Estimating pulse wave velocity using mobile phone sensors

Pulse wave velocity has been recognised as an important physiological phenomenon in the human body, and its measurement can aid in the diagnosis and treatment of chronic diseases. It is the gold standard for arterial stiffness measurements, and it also shares a positive relationship with blood pressure and heart rate. There exist several methods and devices via which it can be measured. However, commercially available devices are more geared towards working health professionals and hospital settings, requiring a significant monetary investment and specialised training to operate correctly. Furthermore, most of these devices are not portable and thus generally not feasible for private home use by the common individual. Given its usefulness as an indicator of certain physiological functions, it is expected that having a more portable, affordable, and simple to use solution would present many benefits to both end users and healthcare professionals alike. This study investigated and developed a working model for a new approach to pulse wave velocity measurement, based on existing methods, but making use of novel equipment. The proposed approach made use of a mobile phone video camera and audio input in conjunction with a Doppler ultrasound probe. The underlying principle is that of a two-point measurement system utilising photoplethysmography and electrocardiogram signals, an existing method commonly found in many studies. Data was collected using the mobile phone sensors and processed and analysed on a computer. A custom program was developed in MATLAB that computed pulse wave velocity given the audio and video signals and a measurement of the distance between the two data acquisition sites. Results were compared to the findings of previous studies in the field, and showed similar trends. As the power of mobile smartphones grows, there exists potential for the work and methods presented here to be fully developed into a standalone mobile application, which would bring forth real benefits of portability and cost-effectiveness to the prospective user base

Cuffless bood pressure estimation

L'hypertension est une maladie qui affecte plus d'un milliard de personnes dans le monde. Il s'agit d'une des principales causes de décès; le suivi et la gestion de cette maladie sont donc cruciaux. La technologie de mesure de la pression artérielle la plus répandue, utilisant le brassard pressurisé, ne permet cependant pas un suivi en continu de la pression, ce qui limite l'étendue de son utilisation. Ces obstacles pourraient être surmontés par la mesure indirecte de la pression par l'entremise de l'électrocardiographie ou de la photopléthysmographie, qui se prêtent à la création d'appareils portables, confortables et peu coûteux. Ce travail de recherche, réalisé en collaboration avec le département d'ingénierie biomédicale de l'université de Lund, en Suède, porte principalement sur la base de données publique Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Datasetde PhysioNet, largement utilisée dans la littérature portant sur le développement et la validation d'algorithmes d'estimation de la pression artérielle sans brassard pressurisé. Puisque ces données proviennent d'unités de soins intensifs et ont été recueillies dans des conditions non contrôlées, plusieurs chercheurs ont avancé que les modèles d'estimation de la pression artérielle se basant sur ces données ne sont pas valides pour la population générale. Pour la première fois dans la littérature, cette hypothèse est ici mise à l'épreuve en comparant les données de MIMIC à un ensemble de données de référence plus représentatif de la population générale et recueilli selon une procédure expérimentale bien définie. Des tests statistiques révèlent une différence significative entre les ensembles de données, ainsi qu'une réponse différente aux changements de pression artérielle, et ce, pour la majorité des caractéristiques extraites du photopléthysmogramme. De plus, les répercussions de ces différences sont démontrées à l'aide d'un test pratique d'estimation de la pression artérielle par apprentissage machine. En effet, un modèle entraîné sur l'un des ensembles de données perd en grande partie sa capacité prédictive lorsque validé sur l'autre ensemble, par rapport à sa performance en validation croisée sur l'ensemble d'entraînement. Ces résultats constituent les contributions principales de ce travail et ont été soumis sous forme d'article à la revue Physiological Measurement. Un volet additionnel de la recherche portant sur l'analyse du pouls par décomposition (pulse de composition analysis ou PDA) est présenté dans un deuxième temps. La PDA est une technique permettant de séparer l'onde du pouls en une composante excitative et ses réflexions, utilisée pour extraire des caractéristiques du signal dans le contexte de l'estimation de la pression artérielle. Les résultats obtenus démontrent que l'estimation de la position temporelle des réflexions à partir de points de référence de la dérivée seconde du signal donne d'aussi bons résultats que leur détermination par la méthode traditionnelle d'approximation successive, tout en étant beaucoup plus rapide. Une méthode récursive rapide de PDA est également étudiée, mais démontrée comme inadéquate dans un contexte de comparaison intersujet.Hypertension affects more than one billion people worldwide. As one of the leading causes of death, tracking and management of the condition is critical, but is impeded by the current cuff-based blood pressure monitoring technology. Continuous and more ubiquitous blood pressure monitoring may be achieved through simpler, cheaper and less invasive cuff-less devices, performing an indirect measure through electrocardiography or photoplethysmography. Produced in collaboration with the department of biomedical engineering of Lund Universityin Sweden, this work focuses on public data that has been widely used in the literature to develop and validate cuffless blood pressure estimation algorithms: The Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Dataset from PhysioNet. Because it is sourced from intensive care units and collected in absence of controlled conditions, it has many times been hypothesized that blood pressure estimation models based on its data may not generalize to the normal population. This work tests that hypothesis for the first time by comparing the MIMIC dataset to another reference dataset more representative of the general population and obtained under controlled experimental conditions. Through statistical testing, a majority of photoplethysmogram based features extracted from MIMIC are shown to differ significantly from the reference dataset and to respond differently to blood pressure changes. In addition, the practical impact of those differences is tested through the training and cross validating of machine learning models on both datasets, demonstrating an acute loss of predictive powers of models facing data from outside the dataset used in the training phase. As the main contribution of this work, these findings have been submitted as a journal paper to Physiological Measurement. Additional original research is also presented in relation to pulse decomposition analysis (PDA), a technique used to separate the pulse wave from its reflections, in the context of blood pressure estimation. The results obtained through this work show that when using the timing of reflections as part of blood pressure predictors, estimating those timings from fiducial points in the second derivative works as well as using the traditional and computationally costly successive approximation PDA method, while being many times faster. An alternative fast recursive PDA algorithm is also presented and shown to perform inadequately in an inter-subject comparison context

Automatic noninvasive measurement of systolic blood pressure using photoplethysmography

<p>Abstract</p> <p>Background</p> <p>Automatic measurement of arterial blood pressure is important, but the available commercial automatic blood pressure meters, mostly based on oscillometry, are of low accuracy.</p> <p>Methods</p> <p>In this study, we present a cuff-based technique for automatic measurement of systolic blood pressure, based on photoplethysmographic signals measured simultaneously in fingers of both hands. After inflating the pressure cuff to a level above systolic blood pressure in a relatively slow rate, it is slowly deflated. The cuff pressure for which the photoplethysmographic signal reappeared during the deflation of the pressure-cuff was taken as the systolic blood pressure. The algorithm for the detection of the photoplethysmographic signal involves: (1) determination of the time-segments in which the photoplethysmographic signal distal to the cuff is expected to appear, utilizing the photoplethysmographic signal in the free hand, and (2) discrimination between random fluctuations and photoplethysmographic pattern. The detected pulses in the time-segments were identified as photoplethysmographic pulses if they met two criteria, based on the pulse waveform and on the correlation between the signal in each segment and the signal in the two neighboring segments.</p> <p>Results</p> <p>Comparison of the photoplethysmographic-based automatic technique to sphygmomanometry, the reference standard, shows that the standard deviation of their differences was 3.7 mmHg. For subjects with systolic blood pressure above 130 mmHg the standard deviation was even lower, 2.9 mmHg. These values are much lower than the 8 mmHg value imposed by AAMI standard for automatic blood pressure meters.</p> <p>Conclusion</p> <p>The photoplethysmographic-based technique for automatic measurement of systolic blood pressure, and the algorithm which was presented in this study, seems to be accurate.</p

Cuffless Blood Pressure Estimation

The blood pressure is an important factor in the diagnosis and evaluation of several diseases, such as acute myocardial infarction and stroke. This way, continuous monitorization of this parameter is crucial to a correct health evaluation. The current methods, like the oscillometric method, have some major drawbacks, that can influence the output values or even make the measurements impossible. One example is the high frequency evaluation of the blood pressure, in the standard used methods the process of measuring can take up to 3 minutes, and a waiting time is necessary between consecutive measurements. This dissertation presents two different cuffless solution to solve those problems. One based on physical models of the human body, and the other using machine learning techniques. In the first solution seven models that correlate pulse transit time and blood pressure, deducted by different authors, were tested to evaluate which one performed better. The testes were performed in a custom dataset acquired at Fraunhofer AICOS and in clinical environment, with two different devices (low cost device and medical grade device). The results indicate that pulse transit time can be used to track blood pressure, the developed device/method was evaluated as grade A based in the Standard IEEE 1708-2014. The second solution it’s a proof of concept using a public database and three different machine learning methods (Random Forest, Neural Network and AdaBoost). Two sets of features are calculated from the ECG and PPG signals, one using TSFEL (spectral, frequency and time domain features) and a total of 15 custom features. The proposed method outperforms the methods presented in bibliography with mean absolute error of 3.6 mmHg and 2.0 mmHg to systolic and diastolic blood pressure respectively

Non-invasive vascular assessment using photoplethysmography

Photoplethysmography (PPG) has become widely accepted as a valuable clinical tool for performing non-invasive biomedical monitoring. The dominant clinical application of PPG has been pulse oximetry, which uses spectral analysis of the peripheral blood supply to establish haemoglobin saturation. PPG has also found success in screening for venous dysfunction, though to a limited degree. Arterial Disease (AD) is a condition where blood flow in the arteries of the body is reduced,a condition known as ischaernia. Ischaernia can result in pain in the affected areas, such as chest pain for an ischearnic heart, but does not always produce symptoms. The most common form of AD is arteriosclerosis, which affects around 5% of the population over 50 years old. Arteriosclerosis, more commonly known as 'hardening of the arteries' is a condition that results in a gradual thickening, hardening and loss of elasticity in the walls of the arteries, reducing overall blood flow. This thesis investigates the possibility of employing PPG to perform vascular assessment, specifically arterial assessment, in two ways. PPG based perfusion monitoring may allow identification of ischaernia in the periphery. To further investigate this premise, prospective experimental trials are performed, firstly to assess the viability of PPG based perfusion monitoring and culminating in the development of a more objective method for determining ABPI using PPG based vascular assessment. A complex interaction between the heart and the connective vasculature, detected at the measuring site, generates the PPG signal. The haemodynamic properties of the vasculature will affect the shape of the PPG waveform, characterising the PPG signal with the properties of the intermediary vasculature. This thesis investigates the feasibility of deriving quantitative vascular parameters from the PPG signal. A quantitative approach allows direct identification of pathology, simplifying vascular assessment. Both forward and inverse models are developed in order to investigate this topic. Application of the models in prospective experimental trials with both normal subjects and subjects suffering PVD have shown encouraging results. It is concluded that the PPG signal contains information on the connective vasculature of the subject. PPG may be used to perform vascular assessment using either perfusion based techniques, where the magnitude of the PPG signal is of interest, or by directly assessing the connective vasculature using PPG, where the shape of the PPG signal is of interest. it is argued that PPG perfusion based techniques for performing the ABPI diagnosis protocol can offer greater sensitivity to the onset of PAD, compared to more conventional methods. It is speculated that the PPG based ABPI diagnosis protocol could provide enhanced PAD diagnosis, detecting the onset of the disease and allowing a treatmenpt lan to be formed soonert han was possible previously. The determination of quantitative vascular parameters using PPG shape could allow direct vascular diagnosis, reducing subjectivity due to interpretation. The prospective trials investigating PPG shape analysis concentrated on PVD diagnosis, but it is speculated that quantitative PPG shaped based vascular assessment could be a powerful tool in the diagnosis of many vascular based pathological conditions

Mobile health applications digital evidence taxonomy with knowledge sharing approach for digital forensics readiness

M-health is the current application that capable to monitor and detect human biological change and used the Internet as a platform to transfer and receive the data from the cloud providers. However, the advancement of Internet of Things (IoT) technology poses a great challenge for digital forensic experts in order to preserve, acquire and analyse digital evidence. Digital evidence taxonomy is one technique in digital forensics that facilitates digital forensics readiness and integration with knowledge sharing approach is necessary to allow digital forensics experts to share their knowledge. Therefore, this research was carried out that consists three phases, namely (1) initial phase, (2) intermediate phase and (3) final phase. In the initial phase, a systematic literature review was conducted to identify any potential gaps from the existing studies. Subsequently, digital evidence taxonomy in the IoT forensics layers was adopted, which consisted of three artefact categories to represent the IoT forensics layers. In the intermediate phase, 34 top rating m-health apps were used as a case study to validate the digital evidence taxonomy. From the analysis of the result, various types of information for forensic investigation were acquired, such as type of outdoor activity, activity timestamp, client IP address and date accessed. In the final phase, the M-Health Digital Evidence Taxonomy System (MDETS) was developed as a proof of concept to demonstrate the integration of digital evidence taxonomy with the knowledge-sharing approach to facilitate digital forensic readiness. Interviews were used as the instrument tool to evaluate knowledge sharing in terms of people, process and technology elements in enabling digital forensic readiness. The results from the interviews support that knowledge sharing facilitates digital forensic readiness in terms of people, process and technology elements. As a conclusion, the integration of digital evidence taxonomy with the knowledge-sharing approach gives the opportunity for the digital forensic community to enhance the existing approach or procedure to increase the findings of a digital forensic investigation and make digital forensic readiness more proactive within the organisation

ARTERIAL WAVEFORM MEASUREMENT USING A PIEZOELECTRIC SENSOR

This study aims to develop a new method to monitor peripheral arterial pulse using a PVDF piezoelectric sensor. After comparing different locations of sensor placement, a specific sensor wrap for the finger was developed. Its composition, size, and location make it inexpensive and very convenient to use. In order to monitor the effectiveness of the sensor at producing a reliable pulse waveform, a monitoring system, including the PZT sensor, ECG, pulse-oximeter, respiratory sensor, and accelerometer was setup. Signal analysis from the system helped discover that the PZT waveform is relative to the 1st derivative of the artery pressure wave. Also, the system helped discover that the first, second, and third peaks in PZT waveform represent the pulse peak, inflection point, and dicrotic notch respectively. The relationship between PZT wave and respiration was also analyzed, and, consequently, an algorithm to derive respiratory rate directly from the PZT waveform was developed. This algorithm gave a 96% estimating accuracy. Another feature of the sensor is that by analyzing the relationship between pulse peak amplitude and blood pressure change, temporal artery blood pressure can be predicted during Valsalva maneuver. PZT pulse wave monitoring offers a new type of pulse waveform which is not yet fully understood. Future studies will lead to a more broadly applied use of PZT sensors in cardiac monitoring applications

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been driven predominantly by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has brought up significant concerns, regarding the accuracy of reported BP values across settings. In this survey, focusing mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices which use artificial-intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and to provide individualized BP-related cardiovascular risk indexes