Personalization of pulse arrival time based blood pressure surrogates through single spot check measurements.

OBJECTIVE: We investigate the effect of selective single parameter personalization on the performance of multi-parameter models for pulse arrival time (PAT) based blood pressure (BP) surrogates. METHODS: Our data set stems from 15 surgery patients, and we selected from each patient 5 segments of 30 min length each. We evaluate the root mean squared BP tracking error of the two models with and without single parameter personalization. We further compare the BP tracking performance to a surrogate-free sample-and-hold approach, e.g., as afforded by conventional non-invasive blood pressure (NIBP) oscillometry. RESULTS: Parameter personalization is key to realizing a tracking performance benefit of PAT-based BP surrogates. The highest tracking error reduction of about 3.7 mmHg with respect to a sample-and-hold approach was reached with a personalized model which is linear in the pulse wave velocity domain. It achieves an estimation error of 7.8 mmHg with respect to a continuously measured invasive reference.Clinical Relevance-We give a performance analysis of PAT-based BP surrogates which are personalized to a patient with a single NIBP spot measurement. We show for surgery patients that patient-specific personalization enables continuous beat-to-beat BP monitoring over 30 min intervals with a average root mean squared error of less than 8 mmHg.

Method for measurement of arterial compliance by fusion of oscillometry and pulse wave velocity.

Up until now estimation of arterial compliance has been performed either by analysis of arterial pressure changes with respect to volume changes or by inference based on pulse wave velocity (PWV). In this study we demonstrate the possibility of an approach to assess arterial compliance by fusing the two information sources namely the pressure/volume relationship obtained from oscillography and PWV data. The goal is to assess arterial properties easily and robustly, enhancing current hemodynamic monitoring. The approach requires as input signals: an electrocardiogram (ECG), a photo- plethysmogram (PPG) and the arterial oscillation as measured during non-invasive blood pressure measurements based on oscillometry with a cuff. These signals are fused by an algorithm using Bayesian principles underpinned by a physiological model. In our simulations, we demonstrate the feasibility to infer arterial compliance by our proposed strategy. A very first measurement on a healthy volunteer supports our findings from the simulation.Clinical Relevance- Arterial compliance/stiffness is recognized as a key hemodynamic parameter, which is not easily accessible and not a standard parameter currently. The presented method and obtained results are encouraging for future research in this area.

Optimized non-uniform sampling of blood pressure time series from the operating room.

OBJECTIVE: We investigate an optimized non-uniform sampling strategy for blood pressure time series from the operating room (OR). Our aim is to obtain an approximate bound on the achievable reconstruction fidelity given an average sampling rate constraint. METHODS: Our data set consists of 117 hours of recordings of continuous invasive blood pressure from 28 surgery patients. We evaluate the root mean squared error (RMSE) of the zero-order hold sampling reconstruction of the blood pressure time series. We quantitatively compare the errors achieved by uniform versus optimized non-uniform sample placements for several average sample rates, ranging from 2 to 24 measurements per hour. RESULTS: An optimized non-uniform measurement schedule can lead to approximately 50% reduction of reconstruction RMSE for systolic, mean, and diastolic blood pressure time series with respect to uniform sampling, while maintaining the same average sampling rate.

Feasibility of in-vivo estimation of the brachial artery area-pressure relation from CINE and real-time MRI during upper arm cuff inflations.

OBJECTIVE: We investigate the basic feasibility of estimating the brachial artery area-pressure relationship from MRI data obtained during pressure cuff inflations in-vivo. METHODS: We acquired cross-sectional real-time MR images and cardiac-gated CINE MR images from the upper arm of a single male subject at rest during supra-systolic pressure cuff inflations and deflations. We estimate from the MR images the lumen area changes of the brachial artery, and, simultaneously, from the cuff pressure the systemic blood pressure of the subject. We reconstruct the area-pressure curve from two real-time and three CINE independent measurements. RESULTS: The area-pressure curve can be reconstructed, and it is plausible and appears largely consistent with the literature using other methods. CONCLUSION: MR imaging during pressure cuff inflations is an easy to use, non-invasive candidate method to estimate the brachial artery pressure-area curve.

Insights into oscillometry: An Experimental Study for Improvement of Cuff-Based Blood Pressure Measurement Technology.

Non-invasive blood pressure (BP) measurements are usually performed by means of an empirical interpretation of arterial oscillations recorded via cuff based oscillometic methods. Extensive effort has been put into development of a theoretical treatment of oscillometry aiming at more accurate BP estimations and measurement of additional hemodynamic parameters. However, oscillometry is still basically a heuristic method for BP inference.This study introduces an experimental setup and discusses experimental results to improve understanding of cuff characteristics and the process by which oscillometric signals are produced, with the aim of improving cuff-based non-invasive BP measurement technology relevant in clinical practice. The work focuses on mechanical simulations of arm volume pulsations in cuff pressure signals. The effects of air compression within the cuff and the influence of viscoelastic properties of exterior cuff material are also investigated. Additionally, arm volume changes and compressibility of arm tissue due to external cuff pressure were studied with an MRI system. Our results reveal novel insights into oscillometry and enable understanding of transducer design for cuffs including the importance of viscoelastic material properties and effects of cuff inflation on arm tissue.

Artifact detection in accelerometer signals acquired from the carotid.

Manual pulse palpation is the common procedure to assess pulse in unconscious patients. This is an error prone procedure during cardiopulmonary resuscitation and therefore automatic pulse detection techniques are being investigated. Accelerometry is an interesting sensing modality for this type of applications. However, accelerometers are highly prone to movement artifacts. Hence, one challenge in designing a solution using accelerometers is to handle motion artifacts properly. In this paper we investigate computationally simple features and classifier to capture movement artifacts in accelerometer signals acquired from the carotid. In particular, based on data obtained from health subjects we show that it is possible to use simple features to achieve an artifact detection sensitivity and specificity higher than 90%.

Systolic blood pressure estimation using PPG and ECG during physical exercise.

In this work, a model to estimate systolic blood pressure (SBP) using photoplethysmography (PPG) and electrocardiography (ECG) is proposed. Data from 19 subjects doing a 40 min exercise was analyzed. Reference SBP was measured at the finger based on the volume-clamp principle. PPG signals were measured at the finger and forehead. After an initialization process for each subject at rest, the model estimated SBP every 30 s for the whole period of exercise. In order to build this model, 18 features were extracted from PPG signals by means of its waveform, first derivative, second derivative, and frequency spectrum. In addition, pulse arrival time (PAT) was derived as a feature from the combination of PPG and ECG. After evaluating four regression models, we chose multiple linear regression (MLR) to combine all derived features to estimate SBP. The contribution of each feature was quantified using its normalized weight in the MLR. To evaluate the performance of the model, we used a leave-one-subject-out cross validation. With the aim of exploring the potential of the model, we investigated the influences of the inclusion of PAT, regression models, measurement sites (finger and forehead), and posture change (lying, sitting, and standing). The results show that the inclusion of PAT reduced the standard deviation (SD) of the difference from 14.07 to 13.52 mmHg. There was no significant difference in the estimation performance between the model using finger- and forehead-derived PPG signals. Separate models are necessary for different postures. The optimized model using finger-derived PPG signals during physical exercise had a performance with a mean difference of 0.43 mmHg, an SD of difference of 13.52 mmHg, and median correlation coefficients of 0.86. Furthermore, we identified two groups of features that contributed more to SBP estimation compared to other features. One group consists of our proposed features depicting beat morphology. The other comprises existing features depicting the dicrotic notch. The present work demonstrates promising results of the SBP estimation model during physical exercise.

Real-Time Prediction of Neurally Mediated Syncope.

Neurally mediated syncope (NMS) patients suffer from sudden loss of consciousness, which is associated with a high rate of falls and hospitalization. NMS negatively impacts a subject's quality of life and is a growing cost issue in our aging society, as its incidence increases with age. In this paper, we present a solution for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) alone. Several parameters extracted from ECG and PPG, associated with reflectory mechanisms underlying NMS in previous publications, were combined in a single algorithm to detect impending syncope. The proposed algorithm was evaluated in a population of 43 subjects. The feature selection, distance metric selection, and optimal threshold were performed in a subset of 30 patients, while the remaining data from 13 patients were used to test the final solution. Additionally, a leave-one-out cross-validation scheme was also used to evaluate the performance of the proposed algorithm yielding the following results: sensitivity (SE)--95.2%; specificity (SP)--95.4%; positive predictive value (PPV)--90.9%; false-positive rate per hour (FPRh)-0.14 h(-1), and prediction time (aPTime)--116.4 s.

Can PPG be used for HRV analysis?

Heart rate variability (HRV) represents one of the most promising markers of the autonomic nervous system (ANS) regulation. However, it requires the acquisition of the ECG signal in order to reliably detect the RR intervals, which is not always easily and comfortably available in personal health applications. Additionally, due to progress in single spot optical sensors, photoplethysmography (PPG) is an interesting alternative for heartbeat interval measurements, since it is a more convenient and a less intrusive measurement technique. Driven by the technological advances in such sensors, wrist-worn devices are becoming a commodity, and the interest in the assessment of HRV indexes from the PPG analysis (pulse rate variability - PRV) is rising. In this study, we investigate the hypothesis of using PRV features as surrogates for HRV indexes, in three different contexts: healthy subjects at rest, healthy subjects after physical exercise and subjects with cardiovascular diseases (CVD). Additionally, we also evaluate which are the characteristic points better suited for PRV analysis in these contexts, i.e. the PPG waveform characteristic points leading to the PRV features that present the best estimates of HRV (correlation and error analysis). The achieved results suggest that the PRV can be often used as an alternative for HRV analysis in healthy subjects, with significant correlations above 82%, for both time and frequency features. Contrarily, in the post-exercise and CVD subjects, time and (most importantly) frequency domain features shall be used with caution (mean correlations ranging from 68% to 88%).

Assessment of cardiovascular function from multi-Gaussian fitting of a finger photoplethysmogram.

Monitoring of cardiovascular function on a beat-to-beat basis is fundamental for protecting patients in different settings including emergency medicine and interventional cardiology, but still faces technical challenges and several limitations. In the present study, we propose a new method for the extraction of cardiovascular performance surrogates from analysis of the photoplethysmographic (PPG) signal alone.We propose using a multi-Gaussian (MG) model consisting of five Gaussian functions to decompose the PPG pulses into its main physiological components. From the analysis of these components, we aim to extract estimators of the left ventricular ejection time, blood pressure and vascular tone changes. Using a multi-derivative analysis of the components related with the systolic ejection, we investigate which are the characteristic points that best define the left ventricular ejection time (LVET). Six LVET estimates were compared with the echocardiographic LVET in a database comprising 68 healthy and cardiovascular diseased volunteers. The best LVET estimate achieved a low absolute error (15.41 ± 13.66 ms), and a high correlation (ρ = 0.78) with the echocardiographic reference.To assess the potential use of the temporal and morphological characteristics of the proposed MG model components as surrogates for blood pressure and vascular tone, six parameters have been investigated: the stiffness index (SI), the T1_d and T1_2 (defined as the time span between the MG model forward and reflected waves), the reflection index (RI), the R1_d and the R1_2 (defined as their amplitude ratio). Their association to reference values of blood pressure and total peripheral resistance was investigated in 43 volunteers exhibiting hemodynamic instability. A good correlation was found between the majority of the extracted and reference parameters, with an exception to R1_2 (amplitude ratio between the main forward wave and the first reflection wave), which correlated low with all the reference parameters. The highest correlation ([Formula: see text] = 0.45) was found between T1_2 and the total peripheral resistance index (TPRI); while in the patients that experienced syncope, the highest agreement ([Formula: see text] = 0.57) was found between SI and systolic blood pressure (SBP) and mean blood pressure (MBP).In conclusion, the presented method for the extraction of surrogates of cardiovascular performance might improve patient monitoring and warrants further investigation.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

A novel multi-parametric algorithm for faint prediction integrating indices of cardiac inotropy and vascular tone.

Neurally medicated syncope (NMS) patients suffer from sudden loss of consciousness, which is associated with a high rate of falls and hospitalization. NMS negatively impacts a subject's quality of life and is a growing cost issue for the healthcare systems in particular since mainly elderly are at risk of NMS in our aging societies. In the present paper we present an algorithm for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Several parameters extracted from ECG and PPG, which have been associated in previous works with reflectory mechanisms underlying NMS, were combined in a single algorithm to detect impending syncope. The proposed algorithm was validated in 43 subjects using a 3-way data split scheme and achieved the following performance: sensitivity (SE) - 100%; specificity (SP) - 92%; positive predictive value (PPV) - 85%; false positive rate per hour (FPRh) - 0.146h(-1) and; average prediction time (aPTime) - 217.58s.

HeartCycle: advanced sensors for telehealth applications.

Current treatment of Cardiovascular Disease (CVD)--the most frequent cause of hospitalization for people over 65--involves changes of diet and lifestyle, requiring in addition physical exercise to support these. Nowadays, patients receive sporadic feedback at doctor visits, or later on, when facing symptoms. The HeartCycle project aimed at providing 1) daily monitoring, 2) close follow up, 3) help on treatment routine and 4) decreasing non-compliance to treatment regimes. The present paper illustrates a new toolbox of advanced sensors developed within the HeartCycle project. Ongoing clinical studies support these developments.

Beat-to-beat systolic time-interval measurement from heart sounds and ECG.

Systolic time intervals are highly correlated to fundamental cardiac functions. Several studies have shown that these measurements have significant diagnostic and prognostic value in heart failure condition and are adequate for long-term patient follow-up and disease management. In this paper, we investigate the feasibility of using heart sound (HS) to accurately measure the opening and closing moments of the aortic heart valve. These moments are crucial to define the main systolic timings of the heart cycle, i.e. pre-ejection period (PEP) and left ventricular ejection time (LVET). We introduce an algorithm for automatic extraction of PEP and LVET using HS and electrocardiogram. PEP is estimated with a Bayesian approach using the signal's instantaneous amplitude and patient-specific time intervals between atrio-ventricular valve closure and aortic valve opening. As for LVET, since the aortic valve closure corresponds to the start of the S2 HS component, we base LVET estimation on the detection of the S2 onset. A comparative assessment of the main systolic time intervals is performed using synchronous signal acquisitions of the current gold standard in cardiac time-interval measurement, i.e. echocardiography, and HS. The algorithms were evaluated on a healthy population, as well as on a group of subjects with different cardiovascular diseases (CVD). In the healthy group, from a set of 942 heartbeats, the proposed algorithm achieved 7.66 ± 5.92 ms absolute PEP estimation error. For LVET, the absolute estimation error was 11.39 ± 8.98 ms. For the CVD population, 404 beats were used, leading to 11.86 ± 8.30 and 17.51 ± 17.21 ms absolute PEP and LVET errors, respectively. The results achieved in this study suggest that HS can be used to accurately estimate LVET and PEP.

Detection of motion artifacts in photoplethysmographic signals based on time and period domain analysis.

The presence of motion artifacts in the photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in real time and continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection, which is based on the analysis of the variations in the time and period domain characteristics of the PPG signal. The extracted features are ranked using a feature selection algorithm (NMIFS) and the best features are used in a Support Vector Machine classification model to distinguish between clean and corrupted sections of the PPG signal. The results achieved by the current algorithm (SE: 0.827 and SP: 0.927) show that both time and especially period domain features play an important role in the discrimination of motion artifacts from clean PPG pulses.

Multi-Gaussian fitting for the assessment of left ventricular ejection time from the photoplethysmogram.

The Left ventricular ejection time (LVET) is one of the primary surrogates of the left ventricular contractility and stroke volume. Its continuous monitoring is considered to be a valuable hypovolumia prognostic parameter and an important risk predictor in cardiovascular diseases such as cardiac and light chain amyloidosis. In this paper, we present a novel methodology for the assessment of LVET based the Photoplethysmographic (PPG) waveform. We propose the use of Gaussian functions to model both systolic and diastolic phases of the PPG beat and consequently determine the onset and offset of the systolic ejection from the analysis of the systolic phase 3(rd) derivative. The results achieved by the proposed methodology were compared with the algorithm proposed by Chan et al. [1], revealing better estimation of LVET (15.84 ± 13.56 ms vs 23.01 ± 14.60 ms), and similar correlation with the echocardiographic reference (0.73 vs 0.75).

Beat-to-beat cardiac output inference using heart sounds.

Cardiac output (CO) change is the primary compensatory mechanism that responds to oxygenation demand. Its continuous monitoring has great potential for the diagnosis and management of cardiovascular diseases, both in hospital as well as in ambulatory settings. However, CO measurements are currently limited to hospital settings only. In this paper, we present an extension of the model proposed by Finkelstein for beat-to-beat CO assessment. We use a nonlinear model consisting of a two-layer feed-forward artificial neural network. In addition to demographic (body surface area and age) and physiological parameters (HR), surrogates of contractility, afterload and mean arterial pressure based on systolic time intervals (STIs), estimated from echocardiography and heart sounds are used as inputs to our models. The results showed that the proposed models--with echocardiography as reference--produce better estimations of stroke volume/CO than the Finkelstein model (12.83 ± 10.66 ml vs 7.23 ± 6.6 ml), as well as higher correlation (0.46 vs 0.82).