Challenges and opportunities in cardiovascular health informatics

Kolmogorov-Arnold and Long Short-Term Memory Convolutional Network Models for Supervised Quality Recognition of Photoplethysmogram Signals

Photoplethysmogram (PPG) signals recover key physiological parameters as pulse, oximetry, and ECG. In this paper, we first employ a hybrid architecture combining the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for the analysis of PPG signals to enable an automated quality recognition. Then, we compare its performance to a simpler CNN architecture enriched with Kolmogorov-Arnold Network (KAN) layers. Our results suggest that the usage of KAN layers is effective at reducing the number of parameters, while also enhancing the performance of CNNs when equipped with standard Multi-Layer Perceptron (MLP) layers.

A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) tracer is the standard clinical technique to measure myocardial and vessel metabolism and viability and to investigate the metabolic syndrome associated with cardiovascular diseases. The quantitative analysis of PET images allows one to study the cardiovascular physiological processes, by extracting quantitative parameters from the analysis of the tracer kinetic. Here, we propose a new methodology to quantify and evaluate the evolution of glucose metabolism inside the myocardium and the large vascular structures over time. We merge and analyze PET and CT cardiac images, extracting different volumes of interest (VOI) and performing quantitative measurements. To validate it, we apply the methodology to merge images of the aorta vessel for patients affected by metabolic syndrome. The application of the proposed approach to the use case reveals a correlation between administered drugs and metabolic syndrome, measuring the glucose metabolic rate (MRGlu) in both the myocardium and aorta. The proposed methodology can be used to evaluate some cardiovascular risk indexes of diabetic patients, too. The proposed methodology can also be deployed to analyze other application domains.

Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. The advent of smart technologies has significantly impacted the management of CVD, offering innovative tools and solutions to improve patient outcomes. Smart technologies have revolutionized and transformed the management of CVD, providing innovative tools to improve patient care, enhance diagnostics, and enable more personalized treatment approaches. These smart tools encompass a wide range of technologies, including wearable devices, mobile applications,3D printing technologies, artificial intelligence (AI), remote monitoring systems, and electronic health records (EHR). They offer numerous advantages, such as real-time monitoring, early detection of abnormalities, remote patient management, and data-driven decision-making. However, they also come with certain limitations and challenges, including data privacy concerns, technical issues, and the need for regulatory frameworks. In this review, despite these challenges, the future of smart technologies in CVD management looks promising, with advancements in AI algorithms, telemedicine platforms, and bio fabrication techniques opening new possibilities for personalized and efficient care. In this article, we also explore the role of smart technologies in CVD management, their advantages and disadvantages, limitations, current applications, and their smart future.

Numerical investigation of different viscosity models on pulsatile blood flow of thoracic aortic aneurysm (TAA) in a patient-specific model

Aortic aneurysm is one of the most common aortic diseases that can lead to unfortunate consequences. Numerical simulations have an important role in the prediction of the aftereffects of vascular diseases including aneurysm. In this research, numerical simulation of pulsatile blood flow is performed for a 3-dimensional patient-specific model of a thoracic aortic aneurysm (TAA). Since the choice of blood viscosity model may have a significant impact on the simulation results, the effects of four non-Newtonian models of blood viscosity namely Carreau, Casson, Herschel-Bulkley, power low, and the Newtonian model on the wall shear stress (WSS) distribution, shear rate, and oscillatory shear index (OSI) have been analyzed. Simulation results showed that all the non-Newtonian and Newtonian models generally, predict similar patterns for blood flow and shear rate. At high flow rates in the cardiac cycle, the WSS value for all the models are similar to each other except for the power-law model due to the shear thinning behavior. All models predict high values of OSI on the inner wall of the ascending aorta and broad areas of the inner wall of the aneurysm sac. However, the Newtonian model predicts the OSI less than the non-Newtonian models in some areas of the aneurysm sac. Results indicated that the Newtonian model generally can predict the hemodynamic parameters of the blood flow similar to the non-Newtonian but for more precise analysis and to predict the regions prone to rupture and atherosclerosis, choosing a proper non-Newtonian model is recommended.

Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals

Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless BP estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, multi-parameter intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05 ± 5.26 mmHg and 1.58 ± 2.6 mmHg, in turn.

Benefits of Home-Based Solutions for Diagnosis and Treatment of Acute Coronary Syndromes on Health Care Costs: A Systematic Review

Diagnosing and treating acute coronary syndromes consumes a significant fraction of the healthcare budget worldwide. The pressure on resources is expected to increase with the continuing rise of cardiovascular disease, other chronic diseases and extended life expectancy, while expenditure is constrained. The objective of this review is to assess if home-based solutions for measuring chemical cardiac biomarkers can mitigate or reduce the continued rise in the costs of ACS treatment. A systematic review was performed considering published literature in several relevant public databases (i.e., PUBMED, Cochrane, Embase and Scopus) focusing on current biomarker practices in high-risk patients, their cost-effectiveness and the clinical evidence and feasibility of implementation. Out of 26,000 references screened, 86 met the inclusion criteria after independent full-text review. Current clinical evidence highlights that home-based solutions implemented in primary and secondary prevention reduce health care costs by earlier diagnosis, improved patient outcomes and quality of life, as well as by avoidance of unnecessary use of resources. Economical evidence suggests their potential to reduce health care costs if the incremental cost-effectiveness ratio or the willingness-to-pay does not surpass £20,000/QALY or €50,000 limit per 20,000 patients, respectively. The cost-effectiveness of these solutions increases when applied to high-risk patients.

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Compared with PPG signals, ECG signals are easy to collect using wearable devices. This study examined a novel continuous BP estimation approach using one-channel ECG signals for unobtrusive BP monitoring. A BP model is developed based on the fusion of a residual network and long short-term memory to obtain the spatial-temporal information of ECG signals. The public multiparameter intelligent monitoring waveform database, which contains ECG, PPG, and invasive BP data of patients in intensive care units, is used to develop and verify the model. Experimental results demonstrated that the proposed approach exhibited an estimation error of 0.07 ± 7.77 mmHg for mean arterial pressure (MAP) and 0.01 ± 6.29 for diastolic BP (DBP), which comply with the Association for the Advancement of Medical Instrumentation standard. According to the British Hypertension Society standards, the results achieved grade A for MAP and DBP estimation and grade B for systolic BP (SBP) estimation. Furthermore, we verified the model with an independent dataset for arrhythmia patients. The experimental results exhibited an estimation error of -0.22 ± 5.82 mmHg, -0.57 ± 4.39 mmHg, and -0.75 ± 5.62 mmHg for SBP, MAP, and DBP measurements, respectively. These results indicate the feasibility of estimating BP by using a one-channel ECG signal, thus enabling continuous BP measurement for ubiquitous health care applications.

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

Plug and play self-configurable IoT gateway node for telemonitoring of ECG

In the era of IoT and hyperconnection, an efficient electrocardiogram (ECG) telemonitoring system in wireless body area network (WBAN) demands an easy to use, self-configurable, secure, plug and play system with minimum hardware and computational complexities. The compression and quantization parameters required for an efficient representation of ECG signal will vary from patient to patient, from lead to lead, and from time to time. To this end, we propose a compressed sensing based WBAN with self-configurable gateway node (CS-SCGN) using deterministic binary block diagonal (DBBD) measurement matrix. The self-configurability is brought in through a low complex method for adaptive tuning of parameters with a careful choice of measurement matrix and data length. The redundant data transfer between sensor nodes and gateway node is avoided by addressing the diverse requirements in ECG remote health monitoring through three modes of configuration in the proposed system. A further reduction in communication and storage cost is achieved by optimizing the number of bits transmitted by sensor nodes by automatically tuning the compression ratio and quantization depth based on the dynamics of ECG signal. The self-configuration algorithm is designed to run at the gateway node in such a way as to optimize the power efficiency of sensor nodes without causing an extra power drain at the gateway node. Also, we investigate the feasibility of using smartphone as an IoT gateway node for performing primary processing to provide local utility before sending the received data to the remote server. The energy efficiency and real-time feasibility of the proposed algorithm are evaluated by implementing the gateway node on Odroid-XU4 board which runs on the same processor as in the latest smartphones. The experimental results indicate that our proposed self-configurable system at the gateway node makes the entire ECG telemonitoring system flexible, plug and play, patient independent and power-efficient.

Multi-Sensor Fusion Approach for Cuff-Less Blood Pressure Measurement

Ambulatory blood pressure (BP) provides valuable information for cardiovascular risk assessment. The present cuff-based devices are intrusive for long-term BP monitoring, whereas cuff-less BP measurement methods based on pulse transit time or multi-parameter are inferior in robustness and reliability by using electrocardiogram (ECG) and photoplethysmogram signals. This study examined a multi-sensor fusion-based platform and algorithm for systolic BP (SBP), mean arterial pressure (MAP), and diastolic BP (DBP) estimation. The proposed multi-sensor platform was comprised of one ECG sensor and two pulse pressure wave sensors for simultaneous signal collection. After extracting 35 features from the collected signals, a weakly supervised feature selection method was proposed for dimension reduction because the reference oscillometric technique-based BP are intermittent and can be redeemed as coarse-grained labels. BP models were then established using a multi-instance regression algorithm. A total of 85 participants including 17 hypertensive and 12 hypotensive patients were enrolled. Experimental results showed that the proposed approach exhibited good accuracy for diverse population with an estimation error of 1.62 ± 7.76 mmHg for SBP, 1.53 ± 6.03 mmHg for MAP, and 1.49 ± 5.52 for DBP, which complied with the association for the advancement of medical instrumentation standards in BP estimation. Moreover, the estimation accuracy is with random daily fluctuations rather than long-term degradation through a maximum two-month follow-up period indicated good robustness performance. These results suggest that the proposed approach is with high reliability and robustness and thus provides a novel insight for cuff-less BP measurement.

Spontaneous regression of cervical discs: Retrospective analysis of 14 cases

Cervical disc herniation is a condition which arises from compression of cervical spinal nerve root by the degenerated disc and vast majority of the patients are aged between 30 and 40 years. Spontaneous regression of cervical disc was first reported by Kriegerand Maniker in 1992. Our study is the second large series in literature. Besides, 4 patients are the first who were shown to have resorption in C 4-5.The records of patients diagnosed with cervical disc herniation who applied to the Spine Polyclinic between 2014 and 2018 were reviewed retrospectively. The files of the patients who were recommended surgery with the diagnosis of cervical disc herniation were examined. Patients who did not accept surgery on their own initiative, but who attended our outpatient clinic for a check-up were included in the study.Of a total of 14 patients, 28.57% (n = 4) were male and 71.43% (n = 10) were female. Mean age of the patients was 40.79 (range 25-60).The results of the study indicate that likelihood of spontaneous regression is higher in para-central or foraminal disc compared to central disc hernias. Although there are a limited number of case reports in the literature, conservative treatment seems to be a good option in patients without neurological deficits, with foraminal disc hernias and not requiring emergency surgery.

Blood pressure waveform contour analysis for assessing peripheral resistance changes in sepsis

BACKGROUND

This paper proposes a methodology for helping bridge the gap between the complex waveform information frequently available in an intensive care unit and the simple, lumped values favoured for rapid clinical diagnosis and management. This methodology employs a simple waveform contour analysis approach to compare aortic, femoral and central venous pressure waveforms on a beat-by-beat basis and extract lumped metrics pertaining to the pressure drop and pressure-pulse amplitude attenuation as blood passes through the various sections of systemic circulation.

RESULTS

Validation encompasses a comparison between novel metrics and well-known, analogous clinical metrics such as mean arterial and venous pressures, across an animal model of induced sepsis. The novel metric Ofe → vc, the direct pressure offset between the femoral artery and vena cava, and the clinical metric, ΔMP, the difference between mean arterial and venous pressure, performed well. However, Ofe → vc reduced the optimal average time to sepsis detection after endotoxin infusion from 46.2 min for ΔMP to 11.6 min, for a slight increase in false positive rate from 1.8 to 6.2%. Thus, the novel Ofe → vc provided the best combination of specificity and sensitivity, assuming an equal weighting to both, of the metrics assessed.

CONCLUSIONS

Overall, the potential of these novel metrics in the detection of diagnostic shifts in physiological behaviour, here driven by sepsis, is demonstrated.

Multiband ultra-thin flexible on-body transceivers for wearable health informatics

Substantial concentration has been associated to the monitoring of vital signs and human activity using wireless body area networks. However, one of the key technical challenges is to characterize an optimized transceiver geometry for desired isolation/bandwidth and specific absorption rate (SAR) characteristics, independent of transceiver chip on-body location. A microwave performance evaluation of monopole wearable transceiver was completed and results presented. A novel on-body antenna transceiver was designed, simulated and fabricated using an ultra-thin substrate RO 3010 (h = 250 µm) that ensures compactness and enhanced flexibility. The designed transceiver was evolved using very high value of dielectric constant using CST® Studio Suit and FEKO® numerical platforms. The on-body characterization for both fatty and bone tissues was experimentally verified for a bandwidth of 200 MHz. The fabricated configuration and real-time testing provides very promising microwave radiation parameters with a gain of 2.69 dBi, S₁₁ < - 13 dB at an operational frequency of 2.46 GHz. Multi-banding was achieved by introducing fractals in the design of the printed monopole. SAR calculations for feet, head and arm at microwave power levels ranging from 100 to 800 mW are incorporated. Furthermore, the real time data acquisition using developed transceiver and its experimental verification is illustrated.

Multi-Wavelength Photoplethysmography Enabling Continuous Blood Pressure Measurement With Compact Wearable Electronics

OBJECTIVE

To fight the "silent killer" hypertension, continuous blood pressure (BP) monitoring has been one of the most desired functions in wearable electronics. However, current BP measuring principles and protocols either involve a vessel occlusion process with a cuff or require multiple sensing nodes on the body, which makes it difficult to implement them in compact wearable electronics like smartwatches and wristbands with long-term wearability.

METHODS

In this work, we proposed a highly compact multi-wavelength photoplethysmography (MWPPG) module and a depth-resolved MWPPG approach for continuous monitoring of BP and systemic vascular resistance (SVR). By associating the wavelength-dependent light penetration depth in the skin with skin vasculatures, our method exploited the pulse transit time (PTT) on skin arterioles for tracking SVR (n = 20). Then, we developed an arteriolar PTT-based method for beat-to-beat BP measurement. The BP estimation accuracy of the proposed arteriolar PTT method was validated against Finometer (n = 20) and the arterial line (n = 4).

RESULTS

The correlation between arteriolar PTT and SVR was theoretically deduced and experimentally validated on 20 human subjects performing various maneuvers. The proposed arteriolar PTT-based method outperformed the traditional arterial PTT-based method with better BP estimation accuracy and simpler measurement setup, i.e., with a single sensing node.

CONCLUSION

The proposed depth-resolved MWPPG method can provide accurate measurements of SVR and BP, which are traditionally difficult to measure in a noninvasive or continuous fashion.

SIGNIFICANCE

This MWPPG work provides the wearable healthcare electronics of compact size with a low-cost and physiology-based solution for continuous measurement of BP and SVR.

Peripheral vascular disease assessment in the lower limb: a review of current and emerging non-invasive diagnostic methods

BACKGROUND

Worldwide, at least 200 million people are affected by peripheral vascular diseases (PVDs), including peripheral arterial disease (PAD), chronic venous insufficiency (CVI) and deep vein thrombosis (DVT). The high prevalence and serious consequences of PVDs have led to the development of several diagnostic tools and clinical guidelines to assist timely diagnosis and patient management. Given the increasing number of diagnostic methods available, a comprehensive review of available technologies is timely in order to understand their limitations and direct future development effort.

MAIN BODY

This paper reviews the available diagnostic methods for PAD, CVI, and DVT with a focus on non-invasive modalities. Each method is critically evaluated in terms of sensitivity, specificity, accuracy, ease of use, procedure time duration, and training requirements where applicable.

CONCLUSION

This review emphasizes the limitations of existing methods, highlighting a latent need for the development of new non-invasive, efficient diagnostic methods. Some newly emerging technologies are identified, in particular wearable sensors, which demonstrate considerable potential to address the need for simple, cost-effective, accurate and timely diagnosis of PVDs.

Hemodynamic effects of perfusion level of peripheral ECMO on cardiovascular system

Background

Peripheral ECMO is an effective cardiopulmonary support in clinical. The perfusion level could directly influence the performances and complications. However, there are few studies on the effects of the perfusion level on hemodynamics of peripheral ECMO.

Methods

The geometric model of cardiovascular system with peripheral ECMO was established. The blood assist index was used to classify the perfusion level of the ECMO. The flow pattern from the aorta to the femoral artery and their branches, blood flow rate from aorta to brain and limbs, flow interface, harmonic index of blood flow, wall shear stress and oscillatory shear index were chosen to evaluate the hemodynamic effects of peripheral ECMO.

Results

The results demonstrated that the flow rate of aorta outlets increased and perfusion condition had been improved. And the average flow to the upper limbs and brain has a positive correlation with BAI (r = 0.037, p < 0.05), while there is a negative correlation with lower limbs (r = − 0.054, p < 0.05). The HI has negative correlation with BAI (p < 0.05, r < 0). The blood interface is further from the heart with the BAI decrease. And the average WSS has negative correlation with BAI (p < 0.05, r = − 0.983) at the bifurcation of femoral aorta and has positive correlation with BAI (p < 0.05, r = 0.99) at the inner aorta. The OSI under different BAI is higher (reaching 0.4) at the inner wall of the aortic arch, the descending aorta and the femoral access.

Conclusions

The pathogenesis of peripheral ECMO with different perfusion levels varies; its further research will be thorough and extensive.

Sensor fusion methods for reducing false alarms in heart rate monitoring

Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.