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Cardiovascular disease (CVD) threatens the lives of many and affects their productivity. Wearable sensors can enable continuous monitoring of hemodynamic parameters to improve the diagnosis and management of CVD. Bio-Impedance (Bio-Z) is an effective non-invasive sensor for arterial pulse wave monitoring based on blood volume changes in the artery due to the deep penetration of its current signal inside the tissue. However, the measured data are significantly affected by the placement of electrodes relative to the artery and the electrode configuration. In this work, we created a Bio-Z simulation platform that models the tissue, arterial pulse wave, and Bio-Z sensing configuration using a 3D circuit model based on a time-varying impedance grid. A new method is proposed to accurately simulate the different tissue types such as blood, fat, muscles, and bones in a 3D circuit model in addition to the pulsatile activity of the arteries through a variable impedance model. This circuit model is simulated in SPICE and can be used to guide design decisions (i.e. electrode placement relative to the artery and electrode configuration) to optimize the monitoring of pulse wave prior to experimentation. We present extensive simulations of the arterial pulse waveform for different sensor locations, electrode sizes, current injection frequencies, and artery depths. These simulations are validated by experimental Bio-Z measurements.

Aims: There are few prospective reports of 1‐year outcomes for women with peripartum cardiomyopathy (PPCM). We report findings from the European Society of Cardiology EURObservational Research Programme PPCM Registry. Methods and results: The registry enrolled women from 51 countries from 2012 to 2018. Eligibility included: (i) a peripartum state, (ii) signs or symptoms of heart failure, (iii) left ventricular (LV) ejection fraction ≤45%, (iv) exclusion of alternative causes of heart failure. We report mortality, thromboembolism, stroke, rehospitalization, LV recovery and remodelling at 1 year. Differences between regions were compared. One‐year mortality data were available in 535 (71%) women and follow‐up differed across regions. At 1 year, death from any cause occurred in 8.4% of women, with regional variation (Europe 4.9%, Africa 6.5%, Asia‐Pacific 9.2%, Middle East 18.9%; p < 0.001). The frequencies of thromboembolism and stroke were 6.3% and 2.5%, respectively, and were similar across regions. A total of 14.0% of women had at least one rehospitalization and 3.5% had recurrent rehospitalizations (i.e. two or more). Overall, 66.1% of women had recovery of LV function (22% between 6 months and 1 year), with a mean LV ejection fraction increase from baseline of 21.2% (±13.6). Recovery occurred most frequently in Asia‐Pacific (77.5%) and least frequently in the Middle East (32.7%). There were significant regional differences in the use of heart failure pharmacotherapies. Conclusions: Approximately 1 in 12 women with PPCM had died by 1 year and thromboembolism and stroke occurred in 6.3% and 2.5%, respectively. Around 1 in 7 women had been rehospitalized and, in 1 in 3, LV recovery had not occurred. PPCM is associated with substantial mortality and morbidity globally. [ABSTRACT FROM AUTHOR]

Aims: Hypertensive disorders occur in women with peripartum cardiomyopathy (PPCM). How often hypertensive disorders co-exist, and to what extent they impact outcomes, is less clear. We describe differences in phenotype and outcomes in women with PPCM with and without hypertensive disorders during pregnancy., Methods and Results: The European Society of Cardiology EURObservational Research Programme PPCM Registry enrolled women with PPCM from 2012-2018. Three groups were examined: (i) women without hypertension (PPCM-noHTN); (ii) women with hypertension but without pre-eclampsia (PPCM-HTN); (iii) women with pre-eclampsia (PPCM-PE). Maternal (6-month) and neonatal outcomes were compared. Of 735 women included, 452 (61.5%) had PPCM-noHTN, 99 (13.5%) had PPCM-HTN and 184 (25.0%) had PPCM-PE. Compared to women with PPCM-noHTN, women with PPCM-PE had more severe symptoms (New York Heart Association class IV in 44.4% vs. 29.9%, P < 0.001), more frequent signs of heart failure (pulmonary rales in 70.7% vs. 55.4%, P = 0.002), a higher baseline left ventricular ejection fraction (LVEF) (32.7% vs. 30.7%, P = 0.005) and a smaller left ventricular end-diastolic diameter (57.4 ± 6.7 mm vs. 59.8 ± 8.1 mm, P = 0.001). There were no differences in the frequencies of death from any cause, rehospitalization for any cause, stroke, or thromboembolic events. Compared to women with PPCM-noHTN, women with PPCM-PE had a greater likelihood of left ventricular recovery (LVEF ≥ 50%) (adjusted odds ratio 2.08, 95% confidence interval 1.21-3.57) and an adverse neonatal outcome (composite of termination, miscarriage, low birth weight or neonatal death) (adjusted odds ratio 2.84, 95% confidence interval 1.66-4.87)., Conclusion: Differences exist in phenotype, recovery of cardiac function and neonatal outcomes according to hypertensive status in women with PPCM., (© 2021 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Continuous and unobtrusive blood pressure (BP) monitoring provides significant advantages in predicting the onset of cardiovascular disease. Bio-impedance sensing is a prominent method for continuous BP monitoring in a wearable form factor that can effectively measure blood pulsations from the arteries and translate them into BP. However, assessing the quality of the bio-impedance signal captured from small electrodes placed on the skin is required to determine the accuracy of BP estimation. In wearable devices, frequent movements of the electrodes on the skin are expected which cause non-optimal contact quality between the electrodes and the skin. This can lead to high skin-electrode impedance which can cause saturation of the current injection module of the bio-impedance device. This phenomenon degrades the signal quality In this paper, we present an automatic gain control (AGC) circuit that controls the amplitude of the current injection into the body based on sensing the skin-electrode impedance to ensure injection of maximum current to maximize the signal-to-noise ratio (SNR) while avoiding saturation of the current injection module. In this work, the proposed AGC method shows higher quality of blood pulsation from bio-impedance signal measured from a human subject with 1.59 dB improvement in SNR which leads to a better estimation of blood pressure.Clinical Relevance- The proposed automatic gain control (AGC) circuit establishes a more accurate method of continuous blood pressure monitoring using bio-impedance.

There is an urgent need to bring forth portable, low-cost, point-of-care diagnostic instruments to monitor patient health and wellbeing. This is elevated by the COVID-19 global pandemic in which the availability of proper lung imaging equipment has proven to be pivotal in the timely treatment of patients. Electrical impedance tomography (EIT) has long been studied and utilized as such a critical imaging device in hospitals especially for lung ventilation. Despite decades of research and development, many challenges remain with EIT in terms of 1) optimal image reconstruction algorithms, 2) simulation and measurement protocols, 3) hardware imperfections, and 4) uncompensated tissue bioelectrical physiology. Due to the inter-connectivity of these challenges, singular solutions to improve EIT performance continue to fall short of the desired sensitivity and accuracy. Motivated to gain a better understanding and optimization of the EIT system, we report the development of a bioelectric facsimile simulator demonstrating the dynamic operations, sensitivity analysis, and reconstruction outcome prediction of the EIT sensor with stepwise visualization. By building a sandbox platform to incorporate full anatomical and bioelectrical properties of the tissue under study into the simulation, we created a tissue-mimicking phantom with adjustable EIT parameters to interpret bioelectrical interactions and to optimize image reconstruction accuracy through improved hardware setup and sensing protocol selections.

This position paper focusses on the pathophysiology, diagnosis and management of women diagnosed with a cardiomyopathy, or at risk of heart failure (HF), who are planning to conceive or present with (de novo or previously unknown) HF during or after pregnancy. This includes the heterogeneous group of heart muscle diseases such as hypertrophic, dilated, arrhythmogenic right ventricular and non-classified cardiomyopathies, left ventricular non-compaction, peripartum cardiomyopathy, Takotsubo syndrome, adult congenital heart disease with HF, and patients with right HF. Also, patients with a history of chemo-/radiotherapy for cancer or haematological malignancies need specific pre-, during and post-pregnancy assessment and counselling. We summarize the current knowledge about pathophysiological mechanisms, including gene mutations, clinical presentation, diagnosis, and medical and device management, as well as risk stratification. Women with a known diagnosis of a cardiomyopathy will often require continuation of drug therapy, which has the potential to exert negative effects on the foetus. This position paper assists in balancing benefits and detrimental effects., (© 2021 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with frequent changes throughout a given day. Capturing blood pressure remotely and frequently (also known as ambulatory blood pressure monitoring) has traditionally been achieved by measuring blood pressure at discrete intervals using an inflatable cuff. However, there is growing interest in developing a cuffless ambulatory blood pressure monitoring system to measure blood pressure continuously. One such approach is by utilizing bioimpedance sensors to build regression models. A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive. In this paper, we propose the application of the domain-adversarial training neural network (DANN) method on our multitask learning (MTL) blood pressure estimation model, allowing for knowledge transfer between subjects. Our proposed model obtains average root mean square error (RMSE) of 4.80 ± 0.74 mmHg for diastolic blood pressure and 7.34 ± 1.88 mmHg for systolic blood pressure when using three minutes of training data, 4.64 ± 0.60 mmHg and 7.10 ± 1.79 respectively when using four minutes of training data, and 4.48±0.57 mmHg and 6.79±1.70 respectively when using five minutes of training data. DANN improves training with minimal data in comparison to both directly training and to training with a pretrained model from another subject, decreasing RMSE by 0.19 to 0.26 mmHg (diastolic) and by 0.46 to 0.67 mmHg (systolic) in comparison to the best baseline models. We observe that four minutes of training data is the minimum requirement for our framework to exceed ISO standards within this cohort of patients.

Continuous monitoring of cardiac parameters such as blood pressure (BP) and pulse transit time (PTT) from wearable devices can improve the diagnosis and management of the cardiovascular disease. Continuous monitoring of these parameters depends on monitoring arterial pulse wave based on the blood volume changes in the artery using non-invasive sensors such as bio-impedance (Bio-Z). PTT and BP monitoring require the measurement of multiple pulse signals along the artery through the placement of multiple sensors within a small distance. Conventionally, these Bio-Z sensors are excited by a single shared current source, which results in low directivity and distortion of pulse signal due to the interaction of the different sensors together. For a localized pulse sensing, each sensor should focus on a certain point on the artery to provide the most accurate arterial pulse wave, which improves PTT and BP readings. In this paper, we propose a multi-source multi-frequency method for multi-sensor Bio-Z measurement that relies on using separate current sources for each sensor with different frequencies to allow the separation of these signals in the frequency domain, which results in isolation in the spatial domain. The effectiveness of the new method was demonstrated by a reduction in the inter-beat-interval (IBI) root mean square error (RMSE) by 19% and an increase of average PTT by 15% compared to the conventional method.

Accurate measurement of the heart pulse waveform based on blood volume changes inside the arteries is crucial for reliable estimation of hemodynamic parameters such as blood pressure and cardiac output. Placement of blood volume sensors such as bio-impedance sensors close to the arterial sites is essential for the accurate measurement of the pulse waveform. The effect of sensor location relative to the wrist arteries on the pulse waveform had not been studied previously on human subjects. In this paper, we explore the effect of arterial and off-arterial placement of the bio-impedance sensor on important pulse waveform features such as pulse transit time (PTT), which is the travel time of the arterial pressure pulse between two sensors, and diastolic peak error (DPE), a measure of pulse signal sharpness. Placing the current injection and voltage sensing electrodes of a bio-impedance sensor on the radial artery provide greater accuracy for such features. We find that arterial PTT has a significantly lower standard deviation compared to off-arterial PTT indicating better signal quality. Similarly, we observe that DPE is much smaller for arterial bio-impedance which confirms our expectations. Based on these features, the location of the artery can be determined using an array of sensors placed around the artery.

Continuous monitoring of respiration and heart pressure pulse waves generated by lung and heart movements is essential in the diagnosis and management of cardiovascular and lung diseases. Traditional methods in measuring these physiological signals are not convenient for long-term monitoring during daily activities and sleeping due to their use of long wires and/or face masks, and leading to patients having to spend long duration in clinics while undergoing supervised monitoring. In this paper, we present a new method to measure global chest physiological signals using small-sized smart band-age like patches placed at different locations of the chest. The introduced patches communicate with each other using human body and detect small variations in the bio-Impedance (Bio-Z) depending on the mechanical heart and lung movements, where one patch injects an AC current from one side of the chest and several Bio-Z sensors measure the voltage difference across different locations of the chest. In order to prevent usage of long wires and increase convenience for wearable applications, electrical connection between current injection and Bio-Z sensing patches are eliminated. Independent component analysis is used to separate sources of physiological observations and improve accuracy of the system. We show that the presented system can successfully detect respiration rate and heart rate with an average error of 4.86% (less than 1 breath per minute) and 1.86% (less than 2 beats per minute) respectively tested on 6 healthy subjects over 6 minutes of data collection for each subject.

Continuous blood pressure (BP) monitoring is essential for diagnosis and management of cardiovascular disorders. Currently, BP is measured using cuff-based methods, which are obtrusive and not suitable for continuous monitoring. Estimation of BP using pulse transit time (PTT) is a prominent method that eliminates the need for a cuff. In this paper, we present a new method to estimate BP based on PTT measurements from an array of 2×2 bio-impedance sensors placed on the wrist, which can be integrated into a small wearable device such as a smart watch for continuous BP monitoring. Diastolic and systolic BP were estimated using AdaBoost regression model based on PTT features extracted from the wrist bio-impedance signals. Data was collected from three participants using our custom bio-impedance sensors. Our method can estimate BP accurately with correlation coefficient, mean absolute error (MAE) and standard deviation (STD) of 0.92, 1.71 and 2.46 mmHg for the diastolic BP and 0.94, 2.57 and 4.35 mmHg for the systolic BP.