Method-comparison study between a watch-like sensor and a cuff-based device for 24-h ambulatory blood pressure monitoring.

The use of 24-h ambulatory blood pressure monitoring (ABPM) has been continuously increasing over the last decades. However, cuff-based devices may cause discomfort, particularly at night, leading to potentially non-representative blood pressure (BP) values. We investigated the feasibility of a cuff-less BP monitoring solution in 67 subjects undergoing conventional 24-h ABPM. A watch-like optical sensor was attached at the upper arm or wrist at the contralateral side of the cuff. Systolic (SBP) and diastolic BP (DBP) values were estimated from the measured optical signals by pulse wave analysis. Average 24-h, daytime and nighttime BP values were compared between the conventional monitor and the cuff-less sensor. The differences between both methods-expressed as mean ± standard deviation (95% limits of agreement)-were of - 1.8 ± 6.2 mmHg (- 13.9, 10.3) on SBP and - 2.3 ± 5.4 mmHg (- 13.0, 8.3) on DBP for 24-h averages, of - 1.5 ± 6.6 mmHg (- 14.4, 11.4) on SBP and - 1.8 ± 5.9 mmHg (- 13.4, 9.9) on DBP for daytime averages, and of 0.4 ± 7.5 mmHg (- 14.4, 15.1) on SBP and - 1.3 ± 6.8 mmHg (- 14.7, 12.0) on DBP for nighttime averages. These results encouragingly suggest that cuff-less 24-h ABPM may soon become a clinical possibility.

Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning.

Blood pressure (BP) is a crucial biomarker giving valuable information regarding cardiovascular diseases but requires accurate continuous monitoring to maximize its value. In the effort of developing non-invasive, non-occlusive and continuous BP monitoring devices, photoplethysmography (PPG) has recently gained interest. Researchers have attempted to estimate BP based on the analysis of PPG waveform morphology, with promising results, yet often validated on a small number of subjects with moderate BP variations. This work presents an accurate BP estimator based on PPG morphology features. The method first uses a clinically-validated algorithm (oBPM®) to perform signal preprocessing and extraction of physiological features. A subset of features that best reflects BP changes is automatically identified by Lasso regression, and a feature relevance analysis is conducted. Three machine learning (ML) methods are then investigated to translate this subset of features into systolic BP (SBP) and diastolic BP (DBP) estimates; namely Lasso regression, support vector regression and Gaussian process regression. The accuracy of absolute BP estimates and trending ability are evaluated. Such an approach considerably improves the performance for SBP estimation over previous oBPM® technology, with a reduction in the standard deviation of the error of over 20%. Furthermore, rapid BP changes assessed by the PPG-based approach demonstrates concordance rate over 99% with the invasive reference. Altogether, the results confirm that PPG morphology features can be combined with ML methods to accurately track BP variations generated during anesthesia induction. They also reinforce the importance of adding a calibration measure to obtain an absolute BP estimate.

Photoplethysmography-Based Blood Pressure Monitoring Could Improve Patient Outcome during Anesthesia Induction.

During anesthesia, noncritical patients are routinely monitored via noninvasive cuff-based blood pressure (BP) monitors. Due to the noncontinuous nature of the monitoring, the BP values of the patient remain unavailable between consecutive cuff measurements, carrying the risk of missing rapid and sudden variations in BP. We evaluated the added value of using a photoplethysmography (PPG)-based continuous BP measurement device in addition to the standard cuff-based monitoring in a cohort of 40 patients in comparison with the current approach, in which only intermittent cuff-based measurements are available. When using a three-minute cuff measurement interval, using the PPG-based BP measurement in addition to the cuff-based monitor reduced the error (mean ± SD) of systolic (SBP) and mean (MBP) BP from 2.6 ± 19.6 mmHg and 1.2 ± 13.2 mmHg to 0.5 ± 11.2 mmHg and 0.0 ± 8.1 mmHg, respectively. Error grid analysis was also used to assess the improvement in patient safety. The additional use of the PPG-based BP measurement reduced the amount of data falling into higher risk categories. For SBP, points falling in the significant-, moderate-, and low-risk categories decreased from 1.1%, 8.7%, and 19.3% to 0.0%, 2.3%, and 9.6%, respectively. Similar results were obtained for MBP. These results suggest that using a PPG-based BP monitor-in addition to the standard cuff-based monitor-can improve patient safety during anesthesia induction, with no additional sensor needed.

Comparability of a Blood-Pressure-Monitoring Smartphone Application with Conventional Measurements-A Pilot Study.

(1) Background: New cuffless technologies attempting blood-pressure measurements (BPM) offer possibilities to improve hypertension awareness and control. The aim of this study was to compare a smartphone application (app)-based algorithm with office BPM (OBPM). (2) Methods: We included consecutive patients with an indication for ambulatory BPM. The smartphone app (RIVA digital) acquired the pulse wave in the fingers' arterial bed using the phone's camera and estimated BP based on photoplethysmographic (PPG) waveforms. Measurements were alternatingly taken with an oscillometric cuff-based device and smartphone BPM (AppBP) on two consecutive days. AppBP were calibrated to the first OBPM. Each AppBP was compared to its CuffBP (mean of the previous/following OBPM). (3) Results: 50 participants were included, resulting in 50 AppBP values on Day 1 and 33 on Day 2 after exclusion of 225 AppBP due to insufficient quality. The mean ± SD of the differences between AppBP and CuffBP was 0.7 ± 9.4/1.0 ± 4.5 mmHg (p-value 0.739/0.201) on Day 1 and 2.6 ± 8.2/1.3 ± 4.1 mmHg (p-value 0.106/0.091) on Day 2 for systolic/diastolic values, respectively. There were no significant differences between the deviations on Day 1 and Day 2 (p-value 0.297/0.533 for systolic/diastolic values). Overall, there were 10 (12%) systolic measurement pairs differing by >15 mmHg. (4) Conclusions: In this pilot evaluation, the RIVA Digital app shows promising results when compared to oscillometric cuff-based measurements, especially regarding diastolic values. Its differences between AppBP−CuffBP have a good stability one day after calibration. Before clinical use, signal acquisition needs improvement and the algorithm needs to undergo formal validation against a gold-standard BPM method.

Influence of Measurement Location on Reflectance Pulse Oximetry in Sleep Apnea Patients: Wrist vs. Upper Arm.

Peripheral oxygen saturation (SpO2) plays a key role in diagnosing sleep apnea. It is mainly measured via transmission pulse oximetry at the fingertip, an approach less suited for long-term monitoring over several nights.In this study we tested a more patient-friendly solution via a reflectance pulse oximetry device. Having previously observed issues with pulse oximetry at the wrist, we investigated in this study the influence of the location of our device (upper arm vs. wrist) to measure SpO2. Accuracy was compared against state-of-the-art fingertip SpO2 measurements during a full overnight polysomnography in nine patients with suspected sleep apnea.The upper arm location clearly showed a lower root mean square error ARMS = 1.8% than the wrist ARMS = 2.5% and a lower rate of automatic data rejection (19% vs 25%). Irrespective of the measurement location the accuracies obtained comply with the ISO standard and the FDA guidance for pulse oximeters. In contrast to the wrist, the upper arm location seemed to be more resilient to deteriorating influences such as venous blood.Reflectance pulse oximetry at the wrist remains challenging but the upper arm could provide remedy for more robust SpO2 estimates to reliably screen for sleep apnea and other diseases.Clinical Relevance- The performance of reflectance pulse oximetry measured at the upper arm during sleep is superior to measurements at the wrist which are perturbed by undesired large fluctuations suspected to be caused by venous blood. If confirmed, this could also apply to the optical measurement of other vital signs such as blood pressure.

Feature Learning for Blood Pressure Estimation from Photoplethysmography.

Blood pressure (BP) is an important indicator for prevention and management of cardiovascular diseases. Alongside the improvement in sensors and wearables, photoplethysmography (PPG) appears to be a promising technology for continuous, non-invasive and cuffless BP monitoring. Previous attempts mainly focused on features extracted from the pulse morphology. In this paper, we propose to remove the feature engineering step and automatically generate features from an ensemble average (EA) PPG pulse and its derivatives, using convolutional neural network and a calibration measurement. We used the large VitalDB dataset to accurately evaluate the generalization capability of the proposed model. The model achieved mean errors of -0.24 ± 11.56 mmHg for SBP and -0.5 ± 6.52 mmHg for DBP. We observed a considerable reduction in error standard deviation of above 40% compared to the control case, which assumes no BP variation. Altogether, these results highlight the capability to model the dependency between PPG and BP.

Smartphone based blood pressure measurement: accuracy of the OptiBP mobile application according to the AAMI/ESH/ISO universal validation protocol.

OBJECTIVE: The aim of this study was to assess the accuracy of the OptiBP mobile application based on an optical signal recorded by placing the patient's fingertip on a smartphone's camera to estimate blood pressure (BP). Measurements were carried out in a general population according to existing standards of the Association for the Advancement of Medical Instrumentation (AAMI), the European Society of Hypertension (ESH) and the International Organization for Standardization (ISO). METHODS: Participants were recruited during a scheduled appointment at the hypertension clinic of Lausanne University Hospital in Switzerland. Age, gender and BP distribution were collected to fulfill AAMI/ESH/ISO universal standards. Both auscultatory BP references and OptiBP were measured and compared using the opposite arm simultaneous method as described in the 81060-2:2018 ISO norm. RESULTS: A total of 353 paired recordings from 91 subjects were analyzed. For validation criterion 1, the mean ± SD between OptiBP and reference BP recordings was respectively 0.5 ± 7.7 mmHg and 0.4 ± 4.6 mmHg for SBP and DBP. For validation criterion 2, the SD of the averaged BP differences between OptiBP and reference BP per subject was 6.3 mmHg and 3.5 mmHg for SBP and DBP. OptiBP acceptance rate was 85%. CONCLUSION: The smartphone embedded OptiBP cuffless mobile application fulfills the validation requirements of AAMI/ESH/ISO universal standards in a general population for the measurement of SBP and DBP.

Non-invasive pulmonary artery pressure estimation by electrical impedance tomography in a controlled hypoxemia study in healthy subjects.

Pulmonary hypertension is a hemodynamic disorder defined by an abnormal elevation of pulmonary artery pressure (PAP). Current options for measuring PAP are limited in clinical practice. The aim of this study was to evaluate if electrical impedance tomography (EIT), a radiation-free and non-invasive monitoring technique, can be used for the continuous, unsupervised and safe monitoring of PAP. In 30 healthy volunteers we induced gradual increases in systolic PAP (SPAP) by exposure to normobaric hypoxemia. At various stages of the protocol, the SPAP of the subjects was estimated by transthoracic echocardiography. In parallel, in the pulmonary vasculature, pulse wave velocity was estimated by EIT and calibrated to pressure units. Within-cohort agreement between both methods on SPAP estimation was assessed through Bland-Altman analysis and at subject level, with Pearson's correlation coefficient. There was good agreement between the two methods (inter-method difference not significant (P > 0.05), bias ± standard deviation of - 0.1 ± 4.5 mmHg) independently of the degree of PAP, from baseline oxygen saturation levels to profound hypoxemia. At subject level, the median per-subject agreement was 0.7 ± 3.8 mmHg and Pearson's correlation coefficient 0.87 (P < 0.05). Our results demonstrate the feasibility of accurately assessing changes in SPAP by EIT in healthy volunteers. If confirmed in a patient population, the non-invasive and unsupervised day-to-day monitoring of SPAP could facilitate the clinical management of patients with pulmonary hypertension.

Machine Learning Approaches For Improved Continuous, Non-occlusive Arterial Pressure Monitoring Using Photoplethysmography.

Arterial pressure (AP) is a crucial biomarker for cardiovascular disease prevention and management. Photoplethysmography (PPG) could provide a novel, paradigm-shifting approach for continuous, non-obtrusive AP monitoring, comfortably integrated in wearable and mobile devices; yet, it still faces challenges in accuracy and robustness. In this work, we sought to integrate machine learning (ML) techniques into a previously established, clinically-validated classical approach (oBPM®) to develop new accurate AP estimation tools based on PPG, and at the same time improve our understanding of the underlying physiological parameters. In this novel approach, oBPM® was used to pre-process PPG signals and robustly extract physiological features, and ML models were trained on these features to estimate systolic AP (SAP). A feature relevance analysis showed that reference (calibration) information, followed by various morphological parameters of the PPG pulse wave, comprised the most important features for SAP estimation. A performance analysis then revealed that LASSO-regularized linear regression, Gaussian process regression and support vector regression are effective for SAP estimation, particularly when operating on reduced feature sets previously obtained with e.g. LASSO. These approaches yielded substantial reductions in error standard deviation of 9-15% relative to conventional oBPM®. Altogether, these results indicate that ML approaches are well-suited, and promising tools to help overcoming the challenges of ubiquitous AP monitoring.

Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements.

Mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management. By maximizing the smartphones' optics and computational power, they could allow assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP). We trained the parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anaesthesia on pulse oximeter signals, by collecting optical signals from 51 patients fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter. We then compared smartphone-based measurements obtained on 50 participants in an ambulatory setting via the OptiBP app against simultaneously acquired auscultatory systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean blood pressure (MBP) measurements. Patients were normotensive (70.0% for SBP versus 61.4% for DBP), hypertensive (17.1% vs. 13.6%) or hypotensive (12.9% vs. 25.0%). The difference in BP (mean ± standard deviation) between both methods were within the ISO 81,060-2:2018 standard for SBP (- 0.7 ± 7.7 mmHg), DBP (- 0.4 ± 4.5 mmHg) and MBP (- 0.6 ± 5.2 mmHg). These results demonstrate that BP can be measured with accuracy at the finger using the OptiBP smartphone app. This may become an important tool to detect hypertension in various settings, for example in low-income countries, where the availability of smartphones is high but access to health care is low.

Pulse Oximetry at the Wrist During Sleep: Performance, Challenges and Perspectives.

The state-of-the-art non-invasive measurement of peripheral oxygen saturation (SpO2) during sleep is mainly based on pulse oximetry at the fingertip. Although this approach is noninvasive, it can still be obtrusive and cumbersome to apply, in particular for ambulatory monitoring over several nights.We developed a wrist-worn reflectance pulse oximetry device which can be embedded in a watch, making it less obtrusive and easy to apply. This device was tested in an ongoing clinical study on 57 subjects (33 patients and 24 healthy volunteers) undergoing a full overnight polysomnography recording. The accuracy was evaluated against state-of-the-art fingertip SpO2 measurements.In the 54 subjects available for analysis we obtained an SpO2 accuracy (ARMS) of 3.4 % when automatically rejecting 17.7 % of signals due to low quality. When further excluding measurements suffering from insufficient contact of the watch with the skin an ARMS of 2.7 % was obtained while rejecting a total of 23.2 % measurements. These accuracies comply with the ISO standard and the FDA guidance for pulse oximeters.The present results are promising and pave the way for unobtrusive and continuous monitoring of SpO2 to screen for sleep disordered breathing. Nonetheless, contact pressure and venous blood have shown to adversely affect the SpO2 estimation and remain a challenge for wrist-based reflectance pulse oximetry.

Automated Pulse Oximeter Waveform Analysis to Track Changes in Blood Pressure During Anesthesia Induction: A Proof-of-Concept Study.

BACKGROUND: Intraoperative hypotension is associated with postoperative complications and death. Oscillometric brachial cuffs are used to measure arterial pressure (AP) in most surgical patients but may miss acute changes in AP. We hypothesized that pulse oximeter waveform analysis may help to detect changes in systolic AP (SAP) and mean AP (MAP) during anesthesia induction. METHODS: In 40 patients scheduled for an elective surgery necessitating general anesthesia and invasive AP monitoring, we assessed the performance of a pulse oximeter waveform analysis algorithm (optical blood pressure monitoring [oBPM]) to estimate SAP, MAP, and their changes during the induction of general anesthesia. Acute AP changes (>20%) in SAP and MAP assessed by the reference invasive method and by oBPM were compared using 4-quadrant and polar plots. The tracking ability of the algorithm was evaluated on changes occurring over increasingly larger time spans, from 30 seconds up to 5 minutes. The second objective of the study was to assess the ability of the oBPM algorithm to cope with the Association for the Advancement of Medical Instrumentation (AAMI) standards. The accuracy and precision of oBPM in estimating absolute SAP and MAP values compared to the invasive method was evaluated at various instants after algorithm calibration, from 30 seconds to 5 minutes. RESULTS: Rapid changes (occurring over time spans of ≤60 seconds) in SAP and MAP assessed by oBPM were strongly correlated and showed excellent concordance with changes in invasive AP (worst-case Pearson correlation of 0.94 [0.88, 0.97] [95% confidence interval], concordance rate of 100% [100%, 100%], and angular concordance rate at ±30° of 100% [100%, 100%]). The trending ability tended to decrease progressively as the time span over which the changes occurred increased, reaching 0.89 (0.85, 0.91) (Pearson correlation), 97% (95%, 100%) (concordance rate), and 90% (85%, 94%) (angular concordance rate) in the worst case. Regarding accuracy and precision, oBPM-derived SAP values were shown to comply with AAMI criteria up to 2 minutes after calibration, whereas oBPM-derived MAP values were shown to comply with criteria at all times. CONCLUSIONS: Pulse oximeter waveform analysis was useful to track rapid changes in SAP and MAP during anesthesia induction. A good agreement with reference invasive measurements was observed for MAP up to at least 5 minutes after initial calibration. In the future, this method could be used to track changes in AP between intermittent oscillometric measurements and to automatically trigger brachial cuff inflation when a significant change in AP is detected.

Noninvasive measurement of stroke volume changes in critically ill patients by means of electrical impedance tomography.

Previous animal experiments have suggested that electrical impedance tomography (EIT) has the ability to noninvasively track changes in cardiac stroke volume (SV). The present study intended to reproduce these findings in patients during a fluid challenge. In a prospective observational study including critically ill patients on mechanical ventilation, SV was estimated via ECG-gated EIT before and after a fluid challenge and compared to transpulmonary thermodilution reference measurements. Relative changes in EIT-derived cardiosynchronous impedance changes in the heart ([Formula: see text]) and lung region ([Formula: see text]) were compared to changes in reference SV by assessing the concordance rate (CR) and Pearson's correlation coefficient (R). We compared 39 measurements of 20 patients. [Formula: see text] did not show to be a reliable estimate for tracking changes of SV (CR = 52.6% and R = 0.13 with P = 0.44). In contrast, [Formula: see text] showed an acceptable trending performance (CR = 94.4% and R = 0.72 with P < 0.0001). Our results indicate that ECG-gated EIT measurements of [Formula: see text] are able to noninvasively monitor changes in SV during a fluid challenge in critically ill patients. However, this was not possible using [Formula: see text]. The present approach is limited by the influences induced by ventilation, posture or changes in electrode-skin contact and requires further validation.

PPG-Based Blood Pressure Monitoring by Pulse Wave Analysis: Calibration Parameters are Stable for Three Months.

The current non-invasive gold standard for the measurement of blood pressure (BP) is the oscillometric cuff at the upper arm, despite its known limitations. In particular, its poor adequacy with continuous monitoring and its measurement incommodity call for the development of simpler and more convenient solutions. Among these, solutions based on pulse wave analysis (PWA) and photoplethysmography (PPG) are of particular interest, due to their low-cost, strong patient compliance, and applicability in and out of clinical settings. In that context, we have recently disclosed a PPG-based PWA algorithm (oBPM™) dedicated to the continuous monitoring of BP in patients undergoing induction of general anesthesia. As is standard with PPG-based BP monitoring techniques, an initial calibration procedure with a reference device is required to allow the estimation of absolute values of BP (in mmHg). However, due to their sensitivity to peripheral effects such as vasomotion, the applicability of PPG-based techniques is often limited by the constant need of re-calibration procedures, sometimes in matters of minutes. In the present study, we evaluated the long-term stability of the calibration for our algorithm by performing PPG measurements at irregular time intervals over a period of 3 months in 13 healthy volunteers. For each measurement, diastolic BP (DBP) was assessed by an oscillometric device and estimated by the oBPM™ algorithm. We found the calibration to remain stable over the entire 3-month period, with estimation errors remaining stable over time and complying with the ISO 81060-2:2018 standard. In addition, we verified - in 11 of our 13 subjects - the sensitivity of the oBPM™ algorithm to changes in DBP. This was done in a protocol involving static leg extension exercises. Excellent trending ability (average per-subject concordance rate of 97.7 ± 5.2 %, and correlation coefficient of 0.98 ± 0.02, p <; 0.001) was found between cuff-derived DBP changes and our estimates. These findings provide a strong added value to the practical usability of the proposed PPG-based PWA approach to BP monitoring, particularly for the clinical management of hypertensive patients in and out of clinics, for whom a simple and comfortable continuous alternative to the oscillometric cuff would be strongly preferred.

Performance Assessment of a Dedicated Reflectance Pulse Oximeter in a Neonatal Intensive Care Unit.

The measurement of peripheral oxygen saturation (SpO2) in neonatal intensive care units (NICUs) poses a significant challenge. Motion artifacts due to the patient's limb motion induce many false alarms, which in turn cause an additional workload for the medical staff and anxiety for the parents. We developed a reflectance pulse oximeter dedicated to be placed at the patient's forehead, which is less prone to such artifacts. We trained our algorithms for SpO2 estimation on 8 adult healthy volunteers participating in a controlled desaturation study. We then validated our SpO2 monitoring system on 25 newborn patients monitored in an NICU. We further evaluated the versatility and resilience to low signal-tonoise ratios (SNR) of our solution by testing it on signals acquired in a low-perfusion region (upper right part of the chest) of our adult volunteers. We obtained an SpO2 estimation accuracy ($A _{\mathbf {rms}}$) of 1.9 % and 3.1 % at the forehead and the chest in our adult volunteers, respectively. These performances were obtained after automatic rejection of 0.1 % and 30.0 %, respectively, of low-SNR signals by our dedicated quality index. In the dataset recorded on newborn patients in the NICU, we obtained an accuracy of 3.9 % after automatic rejection of 11.7 % of low-SNR signals by our quality index. These analyses were carried out following the procedures suggested by the ISO 80601-2-61:2011 standard, which specifies a target $A _{\mathbf {rms}} \le $ 4 % for SpO2 monitoring applications. These promising results suggest that reflectance pulse oximeters can achieve clinically acceptable accuracy, while being placed at locations less sensitive to limb motion artifacts - such as the forehead - thereby reducing the amount of SpO2-related false alarms in NICUs.

Accuracy and reliability of noninvasive stroke volume monitoring via ECG-gated 3D electrical impedance tomography in healthy volunteers.

Cardiac output (CO) and stroke volume (SV) are parameters of key clinical interest. Many techniques exist to measure CO and SV, but are either invasive or insufficiently accurate in clinical settings. Electrical impedance tomography (EIT) has been suggested as a noninvasive measure of SV, but inconsistent results have been reported. Our goal is to determine the accuracy and reliability of EIT-based SV measurements, and whether advanced image reconstruction approaches can help to improve the estimates. Data were collected on ten healthy volunteers undergoing postural changes and exercise. To overcome the sensitivity to heart displacement and thorax morphology reported in previous work, we used a 3D EIT configuration with 2 planes of 16 electrodes and subject-specific reconstruction models. Various EIT-derived SV estimates were compared to reference measurements derived from the oxygen uptake. Results revealed a dramatic impact of posture on the EIT images. Therefore, the analysis was restricted to measurements in supine position under controlled conditions (low noise and stable heart and lung regions). In these measurements, amplitudes of impedance changes in the heart and lung regions could successfully be derived from EIT using ECG gating. However, despite a subject-specific calibration the heart-related estimates showed an error of 0.0 ± 15.2 mL for absolute SV estimation. For trending of relative SV changes, a concordance rate of 80.9% and an angular error of -1.0 ± 23.0° were obtained. These performances are insufficient for most clinical uses. Similar conclusions were derived from lung-related estimates. Our findings indicate that the key difficulty in EIT-based SV monitoring is that purely amplitude-based features are strongly influenced by other factors (such as posture, electrode contact impedance and lung or heart conductivity). All the data of the present study are made publicly available for further investigations.

Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure.

OBJECTIVE: Electrical impedance tomography (EIT) shows potential for radiation-free and noninvasive hemodynamic monitoring. However, many factors degrade the accuracy and repeatability of these measurements. Our goal is to estimate the impact of this variability on the EIT-based monitoring of two important central hemodynamic parameters: stroke volume (SV) and pulmonary artery pressure (PAP). APPROACH: We performed simulations on a 4D ([Formula: see text]) bioimpedance model of a human volunteer to study the influence of four potential confounding factors (electrode belt displacement, electrode detachment, changes in hematocrit and lung air volume) on the performance of EIT-based SV and PAP estimation. Results were used to estimate how these factors affect the EIT measures of either absolute values or relative changes (i.e. trending). MAIN RESULTS: Our findings reveal that the absolute measurement of SV via EIT is very sensitive to electrode belt displacements and lung conductivity changes. Nonetheless, the trending ability of SV EIT might be a promising alternative. The timing-based measurement of PAP is more robust to lung conductivity changes but sensitive to longitudinal belt displacements at severe hypertensive levels and to rotational displacements (independent of the PAP level). SIGNIFICANCE: We identify and quantify the challenges of EIT-based SV and PAP monitoring. Absolute SV via EIT is challenging, but trending is feasible, while both the absolute and trending of PAP via EIT are mostly impaired by belt displacements.

A Versatile Noise Performance Metric for Electrical Impedance Tomography Algorithms.

Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring of patients under mechanical ventilation. EIT has the potential to offer continuous medical monitoring while being noninvasive, radiation free, and low cost. Due to their ill-posedness, image reconstruction typically uses regularization, which implies a hyperparameter controlling the tradeoff between noise rejection and resolution or other accuracies. In order to compare reconstruction algorithms, it is common to choose hyperparameter values such that the reconstructed images have equal noise performance (NP), i.e., the amount of measurement noise reflected in the images. For EIT many methods have been suggested, but none work well when the data originate from different measurement setups, such as for different electrode positions or measurement patterns. To address this issue, we propose a new NP metric based on the average signal-to-noise ratio in the image domain. The approach is validated for EIT using simulation experiments on a human thorax model and measurements on a resistor phantom. Results show that the approach is robust to the measurement configuration (i.e., number and position of electrodes, skip pattern) and the reconstruction algorithm used. We propose this novel approach as a way to select optimized measurement configurations and algorithms.

Noninvasive pulmonary artery pressure monitoring by EIT: a model-based feasibility study.

Current monitoring modalities for patients with pulmonary hypertension (PH) are limited to invasive solutions. A novel approach for the noninvasive and unsupervised monitoring of pulmonary artery pressure (PAP) in patients with PH was proposed and investigated. The approach was based on the use of electrical impedance tomography (EIT), a noninvasive and safe monitoring technique, and was tested through simulations on a realistic 4D bio-impedance model of the human thorax. Changes in PAP were induced in the model by simulating multiple types of hypertensive conditions. A timing parameter physiologically linked to the PAP via the so-called pulse wave velocity principle was automatically estimated from the EIT data. It was found that changes in PAP could indeed be reliably monitored by EIT, irrespective of the pathophysiological condition that caused them. If confirmed clinically, these findings could open the way for a new generation of noninvasive PAP monitoring solutions for the follow-up of patients with PH.

Non-invasive monitoring of pulmonary artery pressure from timing information by EIT: experimental evaluation during induced hypoxia.

Monitoring of pulmonary artery pressure (PAP) in pulmonary hypertensive patients is currently limited to invasive solutions. We investigate a novel non-invasive approach for continuous monitoring of PAP, based on electrical impedance tomography (EIT), a safe, low-cost and non-invasive imaging technology. EIT recordings were performed in three healthy subjects undergoing hypoxia-induced PAP variations. The pulmonary pulse arrival time (PAT), a timing parameter physiologically linked to the PAP, was automatically calculated from the EIT signals. Values were compared to systolic PAP values from Doppler echocardiography, and yielded strong correlation scores ([Formula: see text]) for all three subjects. Results suggest the feasibility of non-invasive, unsupervised monitoring of PAP.