintaining blood pressure (BP) in hypertensive patients at levels considered ideal has a significant impact in reducing associated mortality and morbidity. Thus, the control of hypertension becomes essential. In addition to patient involvement, some technologies have showed potential in improving this control, such as telemonitoring and AF detection algorithms in BP monitoring devices. New devices have attempted to encompass telemonitoring, AF detection and greater ease of use such as smartwatches or other wrist meters with or without a cuff, however, most fail to obtain certification and clinical validation. In this study, a new portable cuff-less BP meter, which includes heart rate measurement, telemonitoring and detection of suspected AF rhythms is evaluated. The measurements are carried out in about 10 seconds at the wrist level and the device has the advantage of being already certified and clinically validated by the European Society of Hypertension. Objective: Evaluate the opinion of a group of patients with hypertension about a new portable and cuffless BP meter (FreeScan®, Maisense) after using it for 1 month through a questionnaire and evaluate the number of recommended measurements performed. Material and methods: Patients being followed up at HBP consultation who had access to a smartphone (necessary for telemonitoring) were invited to participate in the study. A Freescan® device was provided, it was demonstrated how it function and the calibration with a digital BP meter with arm cuff (Rossmax X5®) was made. For followup, participants were asked to take 2 consecutive measurements in the morning before taking medication for HBP and 2 consecutive measurements at the end of the day, preferably before dinner. After the 4 weeks, a meeting was arranged to collect the device and to deliver a questionnaire, in order to assess the participant's opinion on different aspects of the Freescan® device. Results: We obtained a final sample of 20 participants. The mean age was 57.15 (standard deviation (SD) 9.88) years. The average number of days with at least 1 measurement was 23,05 (SD 8,80) corresponding to 82,32% of the 28 days. The average number of recommended measurements taken was 61,80 (SD 36,05) corresponding to 55,18%. In the questionnaires, 95% answered that it was easy to learn how to use the device (answer 4 “I agree” or 5 “I totally agree”), 80% that it was easy to take BP measurements (4 or 5), 70% that they trusted the BP values (4 or 5), 100% that it was easy to telemonitor the data (4 or 5), 100% gave great importance to the doctor receiving constantly the data (4 or 5) and 70% preferred the Freescan® over cuff-based BP monitors. During the study, 7 cases of non-controlled hypertension and 3 cases with at least one suspected AF measurement were reported to the responsible physician. The number of recommended measurements taken was not influenced by age or sex. Conclusions: In this prospective study of hypertensive patients followed up in hypertension hospital consultation, most participants preferred Freescan® over cuffbased BP monitors and there was a good adaptation to the new technology as well as a good adherence to the number of measurements recommended independent of age or sex. That leads us to conclude that these new devices may have an important role in the follow-up of patients with hypertension, in order to increase control and decrease the mortality and morbidity associated with this condition.Introdução: Conhecido como fator de risco cardiovascular mais prevalente, a Hipertensão arterial (HTA) afeta cerca de 1,13 mil milhões de pessoas em todo o mundo. É apontada como causa de até 45% das mortes por doença cardiovascular e 51% das mortes por Acidente Vascular Cerebral (AVC). Sabe-se atualmente que uma das razões é o facto dos doentes com HTA terem maior risco de desenvolver Fibrilhação Auricular (FA) e consequente embolização de trombos auriculares para a circulação cerebral. Foi demostrado que reduzir e manter a pressão arterial (PA) de doentes hipertensos em níveis considerados ideais tem um impacto significativo na redução da mortalidade e morbilidade associada. Assim, o controlo da doença hipertensiva torna-se fundamental. Além do envolvimento do doente, algumas tecnologias têm-se mostrado promissoras no sentido de melhorar esse controlo como a telemonitorização e os algoritmos de deteção de FA nos medidores de PA. Novos dispositivos têm tentado englobar a telemonitorização, a deteção de FA e uma maior facilidade de utilização como smartwatches ou outros medidores de pulso com ou sem braçadeira, no entanto, a maioria falha em obter certificação e validação clínica. Neste estudo é avaliado um novo medidor de PA portátil, sem braçadeira, que engloba também medição da frequência cardíaca, telemonitorização e deteção de ritmos suspeitos de FA. A medição é realizada em cerca de 10 segundos ao nível do pulso e tem a vantagem de estar já certificado e clinicamente validado pela Sociedade Europeia de Hipertensão. Objetivo: Avaliar a opinião de doentes com HTA na utilização de um novo dispositivo portátil sem braçadeira (Freescan®, Maisense) após 1 mês de utilização, através de questionário, e ainda avaliar o número de medições recomendadas realizadas. Material e Métodos: Foram recrutados doentes seguidos em consulta de HTA que tivessem acesso a um smartphone (necessário para telemonitorização) e com interesse em participar. Foi fornecido um dispositivo Freescan®, demostrado o seu funcionamento e feito a calibração com um medidor de PA digital com braçadeira (Rossmax X5®). Para o seguimento foi pedido aos participantes que efetuassem 2 medições consecutivas de manhã antes de tomar a medicação para a HTA e 2 medições consecutivas ao fim do dia, preferencialmente antes de jantar. Após 4 semanas marcou-se um encontro para recolher o dispositivo e para responderem a um questionário, de forma a avaliar a opinião do participante sobre diferentes aspetos do Freescan®. Resultados: Obteve-se uma amostra final de 20 participantes. A média das idades foi de 57,15 (desvio padrão (DP) 9,88) anos. A média de dias com pelo menos 1 medição foi de 23,05 (DP 8,60), correspondendo a 82,32% dos 28 dias. A média do número de medições recomendadas realizadas foi de 61,80 (DP 36,05), correspondendo a 55,18% do total de medições recomendadas, 112. Aos questionários, 95% respondeu ser fácil aprender a utilizar o Freescan® (Resposta 4 “Concordo” ou 5 “Concordo totalmente”), 80% ser fácil realizar medições (4 ou 5), 70% confiam nos valores de PA (4 ou 5), 100% que foi fácil telemonitorizar os dados (4 ou 5), 100% acham importante o envio dos dados para o médico (4 ou 5) e 70% prefere o Freescan® aos medidores com braçadeira. Durante o estudo foram ainda sinalizados ao médico responsável 7 casos de HTA não controlada e 3 casos com pelo menos uma medição suspeita de FA. Conclusões: No estudo prospetivo em doentes hipertensos acompanhados em consulta hospitalar de Hipertensão, a maioria dos participantes preferiu o Freescan® aos medidores com braçadeira e houve uma boa adaptação à nova tecnologia, verificando-se uma boa adesão ao número de medições recomendadas realizadas independentemente da idade e do sexo. Assim, a preferência dos participantes por este dispositivo aliado às tecnologias de telemonitorização e deteção de FA leva-nos a concluir que estes novos dispositivos poderão ter um papel importante no seguimento dos doentes com HTA, com potencial para aumentar o controlo da HTA e diminuir a mortalidade e morbilidade associadas a esta doença

Cuff-less Blood Pressure Measurement Using a Smart Phone

Blood pressure is vital sign information that physicians often need as preliminary data for immediate intervention during emergency situations or for regular monitoring of people with cardiovascular diseases. Despite the availability of portable blood pressure meters in the market, they are not regularly carried by people, creating a need for an ultra-portable measurement platform or device that can be easily carried and used at all times. One such device is the smartphone which, according to comScore survey is used by 26.2% of the US adult population. the mass production of these phones with built-in sensors and high computation power has created numerous possibilities for application development in different domains including biomedical. Motivated by this capability and their extensive usage, this thesis focuses on developing a blood pressure measurement platform on smartphones. Specifically, I developed a blood pressure measurement system on a smart phone using the built-in camera and a customized external microphone. the system consists of first obtaining heart beats using the microphone and finger pulse with the camera, and finally calculating the blood pressure using the recorded data. I developed techniques for finding the best location for obtaining the data, making the system usable by all categories of people. the proposed system resulted in accuracies between 90-100%, when compared to traditional blood pressure meters. the second part of this thesis presents a new system for remote heart beat monitoring using the smart phone. with the proposed system, heart beats can be transferred live by patients and monitored by physicians remotely for diagnosis. the proposed blood pressure measurement and remote monitoring systems will be able to facilitate information acquisition and decision making by the 9-1-1 operators

A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation

Extensive research has been performed on continuous, non-invasive, cuffless blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals like ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from Arterial Blood Pressure (ABP) signals as dependent variables, and then using machine learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR), and Heart Rate (HR) features from Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster. The method was implemented using the MIMICII dataset with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multi-trend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster, and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36)

Cuff-less blood pressure estimation with photoplethysmogram and electrocardiogram signals

Blood pressure (BP) measurement is an important indication of health and quality of life. However, conventional means of measurement does not provide continual monitoring of data and most of the devices used are cumbersome. Accordingly, developing a non-invasive and cuff-less method for continual BP measurement is essential. In the development of cuff-less and continual BP measurement, novel methods based on pulse arrival time (PAT) which is obtained from both the photoplethysmogram (PPG) or electrocardiogram (ECG) signals have gained popularity. Although PAT, which is the time delay between the peaks of ECG and PPG signals, is the conventional method generally deployed, still, the results obtained are not consistent and reproducible. Along these lines, the main focus of this research is on the use of physiological signals (which comprises of ECG and PPG signals together with other physiological characteristics) and machine learning methods to predict the diastolic BP (DBP) and systolic BP (SBP) values. Compared to ECG signals, the collection of PPG signals is simpler and convenient, therefore, novel methods that extract features from the PPG signal are more popular. Nevertheless, previous studies have only focused on the features extracted from the PPG signal and did not consider the physiological characteristics of an individual, which can serve as important predictors for BP. To improve the accuracy in the estimation of BP based on the PPG signal, the first empirical study undertaken in this research extracted features from the PPG signal, and also collated some physiological characteristics (height, weight, and age) of the 191 subjects. The accuracy of BP estimation obtained when prior knowledge of the physiological characteristics are incorporated into the model, are superior to those which do not take the physiological characteristics into consideration. In this empirical study, the best performing algorithm is an artificial neural network (ANN), which obtained a mean absolute error (MAE) and standard deviation (STD) of 4.74 ± 5.55 mmHg for DBP and 9.18 ± 12.57 mmHg for SBP compared to 6.61 ± 8.04 mmHg for DBP and 11.12 ± 14.20 mmHg for SBP without prior knowledge of the physiological characteristics. The best results of DBP estimation complied with Grade A of the British Hypertension Society (BHS) standards, and the implementation of physiological characteristics improved the accuracy of BP estimation. Some emerging methods have employed complexity features of ECG signals for assessing vital signals, moreover, few studies have also employed the combined complexity features from both PPG and ECG signals for BP estimation. Therefore, the second empirical study conducted in this research was to investigate the performance of a predictive, machine learning BP monitoring system, using complexity features from both PPG and ECG signals. The most accurate DBP result of 5.15 ± 6.46 mmHg is obtained from the ANN model, and the support vector machine (SVM) generated the most accurate prediction for the SBP, which is estimated as 7.33 ± 9.53 mmHg. The best results for DBP fall within the recommended performance of the BHS, but the SBP is outside the range. This demonstrates that the employment of the combination of PPG and ECG signals improved the accuracy of the BP estimation, compared with previously reported results based on the PPG signal only. Although these findings on the improved accuracy of cuff-less BP estimation show enormous potential, they still cannot meet the official standard. Previous investigations on the adoption of raw signals as input into deep learning for the assessments of vital signals have shown some potential for applications. Few studies have demonstrated the use of raw PPG and ECG signals as input into deep learning models for BP estimation, hence the third empirical study undertaken in this research deployed the use of a novel hybrid model to analyse raw signals for BP prediction. To compare with the methods that were deployed in the aforementioned investigations in this research, traditional machine learning models which utilized morphological and complexity features from PPG and ECG signals respectively, were compared with the novel hybrid deep learning methods. The hybrid model performs best in terms of both DBP and SBP with the results of MAE being 3.23 ± 4.75 mmHg, and 4.43 ± 6.09 mmHg respectively. The estimation accuracy of DBP obtained from this hybrid model is consistent with Grade A of the BHS standard. In addition, all hybrid models achieved lower SBP and DBP errors than traditional machine learning methods. Summarily, the results from the models developed in this research demonstrates practical deep learning models for the prediction of BP which are in agreement with official standards

Cuff-Less Methods for Blood Pressure Telemonitoring.

Blood pressure telemonitoring (BPT) is a telemedicine strategy that uses a patient\u27s self-measured blood pressure (BP) and transmits this information to healthcare providers, typically over the internet. BPT has been shown to improve BP control compared to usual care without remote monitoring. Traditionally, a cuff-based monitor with data communication capabilities has been used for BPT; however, cuff-based measurements are inconvenient and cause discomfort, which has prevented the widespread use of cuff-based monitors for BPT. The development of new technologies which allow for remote BP monitoring without the use of a cuff may aid in more extensive adoption of BPT. This would enhance patient autonomy while providing physicians with a more complete picture of their patient\u27s BP profile, potentially leading to improved BP control and better long-term clinical outcomes. This mini-review article aims to: (1) describe the fundamentals of current techniques in cuff-less BP measurement; (2) present examples of commercially available cuff-less technologies for BPT; (3) outline challenges with current methodologies; and (4) describe potential future directions in cuff-less BPT development

A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations