Publications

Cardiac fibrosis occurs following insults to the myocardium and is characterized by the abnormal accumulation of non-compliant extracellular matrix (ECM), which compromises cardiomyocyte contractile activity and eventually leads to heart failure. This phenomenon is driven by the activation of cardiac fibroblasts (cFbs) to myofibroblasts and results in changes in ECM biochemical, structural and mechanical properties. The lack of predictive in vitro models of heart fibrosis has so far hampered the search for innovative treatments, as most of the cellular-based in vitro reductionist models do not take into account the leading role of ECM cues in driving the progression of the pathology. Here, we devised a single-step decellularization protocol to obtain and thoroughly characterize the biochemical and micro-mechanical properties of the ECM secreted by activated cFbs differentiated from human induced pluripotent stem cells (iPSCs). We activated iPSC-derived cFbs to the myofibroblast phenotype by tuning basic fibroblast growth factor (bFGF) and transforming growth factor beta 1 (TGF-beta 1) signalling and confirmed that activated cells acquired key features of myofibroblast phenotype, like SMAD2/3 nuclear shuttling, the formation of aligned alpha-smooth muscle actin (alpha- SMA)-rich stress fibres and increased focal adhesions (FAs) assembly. Next, we used Mass Spectrometry, nanoindentation, scanning electron and confocal microscopy to unveil the characteristic composition and the visco-elastic properties of the abundant, collagen-rich ECM deposited by cardiac myofibroblasts in vitro. Finally, we demonstrated that the fibrotic ECM activates mechanosensitive pathways in iPSC-derived cardiomyocytes, impacting on their shape, sarcomere assembly, phenotype, and calcium handling properties. We thus propose human bio-inspired decellularized matrices as animal-free, isogenic cardiomyocyte culture substrates recapitulating key pathophysiological changes occurring at the cellular level during cardiac fibrosis.

JTD Keywords: Adhesio, Cardiac fibrosis modelling, Decellularized extracellular matrix, Differentiation, Expression, Fibroblast activation, Fibronectin, Heart, Induced pluripotent stem cells, Ipsc-derived-cardiac fibroblasts, Ipsc-derived-cardiomyocyte, Myocardial-infarction, Neonatal cardiomyocytes, Smooth muscle actin, Substrate stiffness

The incorporation of exogenous lactate into cardiac tissues is a regenerative strategy that is rapidly gaining attention. In this work, two polymeric platforms were designed to achieve a sustained release of lactate, combining immediate and prolonged release profiles. Both platforms contained electrospun poly(lactic acid) (PLA) fibers and an alginate (Alg) hydrogel. In the first platform, named L/K(x)/Alg-PLA, lactate and proteinase K (x mg of enzyme per 1 g of PLA) were directly loaded into the Alg hydrogel, into which PLA fibers were assembled. In the second platform, L/Alg-K(x)/PLA, fibers were produced by electrospinning a proteinase K:PLA solution and, subsequently, assembled within the lactate-loaded hydrogel. After characterizing the chemical, morphological, and mechanical properties of the systems, as well as their cytotoxicity, the release profiles of the two platforms were determined considering different amounts of proteinase K (x = 5.2, 26, and 52 mg of proteinase K per 1 g of PLA), which is known to exhibit a broad cleavage activity. The profiles obtained using L/Alg-K(x)/PLA platforms with x = 26 and 52 were the closest to the criteria that must be met for cardiac tissue regeneration. Finally, the amount of lactate directly loaded in the Alg hydrogel for immediate release and the amount of protein in the electrospinning solution were adapted to achieve a constant lactate release of around 6 mM per day over 1 or 2 weeks. In the optimized bioplatform, in which 6 mM lactate was loaded in the hydrogel, the amount of fibers was increased by a factor of ×3, the amount of enzyme was adjusted to 40 mg per 1 g of PLA, and a daily lactate release of 5.9 ± 2.7 mM over a period of 11 days was achieved. Accordingly, the engineered device fully satisfied the characteristics and requirements for heart tissue regeneration.

JTD Keywords: biodegradable fibers, cardiac tissue regeneration, cell, drug-release, elastic-modulus, electrospinning, heart, nanoindentation, plasma treatment, proteinase, scaffold, stiffness, Alginate, Biodegradable fibers, Cardiac tissue regeneration, Electrospinning, Nanoindentation, Plasma treatment, Proteinase, Skeletal-muscle

We aimed to characterize the cerebral hemodynamic response to obstructive sleep apnea/hypopnea events, and evaluate their association to polysomnographic parameters. The characterization of the cerebral hemodynamics in obstructive sleep apnea (OSA) may add complementary information to further the understanding of the severity of the syndrome beyond the conventional polysomnography.Severe OSA patients were studied during night sleep while monitored by polysomnography. Transcranial, bed-side diffuse correlation spectroscopy (DCS) and frequency-domain near-infrared diffuse correlation spectroscopy (NIRS-DOS) were used to follow microvascular cerebral hemodynamics in the frontal lobes of the cerebral cortex. Changes in cerebral blood flow (CBF), total hemoglobin concentration (THC), and cerebral blood oxygen saturation (StO2) were analyzed.We considered 3283 obstructive apnea/hypopnea events from sixteen OSA patients (Age (median, interquartile range) 57 (52-64.5); females 25%; AHI (apnea-hypopnea index) 84.4 (76.1-93.7)). A biphasic response (maximum/minimum followed by a minimum/maximum) was observed for each cerebral hemodynamic variable (CBF, THC, StO2), heart rate and peripheral arterial oxygen saturation (SpO2). Changes of the StO2 followed the dynamics of the SpO2, and were out of phase from the THC and CBF. Longer events were associated with larger CBF changes, faster responses and slower recoveries. Moreover, the extrema of the response to obstructive hypopneas were lower compared to apneas (p < .001).Obstructive apneas/hypopneas cause profound, periodic changes in cerebral hemodynamics, including periods of hyper- and hypo-perfusion and intermittent cerebral hypoxia. The duration of the events is a strong determinant of the cerebral hemodynamic response, which is more pronounced in apnea than hypopnea events.© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society.

JTD Keywords: cerebral hemodynamics, desaturation, diffuse correlation spectroscopy, duration, hypopnea, hypoxemia, near-infrared spectroscopy, optical pathlength, oxygenation, severity, sleep disorder, spectroscopy, tissue, Adult, Airway obstruction, Apnea hypopnea index, Arterial oxygen saturation, Article, Blood oxygen tension, Blood-flow, Brain blood flow, Brain cortex, Cerebral hemodynamics, Controlled study, Diffuse correlation spectroscopy, Disease severity, Female, Frequency, Frontal lobe, Heart rate, Hemodynamics, Hemoglobin, Hemoglobin determination, Human, Humans, Major clinical study, Male, Near infrared spectroscopy, Near-infrared spectroscopy, Obstructive sleep apnea, Oxygen, Periodicity, Polysomnography, Sleep apnea syndromes, Sleep apnea, obstructive, Sleep disorder, Spectroscopy, near-infrared

A large portion of the elderly population are affected by cardiovascular diseases. Early prognosis of cardiomyopathies remains a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology based on significant indexes extracted from the characterization of the baroreflex mechanism in function of the influence of the cardio-respiratory activity over the blood pressure. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM-24 patients) and dilated (DCM-17 patients) were considered. In addition, thirty-nine control (CON) subjects were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic (ECG) signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal (BP), and the respiratory time (TT), from the respiratory flow (RF) signal, were extracted. The three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. DCM patients presented specific patterns in the respiratory response to decreasing blood pressure activity. ICM patients presented more stable cardiorespiratory activity in comparison with DCM patients. In general, CMP shown limited ability to regulate changes in blood pressure. In addition, patients also shown a limited ability of their cardiac and respiratory systems response to regulate incremental changes of the vascular variability and a lower heart rate variability. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. When comparing ICM patients and CON subjects, the best model achieved 88.9% accuracy, 87.5% sensitivity, and 89.7% specificity. When comparing DCM patients and CON subjects, the best model achieved 87.5% accuracy, 76.5% sensitivity, and 92.3% specificity. In conclusion, this study introduced a new method for the classification of patients by their etiology based on new indices from the analysis of the baroreflex mechanism.Copyright © 2023 Rodriguez, Schulz, Voss, Herrera, Benito and Giraldo.

JTD Keywords: abnormalities, blood pressure variability, cardio-respiratory variability, dilated cardiomyopathy, disease, heart-failure secondary, ischemic cardiomyopathy, ischemic-dilated cardiomyopathy, morphology-relative change, Baroreflex activity, Blood pressure variability, Cardio-respiratory variability, Cheyne-stokes respiration, Ischemic-dilated cardiomyopathy, Morphology-relative change

JTD Keywords: Organ-on-a-chip / lab-on-a-chip / organoids and ex vivo models, Vascular systems / vascularisation and heart

JTD Keywords: 3d printing, Biofabrication, Cardiac tissue engineering, Human heart, Human induced pluripotent stem cells

The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".

JTD Keywords: computational, engineered living, engineered organs, multicellular, Brain organoids, Cell diversity, Computational, Dynamics, Engineered living, Engineered organs, Heart, Maturation, Model, Multicellular, Mycobacterium-tuberculosis, Quantitative-analysis, Systems, Tissue deformation

Identification of asymptomatic patients at higher risk for suffering cardiac events remains controversial and challenging in Brugada syndrome (BS). In this work, we proposed an ECG-based classifier to predict BS-related symptoms, by merging the most predictive electrophysiological features derived from the ventricular depolarization and repolarization periods, along with autonomic-related markers. The initial feature space included local and dynamic ECG markers, assessed during a physical exercise test performed in 110 BS patients (25 symptomatic). Morphological, temporal and spatial properties quantifying the ECG dynamic response to exercise and recovery were considered. Our model was obtained by proposing a two-stage feature selection process that combined a resampled-based regularization approach with a wrapper model assessment for balancing, simplicity and performance. For the classification step, an ensemble was constructed by several logistic regression base classifiers, whose outputs were fused using a performance-based weighted average. The most relevant predictors corresponded to the repolarization interval, followed by two autonomic markers and two other makers of depolarization dynamics. Our classifier allowed for the identification of novel symptom-related markers from autonomic and dynamic ECG responses during exercise testing, suggesting the need for multifactorial risk stratification approaches in order to predict future cardiac events in asymptomatic BS patients.

JTD Keywords: brugada syndrome, depolarization disorders, ensemble classifier, heart-rate recovery, Acute myocardial-ischemia, Autonomics, Brugada syndrome, Brugadum syndrome, Cardiac death, Depolarization, Depolarization disorder, Depolarization disorders, Dynamic ecg, Electrocardiography, Electrophysiology, Ensemble classifier, Ensemble-classifier, Events, Exercise, Forecasting, Heart, Heart-rate, Heart-rate recovery, Prognosis, Qrs, Quantification, Recovery, Repolarization, Sudden cardiac death

Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements.Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients' status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT outcomes.Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups.We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care.Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

JTD Keywords: 6mwt, bayesian networks, copd, distance, exercise capacity, physical capacity, reference equations, severity, survival, wearables, 6mwt, Heart-rate recovery, Wearables

In situ tissue engineering strategies are a promising approach to activate the endogenous regenerative potential of the cardiac tissue helping the heart to heal itself after an injury. However, the current use of complex reprogramming vectors for the activation of reparative pathways challenges the easy translation of these therapies into the clinic. Here, we evaluated the response of mouse neonatal and human induced pluripotent stem cell-derived cardiomyocytes to the presence of exogenous lactate, thus mimicking the metabolic environment of the fetal heart. An increase in cardiomyocyte cell cycle activity was observed in the presence of lactate, as determined through Ki67 and Aurora-B kinase. Gene expression and RNA-sequencing data revealed that cardiomyocytes incubated with lactate showed upregulation of BMP10, LIN28 or TCIM in tandem with downregulation of GRIK1 or DGKK among others. Lactate also demonstrated a capability to modulate the production of inflammatory cytokines on cardiac fibroblasts, reducing the production of Fas, Fraktalkine or IL-12p40, while stimulating IL-13 and SDF1a. In addition, the generation of a lactate-rich environment improved ex vivo neonatal heart culture, by affecting the contractile activity and sarcomeric structures and inhibiting epicardial cell spreading. Our results also suggested a common link between the effect of lactate and the activation of hypoxia signaling pathways. These findings support a novel use of lactate in cardiac tissue engineering, modulating the metabolic environment of the heart and thus paving the way to the development of lactate-releasing platforms for in situ cardiac regeneration.Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

JTD Keywords: cardiac regeneration, cardiac tissue engineering, cell cycle, failure, growth, heart regeneration, induced pluripotent stem cells, ischemia, lactate, metabolic environment, metabolism, mouse, proliferation, repair, Bone morphogenetic protein-10, Cardiac tissue engineering, Cardiomyocytes, Cell cycle, Induced pluripotent stem cells, Lactate, Metabolic environment

The histone demethylase KDM1A is a multi- faceted regulator of vital developmental processes, including mesodermal and cardiac tube formation during gastrulation. However, it is unknown whether the fine-tuning of KDM1A splicing isoforms, already shown to regulate neuronal maturation, is crucial for the specification and maintenance of cell identity during cardiogenesis. Here, we discovered a temporal modulation of ubKDM1A and KDM1A+2a during human and mice fetal cardiac development and evaluated their impact on the regulation of cardiac differentiation. We revealed a severely impaired cardiac differentiation in KDM1A(-/-) hESCs that can be rescued by re-expressing ubKDM1A or catalytically impaired ubKDM1A-K661A, but not by KDM1A+2a or KDM1A+2a-K661A. Conversely, KDM1A+2a(-/-) hESCs give rise to functional cardiac cells, displaying increased beating amplitude and frequency and enhanced expression of critical cardiogenic markers. Our findings prove the existence of a divergent scaffolding role of KDM1A splice variants, independent of their enzymatic activity, during hESC differentiation into cardiac cells.

JTD Keywords: cell biology, molecular mechanism of gene regulation, omics, Bhlh transcription factor, Corest, Differentiation, Dna, Embryonic stem-cells, Heart, Lsd1, Phosphorylation, Proteins, Stem cells research, Swirm domain

Patients suffering from obstructive sleep apnea (OSA) usually present an increased sympathetic activity caused by the intermittent hypoxia effect on autonomic control. This study evaluated the relationship between sleep stages and the apnea duration, frequency, and type, as well as their impact on HRV markers in different groups of disease severity. The hypnogram and R-R interval signals were extracted in 81 OSA patients from night polysomnographic (PSG) recordings. The apnea-hypopnea index (AHI) defined patient classification as mild-moderate (AHI< 30, n 44) or severe (AHI>30, n 37). The normalized power in VLH, LF, and HF bands of RR series were estimated by a time-frequency approach and averaged in 1-min epochs of normal and apnea segments. The autonomic response and the impact of sleep stages were assessed in both segments to compare patient groups. Deeper sleep stages (particularly S2) concentrated the shorter and mild apnea episodes (from 10 to 40 s) compared to light (SWS) and REM sleep. Longer episodes (>50 s) although less frequent, were of similar incidence in all stages. This pattern was more pronounced for the group of severe patients. Moreover, during apnea segments, LF nu was higher (p 0.044) for the severe group, since V LF nu and HF nu presented the greatest changes when compared to normal segments. The non-REM sleep seems to better differentiate OSA patients groups, particularly through VLF nu and HF nu (p<0.001). A significant difference in both sympathetic and vagal modulation between REM and non-REM sleep was only found within the severe group. These results confirm the importance of considering sleep stages for HRV analysis to further assess OSA disease severity, beyond the traditional and clinically limited AHI values.Clinical relevance - Accounting for sleep stages during HRV analysis could better assess disease severity in OSA patients. © 2021 IEEE.

JTD Keywords: blood-pressure, genomic consequences, intermittent hypoxia, rapid-eye-movement, sympathetic activity, Heart rate, Heart-rate-variability, Human, Humans, Polysomnography, Rem sleep, Sleep apnea, obstructive, Sleep disordered breathing, Sleep stage, Sleep stages, Sleep, rem

Fixed sample entropy (fSampEn) is a promising technique for the analysis of respiratory electromyographic (EMG) signals. Its use has shown outperformance of amplitude-based estimators such as the root mean square (RMS) in the evaluation of respiratory EMG signals with cardiac noise and a high correlation with respiratory signals, allowing changes in respiratory muscle activity to be tracked. However, the relationship between the fSampEn response to a given muscle activation has not been investigated. The aim of this study was to analyze the nature of the fSampEn measurements that are produced as the EMG activity increases linearly. Simulated EMG signals were generated and increased linearly. The effect of the parameters r and the size of the moving window N of the fSampEn were evaluated and compared with those obtained using the RMS. The RMS showed a linear trend throughout the study. A non-linear, sigmoidal-like behavior was found when analyzing the EMG signals using the fSampEn. The lower the values of r, the higher the non-linearity observed in the fSampEn results. Greater moving windows reduced the variation produced by too small values of r.Clinical Relevance - Understanding the inherent non-linear relationship produced when using the fSampEn in EMG recordings will contribute to the improvement of the respiratory muscle activation assessment at different levels of respiratory effort in patients with respiratory conditions, particularly during the inspiratory phase © 2021 IEEE.

JTD Keywords: Breathing muscle, Breathing rate, Electromyography, Entropy, Heart, Human, Humans, Respiratory muscles, Respiratory rate

JTD Keywords: biocompatibility, bioresorbable stents, degradation, mechanical-properties, poly(epsilon-caprolactone), poly-l-lactic acid, polylactide, radiopacity, thermogel, x-ray imaging, Barium sulfate, Biocompatibility, Bioresorbable, Bioresorbable scaffolds, Bioresorbable stent, Bioresorbable stents, Blood vessels, Computerized tomography, Controlled drug delivery, Coronary heart disease, Direct-writing, Endothelial cells, Fabrication strategies, Injection molding, Lactic acid, Poly-l-lactic acid, Poly-l-lactic acids, Radiopacity, Scaffolds (biology), Solvent cast, Solvent-cast direct-writing, Solvents, Stents, Struts, Sulfur compounds, Targeted drug delivery, X-ray imaging

Currently, three major circumstances threaten the management of bacterial infections: increasing antimicrobial resistance, expansion of chronic biofilm-associated infections, and lack of an appropriate approach to treat them. To date, the development of accelerated drug susceptibility testing of biofilms and of new antibiofouling systems has not been achieved despite the availability of different methodologies. There is a need for easy-to-use methods of testing the antibiotic susceptibility of bacteria that form biofilms and for screening new possible antibiofilm strategies. Herein, we present a microfluidic platform with an integrated interdigitated sensor (BiofilmChip). This new device allows an irreversible and homogeneous attachment of bacterial cells of clinical origin, even directly from clinical specimens, and the biofilms grown can be monitored by confocal microscopy or electrical impedance spectroscopy. The device proved to be suitable to study polymicrobial communities, as well as to measure the effect of antimicrobials on biofilms without introducing disturbances due to manipulation, thus better mimicking real-life clinical situations. Our results demonstrate that BiofilmChip is a straightforward tool for antimicrobial biofilm susceptibility testing that could be easily implemented in routine clinical laboratories.

JTD Keywords: cells, model, resistance, shear, technology, In-vitro

The creation of cardiac tissue models for preclinical testing is still a non-solved problem in drug discovery, due to the limitations related to thein vitroreplication of cardiac tissue complexity. Among these limitations, the difficulty of mimicking the functional properties of the myocardium due to the immaturity of the used cells hampers the obtention of reliable results that could be translated into human patients.In vivomodels are the current gold standard to test new treatments, although it is widely acknowledged that the used animals are unable to fully recapitulate human physiology, which often leads to failures during clinical trials. In the present work, we present a microfluidic platform that aims to provide a range of signaling cues to immature cardiac cells to drive them towards an adult phenotype. The device combines topographical electrospun nanofibers with electrical stimulation in a microfabricated system. We validated our platform using a co-culture of neonatal mouse cardiomyocytes and cardiac fibroblasts, showing that it allows us to control the degree of anisotropy of the cardiac tissue inside the microdevice in a cost-effective way. Moreover, a 3D computational model of the electrical field was created and validated to demonstrate that our platform is able to closely match the distribution obtained with the gold standard (planar electrode technology) using inexpensive rod-shaped biocompatible stainless-steel electrodes. The functionality of the electrical stimulation was shown to induce a higher expression of the tight junction protein Cx-43, as well as the upregulation of several key genes involved in conductive and structural cardiac properties. These results validate our platform as a powerful tool for the tissue engineering community due to its low cost, high imaging compatibility, versatility, and high-throughput configuration capabilities.

JTD Keywords: bioreactor, cardiac tissue engineering, cardiomyocytes, electrospinning, fabrication, fibroblasts, heart-on-a-chip, heart-tissue, in vitro models, myocardium, orientation, platform, scaffolds, Cardiac tissue engineering, Electrospinning, Field stimulation, Heart-on-a-chip, In vitro models, Microphysiological system

JTD Keywords: cardiac-hypertrophy, heart, mechanisms, Protein-kinase-d

CCBY Intermittent hypoxia (IH) produces autonomic dysfunction that promotes the development of arrhythmia and hypertension in patients with obstructive sleep apnea (OSA). This paper investigated different heart rate variability (HRV) indices in the context of IH using a rat model for OSA. Linear and non-linear HRV parameters were assessed from ultra-short (15-s segments) and short-term (5 min) analyses of heartbeat time-series. Transient changes observed from pre-apnea segments to hypoxia episodes were evaluated, besides the relative and global impact of IH, as a function of its severity. Results showed an overall increase in ultra-short HRV markers as immediate response to hypoxia: standard deviation of normal RR intervals, SDNN=1.2 ms (IQR: 1.1-2.1) vs 1.4 ms (IQR: 1.2-2.2), p=0.015; root mean square of the successive differences, RMSSD=1.7 ms (IQR: 1.5-2.2) vs 1.9 ms (IQR: 1.6-2.4), p=0.031. The power in the very low frequency (VLF) band also showed a significant increase: 0.09 ms2 (IQR: 0.05-0.20) vs 0.16 ms2 (IQR: 0.12-0.23), p=0.016, probably associated with the potentiation of the carotid body chemo-sensory response to hypoxia. Moreover, a clear link between severity of IH and short-term HRV measures was found in VLF and LF power, besides their progressive increase seen throughout the experiment after each apnea sequence. However, only those markers quantifying fragmentation levels in RR series were significantly affected when the experiment ended, as compared to baseline measures: percentage of inflection points, PIP=49% (IQR: 45-51) vs 53% (IQR: 47-53), p=0.031; percentage of short (≥3 RR intervals) accelerated/decelerated segments, PSS=75% (IQR: 51-81) vs 87% (IQR: 51-90), p=0.046. These findings suggest a significant deterioration of cardiac rhythm with a more erratic behavior beyond the normal sinus arrhythmia, that may lead to a future cardiac condition.

JTD Keywords: artificial intelligence, atmospheric modeling, electrocardiography, heart rate variability, hypoxia rat model, intermittent hypoxia, obstructive apneas, protocols, radio access technologies, Artificial intelligence, Atmospheric modeling, Electrocardiography, Heart rate variability, Hypoxia rat model, Intermittent hypoxia, Obstructive apneas, Protocols, Radio access technologies, Rats

Respiration rate can be assessed by analyzing respiratory changes of the electrocardiogram (ECG). Several methods can be applied to derive the respiratory signal from the ECG (EDR signal). In this study, four EDR estimation methods based on QRS features were analyzed. A database with 44 healthy subjects (16 females) in supine and sitting positions was analyzed. Respiratory flow and ECG recordings on leads I, II, III and a Chest lead was studied. A QR slope-based method, an RS slope-based method, an QRS angle-based method and an QRS area-based method were applied. Their performance was evaluated by the correlation coefficient with the reference respiratory volume signal. Significantly higher correlation coefficients in the range r = 0.77 – 0.86 were obtained with the Chest lead for all methods. The EDR estimation method based on the QRS angle provided the highest similarity with the volume signal for all recording leads and subject positions. We found no statistically significant differences according to gender or subject position.Clinical Relevance— This work analyzes the EDR signal from four electrocardiographic leads to obtain the respiratory signal and contributes to a simplified analysis of respiratory activity.

JTD Keywords: Electrocardiography, Lead, Estimation, Correlation coefficient, Databases, Heart, Correlation

In this work, we evaluated a non-linear approach to estimate morphological variations in ECG depolarization, in the context of intermittent hypoxia (IH). Obstructive apnea sequences were provoked for 15 minutes in anesthetized Sprague-Dawley rats, alternating with equal periods of normal breathing, in a recurrent obstructive sleep apnea (OSA) model. Each apnea episode lasted 15 s, while the frequency used for each sequence was randomly selected. Average heartbeats obtained before the start and at the end of each episode, were delineated to extract only the QRS wave. Then, the segmented QRS waves were non-linearly aligned using the dynamic time warping (DWT) algorithm. Morphological QRS changes in both the amplitude and temporal domains were estimated from this alignment procedure. The hypoxic and basal segments were analyzed using ECG (lead I) recordings acquired during the experiment. To assess the effects of IH over time, the changes relative to the basal QRS wave were determined, in the intervals prior to each successive events until the end of the experiment. The results showed a progressive increase in the amplitude and time-domain morphological markers of the QRS wave along the experiment, which were strongly correlated with the changes in traditional QRS markers (r ≈ 0.9). Significant changes were found between pre-apnea and hypoxic measures only for the time-domain analysis (p<0.001), probably due to the short duration of the simulated apnea episodes.Clinical relevance Increased variability in ECG depolarization morphology during recurrent hypoxic episodes would be closely related to the expression of cardiovascular dysfunction in OSA patients.

JTD Keywords: Electrocardiography, Rats, Heart rate variability, Sleep apnea, Protocols, Heuristic algorithms

Neural respiratory drive as measured by the electromyography allows the study of the imbalance between the load on respiratory muscles and its capacity. Surface respiratory electromyography (sEMG) is a non-invasive tool used for indirectly assessment of NRD. It also provides a way to evaluate the level and pattern of respiratory muscle activation. The prevalence of electrocardiographic activity (ECG) in respiratory sEMG signals hinders its proper evaluation. Moreover, the occurrence of abnormal heartbeats or cardiac arrhythmias in respiratory sEMG measures can make even more challenging the NRD estimation. Respiratory sEMG can be evaluated using the fixed sample entropy (fSampEn), a technique which is less affected by cardiac artefacts. The aim of this work was to investigate the performance of the fSampEn, the root mean square (RMS) and the average rectified value (ARV) on respiratory sEMG signals with supraventricular arrhythmias (SVA) for NRD estimation. fSampEn, ARV and RMS parameters increased as the inspiratory load increased during the test. fSampEn was less influenced by ECG with SVAs for the NRD estimation showing a greater response to respiratory sEMG, reflected with a higher percentage increase with increasing load (228 % total increase, compared to 142 % and 135 % for ARV and RMS, respectively).

JTD Keywords: Electrocardiography, Muscles, Electrodes, Estimation, Band-pass filters, Electromyography, Heart beat

A novel ECG-derived respiration (EDR) approach is presented to efficiently estimate the respiratory rate. It combines spatial rotations and magnitude variations of the heart's electrical vector due to respiration. Orthogonal leads X, Y and Z from 10 volunteers were analyzed during a tilt table test. The largest vector magnitude (VM) within each QRS loop was assessed, and its 3D coordinates were converted into unit quaternion qb. Angular distances between these quaternions and the axes of the reference coordinate system, θ x , θ y and θ z , were then computed as EDR signals to track their relative variations caused by respiration. The respiratory rate was estimated on the spectrum of individual EDR signals obtained from the angular distances and VM time-series, but also on EDR signals obtained by principal component analysis (PCA). Relative errors (eR) to the reference respiratory signal exhibited relatively low values. The combination of EDR signals' spectrum {θ X ,θ Y, θ Z , VM} (eR=0.63±4.15%) and individual signals derived from θ X (e R =0.46±8.22%) and PCA (eR=0.36±6.58%) achieved the overall best results. The proposed method represents a computationally efficient alternative to other EDR approaches, but its robustness should be further investigated. The method could be enhanced if combined with other features tracking morphological changes induced by respiration.

JTD Keywords: Heart, Three-dimensional displays, Quaternions, Robustness, Computational efficiency, Cardiology, Principal component analysis

Chronic obstructive pulmonary disease (COPD) patients exhibit impaired autonomic control which can be assessed by heart rate variability analysis. The study aims to evaluate the cardiac autonomic responses of COPD patients after completing a conventional six-minute walk test (6MWT). Fifty COPD patients were included in the study, for which an ECG signal (lead II) was acquired by a wearable device, before, during, and after the test. We used the heart rate (HR) time-series to assess the heart rate dynamic during recovery. The heart rate recovery (HRR) marker was evaluated every 5 s after the 6MWT and showed different dynamic trends among severity groups. We compared the HRR among patient groups classified according to the GOLD standard. Significantly larger normalized HRR values (nHRR) were found in mild COPD patients (n=23, GOLD={1,2}; nHRR 1 =14.B±7.5 %, nHRR 2 =18.6±8.1 %) compared to those with more disease severity (n=23, GOLD={3,4}; nHRR 1 =9.3±5.8 %, p=0.002; and nHRR 2 = 13.7±6.7%, p=0.041). The largest differences were observed around the first 30 s of the recovery phase (nHRR=10.8±6.6 % vs. nHRR=5.6±4 % p=0.001). Our results showed a slower recovery for the severest patients, suggesting that cardiac parameters like the ones we propose here, may provide valuable information for a better characterization of COPD severity.

JTD Keywords: Pulmonary diseases, Wearable computers, Electrocardiography, Market research, Cardiology, Heart rate variability

In vitro surrogate models of human cardiac tissue hold great promise in disease modeling, cardiotoxicity testing, and future applications in regenerative medicine. However, the generation of engineered human cardiac constructs with tissue-like functionality is currently thwarted by difficulties in achieving efficient maturation at the cellular and/or tissular level. Here, we report on the design and implementation of a platform for the production of engineered cardiac macrotissues from human pluripotent stem cells (PSCs), which we term “CardioSlice.” PSC-derived cardiomyocytes, together with human fibroblasts, are seeded into large 3D porous scaffolds and cultured using a parallelized perfusion bioreactor with custom-made culture chambers. Continuous electrical stimulation for 2 weeks promotes cardiomyocyte alignment and synchronization, and the emergence of cardiac tissue-like properties. These include electrocardiogram-like signals that can be readily measured on the surface of CardioSlice constructs, and a response to proarrhythmic drugs that is predictive of their effect in human patients.

JTD Keywords: Cardiac tissue engineering, CardioSlice, ECG-like signals, Electrical stimulation, Heart physiology, Human induced pluripotent stem cells, Perfusion bioreactor, Tissue-like properties

Adult zebrafish, in contrast to mammals, are able to regenerate their hearts in response to injury or experimental amputation. Our understanding of the cellular and molecular bases that underlie this process, although fragmentary, has increased significantly over the last years. However, the role of the extracellular matrix (ECM) during zebrafish heart regeneration has been comparatively rarely explored. Here, we set out to characterize the ECM protein composition in adult zebrafish hearts, and whether it changed during the regenerative response. For this purpose, we first established a decellularization protocol of adult zebrafish ventricles that significantly enriched the yield of ECM proteins. We then performed proteomic analyses of decellularized control hearts and at different times of regeneration. Our results show a dynamic change in ECM protein composition, most evident at the earliest (7 days post-amputation) time-point analyzed. Regeneration associated with sharp increases in specific ECM proteins, and with an overall decrease in collagens and cytoskeletal proteins. We finally tested by atomic force microscopy that the changes in ECM composition translated to decreased ECM stiffness. Our cumulative results identify changes in the protein composition and mechanical properties of the zebrafish heart ECM during regeneration.

JTD Keywords: Animal models, Atomic force microscopy, Cardiovascular disease, Cardiovascular function or biology, Developmental biology, Extracellular matrix, Heart regeneration, Proteomic analysis

Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDCHR) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDCLR) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDCHR patients presented lowered HRV and increased BPV compared to both IDCLR patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDCLR vs. IDCHR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.

JTD Keywords: Idiopathic dilated cardiomyopathy, Heart rate variability, Blood pressure variability, Coupling analysis, Sudden cardiac death, Risk stratification

In this paper, a non-linear HRV analysis was performed to assess fragmentation signatures observed in heartbeat time series after intermittent hypoxia (IH). Three markers quantifying short-term fragmentation levels, PIP, IALS and PSS, were evaluated on R-R interval series obtained in a rat model of recurrent apnea. Through airway obstructions, apnea episodes were periodically simulated in six anesthetized Sprague-Dawley rats. The number of apnea events per hour (AHI index) was varied during the first half of the experiment while apnea episodes lasted 15 s. For the second part, apnea episodes lasted 5, 10 or 15 s, but the AHI index was fixed. Recurrent apnea was repeated for 15-min time intervals in all cases, alternating with basal periods of the same duration. The fragmentation markers were evaluated in segments of 5 minutes, selected at the beginning and end of the experiment. The impact of the heart and breathing rates (HR and BR, respectively) on the parameter estimates was also investigated. The results obtained show a significant increase (from 5 to 10%, p <; 0.05) in fragmentation measures of heartbeat time series after IH, indicating a clear deterioration of the initial conditions. Moreover, there was a strong linear relationship (r > 0.9) between these markers and BR, as well as with the ratio given by HR/BR. Although fragmentation may be impacted by IH, we found that it is highly dependent on HR and BR values and thus, they should be considered during its calculation or used to normalize the fragmentation estimates.

JTD Keywords: Rats, Time series analysis, Radio access technologies, Protocols, Heart beat

Hypoxia induced by obstructive sleep apnea (OSA) leads to the deregulation of the autonomic nervous system (ANS), resulting in an abnormally increased sympathetic activity. Since ANS modulation varies throughout the night, notably for each sleep stage, the hypno-gram and heart rate signals of 81 OSA patients were collected during a polysomnography. They were classified as mild-moderate (n=44) or severe (n=37) based on their apnea-hypopnea index (AHI). Spectral heart rate variability (HRV) series were extracted by a time-frequency approach. These series were then averaged for each sleep stage, in order to compare the sympathetic modulation of mild-moderate and severe patients at the following phases: rapid eye movement (REM), S1, S2 and SWS (slow wave sleep). According to normalized power at the low-frequency band (LFnu) values, severe OSA seems to be associated with an increased sympathetic modulation at non-REM sleep. Moreover, a decreased autonomic variability throughout the night may be related to a reduced adaptability of the cardiovascular system, characterizing a more advanced stage of the disease. These results provide further evidence for the role of autonomic alterations induced by hypoxia, suggesting the use of HRV analysis, together with AHI, for the study of OSA severity.

JTD Keywords: Sleep apnea, Heart rate variability, Modulation, Indexes, Standards

Background: Tissue hypoxia-reoxygenation characterizes obstructive sleep apnea (OSA), a very prevalent respiratory disease associated with increased cardiovascular morbidity and mortality. Experimental studies indicate that intermittent hypoxia (IH) mimicking OSA induces oxidative stress and inflammation in heart tissue at the cell and molecular levels. However, it remains unclear whether IH modifies the passive stiffness of the cardiac tissue extracellular matrix (ECM). Aim: To investigate multiscale changes of stiffness induced by chronic IH in the ECM of left ventricular (LV) myocardium in a murine model of OSA. Methods: Two-month and 18-month old mice (N = 10 each) were subjected to IH (20% O2 40 s–6% O2 20 s) for 6 weeks (6 h/day). Corresponding control groups for each age were kept under normoxia. Fresh LV myocardial strips (~7 mm × 1 mm × 1 mm) were prepared, and their ECM was obtained by decellularization. Myocardium ECM macroscale mechanics were measured by performing uniaxial stress–strain tensile tests. Strip macroscale stiffness was assessed as the stress value (σ) measured at 0.2 strain and Young’s modulus (EM) computed at 0.2 strain by fitting Fung’s constitutive model to the stress–strain relationship. ECM stiffness was characterized at the microscale as the Young’s modulus (Em) measured in decellularized tissue slices (~12 μm tick) by atomic force microscopy. Results: Intermittent hypoxia induced a ~1.5-fold increase in σ (p < 0.001) and a ~2.5-fold increase in EM (p < 0.001) of young mice as compared with normoxic controls. In contrast, no significant differences emerged in Em among IH-exposed and normoxic mice. Moreover, the mechanical effects of IH on myocardial ECM were similar in young and aged mice. Conclusion: The marked IH-induced increases in macroscale stiffness of LV myocardium ECM suggests that the ECM plays a role in the cardiac dysfunction induced by OSA. Furthermore, absence of any significant effects of IH on the microscale ECM stiffness suggests that the significant increases in macroscale stiffening are primarily mediated by 3D structural ECM remodeling.

JTD Keywords: Atomic force microscopy, Heart mechanics, Myocardial stiffness, Obstructive sleep apnea, Tensile test, Ventricular strain

Background: Heart failure (HF) – a very prevalent disease with high morbidity and mortality – usually presents with diastolic dysfunction. Although post-menopause women are at increased risk of HF and diastolic dysfunction, poor attention has been paid to clinically and experimentally investigate this group of patients. Specifically, whether myocardial stiffness is affected by menopause is unknown. Aim: To investigate whether loss of female sexual hormones modifies the Young’s modulus (E) of left ventricular (LV) myocardial tissue in a mouse model of menopause induced by ovariectomy (OVX). Methods: After 6 months of bilateral OVX, eight mice were sacrificed, fresh LV myocardial strips were prepared (∼8 × 1 × 1 mm), and their passive stress–stretch relationship was measured. E was computed by exponential fitting of the stress–stretch relationship. Subsequently, to assess the relative role of cellular and extracellular matrix components in determining OVX-induced changes in E, the tissues strips were decellularized and subjected to the same stretching protocol to measure E. A control group of eight sham-OVX mice was simultaneously studied. Results: E (kPa; m ± SE) in OVX mice was ∼twofold lower than in controls (11.7 ± 1.8 and 22.1 ± 4.4, respectively; p < 0.05). No significant difference between groups was found in E of the decellularized tissue (31.4 ± 12.05 and 40.9 ± 11.5, respectively; p = 0.58). Conclusion: Loss of female sexual hormones in an OVX model induces a reduction in the passive stiffness of myocardial tissue, suggesting that active relaxation should play a counterbalancing role in diastolic dysfunction in post-menopausal women with HF.

JTD Keywords: Decellularized tissue, Female hormones, Heart tissue, Ovariectomy, Stress-strain

Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1 min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89 %, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.

JTD Keywords: Chronic heart failure, Ensemble average, Periodic and non-periodic breathing, Respiratory pattern

Patients with Chronic Heart Failure (CHF) often develop oscillatory breathing patterns. This work proposes the characterization of respiratory pattern by Wavelet Transform (WT) technique to identify Periodic Breathing pattern (PB) and Non-Periodic Breathing pattern (nPB) through the respiratory flow signal. A total of 62 subjects were analyzed: 27 CHF patients and 35 healthy subjects. Respiratory time series were extracted, and statistical methods were applied to obtain the most relevant information to classify patients. Support Vector Machine (SVM) were applied using forward selection technique to discriminate patients, considering four kernel functions. Differences between these parameters are assessed by investigating the following four classification issues: healthy subjects versus CHF patients, PB versus nPB patients, PB patients versus healthy subjects, and nPB patients versus healthy subjects. The results are presented in terms of average accuracy for each kernel function, and comparison groups.

JTD Keywords: Chronic heart failure, Forward selection, Non-periodic breathing, Periodic breathing, Support vector machine

Obstructive Sleep Apnea (OSA) is a sleep disorder with a high prevalence in the general population. It is a risk factor for many cardiovascular diseases, and an independent risk factor for cerebrovascular diseases such as stroke. After an apnea episode, both arterial blood pressure and cerebral blood flow velocity change in function of the apnea duration (AD). We hypothesized that the relative excursion in heart rate (AHR), defined as the percentage difference between the maximum and the minimum heart rate values associated to an obstructive apnea event, is also related to AD. In this work we studied the relationship between apnea-related AHR and AD in a population of eight patients with severe OSA. AHR and AD showed a moderate but statistically significant correlation (p <; 0.0001) in a total of 1454 obstructive apneas analyzed. The average heart rate excursion for apneas with AD ≥ 30s (ΔHR = 31.29 ± 6.64%) was significantly greater (p = 0.0002) than for apneas with AD ∈ [10,20)s (ΔHR = 18.14±3.08%). We also observed that patients with similar Apnea-Hypopnea Index (AHI) may exhibit remarkably different distributions of AHR and AD, and that patients with a high AHI need not have a higher average AHR than others with a lower severity index. We conclude that the overall apnea-induced heart rate excursion is partially explained by the duration of apnoeic episodes, and it may be a simple measure of the cardiovascular stress associated with OSA that is not directly reflected in the AHI.

JTD Keywords: Heart rate, Sleep apnea, Correlation, Indexes, Sociology, Blood vessels

Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.

JTD Keywords: Feature extraction, Blood pressure, Heart rate, Estimation, Data mining, Covariance matrices, Hospitals

In computer-assisted beating heart surgeries, accurate tracking of the heart's motion is of huge importance and there is a continuous need to eliminate any source of error that might disturb the tracking process. One source of error is the specular reflection that appears on the glossy surface of the heart. In this paper, we propose a robust solution for the detection and removal of specular highlights. A hybrid color attributes and wavelet based edge projection approach is applied to accurately identify the affected regions. These regions are then recovered using a dynamic search-based inpainting with adaptive windowing. Experimental results demonstrate the precision and efficiency of the proposed method. Moreover, it has a real-time performance and can be generalized to various other applications.

JTD Keywords: Heart, Image color analysis, Image edge detection, Surgery, Tracking, Wavelet transforms

Cardiorespiratory Phase Synchronization (CRPS) is a manifestation of coupling between cardiac and respiratory systems complementary to Respiratory Sinus Arrhythmia. In this work, we investigated CRPS during wake and sleep stages in Polysomnographic (PSG) recordings of 30 subjects suspected from Obstructive Sleep Apnea (OSA). The population was classified into three severity groups according to the Apnea Hypopnea Index (AHI): G1 (AHI<;15), G2 (15<;=AHI<;30) and G3 (AHI>30). The synchrogram between single lead ECG and respiratory abdominal band signals from PSG was computed with the Hilbert transform technique. The different phase locking ratios (PLR) m:n were monitored throughout the night. Ratio 4:1 was the most frequent and it became more dominant as OSA severity increased. CRPS was characterized by the percentage of synchronized time (%Sync) and the average duration of synchronized epochs (AvDurSync) using three different thresholds. Globally, we observed that %Sync significantly decreased and AvDurSync slightly increased with OSA severity. A high synchronization threshold enhanced these population differences. %Sync was significantly higher in NREM than in REM sleep in G2 and G3 groups. Population differences observed during sleep did not translate to the initial wake state. Reduced CRPS could be an early marker of OSA severity during sleep, but further studies are needed to determine whether CRPS is also present during wakefulness.

JTD Keywords: Band-pass filters, Electrocardiography, Heart beat, Sleep apnea, Sociology, Statistics, Synchronization

The lack of physiological motion compensation is a major problem in robotic-assisted cardiac surgery. Since the heart is beating while the surgeon carried out the procedure, dexterity of the surgeon’s and precision are compromised. Due to the operative space and the visibility of the surgical field are reduced, the most practical solution is the use of computer vision techniques. The lack of efficiency and robustness of the existing proposals make physiological motion compensation to be considered an open problem. In this work a novel solution to solve this problem based on the minimization of an energy functional is presented. It is described in the three-dimensional space using the l1−regularized optimization class in which cubic b-splines are used to represent the changes produced on the heart surface. Moreover, the logarithmic barrier function is applied to create an approximation of the total energy in order to avoid its non-differentiability. According to the results, this proposal is able to deal with complex deformations, requires a short computational time and gives a small error.

JTD Keywords: Beating heart surgery, Image analysis, Motion compensation

Infarcted hearts are macroscopically stiffer than healthy organs. Nevertheless, although cell behavior is mediated by the physical features of the cell niche, the intrinsic micromechanical properties of healthy and infarcted heart extracellular matrix (ECM) remain poorly characterized. Using atomic force microscopy, we studied ECM micromechanics of different histological regions of the left ventricle wall of healthy and infarcted mice. Hearts excised from healthy (n = 8) and infarcted mice (n = 8) were decellularized with sodium dodecyl sulfate and cut into 12 μm thick slices. Healthy ventricular ECM revealed marked mechanical heterogeneity across histological regions of the ventricular wall with the effective Young's modulus ranging from 30.2 ± 2.8 to 74.5 ± 8.7 kPa in collagen- and elastin-rich regions of the myocardium, respectively. Infarcted ECM showed a predominant collagen composition and was 3-fold stiffer than collagen-rich regions of the healthy myocardium. ECM of both healthy and infarcted hearts exhibited a solid-like viscoelastic behavior that conforms to two power-law rheology. Knowledge of intrinsic micromechanical properties of the ECM at the length scale at which cells sense their environment will provide further insight into the cell-scaffold interplay in healthy and infarcted hearts.

JTD Keywords: Atomic force microscopy, Extracellular matrix, Heart scaffold, Nanoindentation, Viscoelasticity

Compensating physiological motion in the context of minimally invasive cardiac surgery has become an attractive issue since it outperforms traditional cardiac procedures offering remarkable benefits. Owing to space restrictions, computer vision techniques have proven to be the most practical and suitable solution. However, the lack of robustness and efficiency of existing methods make physiological motion compensation an open and challenging problem. This work focusses on increasing robustness and efficiency via exploration of the classes of l1- and l2-regularized optimization, emphasizing the use of explicit regularization. Both approaches are based on natural features of the heart using intensity information. Results pointed out the l1-regularized optimization class as the best since it offered the shortest computational cost, the smallest average error and it proved to work even under complex deformations.

JTD Keywords: Motion Compensation, Optimization, Regularization, Beating Heart Surgery, Ill-posed problem

Heart motion compensation is a challenging problem within medical robotics and it is still considered an open research area due to the lack of robustness. As it can be formulated as an energy minimization problem, an optimization technique is needed. The selection of an adequate method has a significant impact over the global solution. For this reason, a new methodology is presented here for solving heart motion compensation in which the central topic is oriented to increase robustness with the goal of achieving a balance between efficiency and efficacy. Particularly, genetic algorithms are used as optimization technique since they can be adapted to any real application, complex and oriented to work in real-time problems.

JTD Keywords: Genetic Algorithms, Deformation, Stochastic Optimization, Beating Heart Surgery, Robotic Assisted Surgery

Assessment of the dynamic interactions between cardiovascular signals can provide valuable information that improves the understanding of cardiovascular control. Heart rate variability (HRV) analysis is known to provide information about the autonomic heart rate modulation mechanism. Using the HRV signal, we aimed to obtain parameters for classifying patients with and without chronic heart failure (CHF), and with periodic breathing (PB), non-periodic breathing (nPB), and Cheyne-Stokes respiration (CSR) patterns. An electrocardiogram (ECG) and a respiratory flow signal were recorded in 36 elderly patients: 18 patients with CHF and 18 patients without CHF. According to the clinical criteria, the patients were classified into the follow groups: 19 patients with nPB pattern, 7 with PB pattern, 4 with Cheyne-Stokes respiration (CSR), and 6 non-classified patients (problems with respiratory signal). From the HRV signal, parameters in the time and frequency domain were calculated. Frequency domain parameters were the most discriminant in comparisons of patients with and without CHF: P_Tot (p = 0.02), P_LF (p = 0.022) and fp_HF (p = 0.021). For the comparison of the nPB vs. CSR patients groups, the best parameters were RMSSD (p = 0.028) and SDSD (p = 0.028). Therefore, the parameters appear to be suitable for enhanced diagnosis of decompensated CHF patients and the possibility of developed periodic breathing and a CSR pattern.

JTD Keywords: cardiovascular system, diseases, electrocardiography, frequency-domain analysis, geriatrics, medical signal processing, patient diagnosis, pneumodynamics, signal classification, Cheyne-Stokes respiration patterns, ECG, autonomic heart rate modulation mechanism, cardiovascular control, cardiovascular signals, chronic heart failure, decompensated CHF patients, dynamic interaction assessment, elderly patients, electrocardiogram, enhanced diagnosis, frequency domain parameters, heart rate variability analysis, patient classification, periodic breathing, respiratory flow signal recording, Electrocardiography, Frequency modulation, Frequency-domain analysis, Heart rate variability, Senior citizens, Standards

This work investigates the very low frequency (VLF) modulation of QRS slopes and heart rate variability (HRV). Electrocardiogram (ECG) and respiratory flow signal were acquired from patients with dilated cardiomyopathy and ischemic cardiomyopathy. HRV as well as the upward QRS slope (I_US) and downward QRS slope (I_DS) were extracted from the ECG. The relation between HRV and QRS slopes in the VLF band was measured using ordinary coherence in 5-minute segments. Partial coherence was then used to remove the influence that respiration simultaneously exerts on HRV and QRS slopes. A statistical threshold was determined, below which coherence values were considered not to represent a linear relation. 7 out of 276 segments belonging to 5 out of 29 patients for I_US and 10 segments belonging to 5 patients for I_DS presented a VLF modulation in QRS slopes, HRV and respiration. In these segments spectral coherence was statistically significant, while partial coherence decreased, indicating that the coupling HRV and QRS slopes was related to respiration. 4 segments had a partial coherence value below the threshold for I_US, 3 segments for I_DS. The rest of the segments also presented a notable decrease in partial coherence, but still above the threshold, which means that other non-linearly effects may also affect this modulation.

JTD Keywords: diseases, electrocardiography, feature extraction, medical signal processing, pneumodynamics, statistical analysis, ECG, QRS slopes, cardiomyopathy patients, dilated cardiomyopathy, electrocardiogram, feature extraction, heart rate variability, ischemic cardiomyopathy, ordinary coherence, partial coherence value, respiration, respiratory flow signal acquisition, spectral coherence, statistical threshold, time 5 min, very low frequency modulation, Coherence, Educational institutions, Electrocardiography, Frequency modulation, Heart rate variability

Rat model of Obstructive Sleep Apnea (OSA) is a realistic approach for studying physiological mechanisms involved in sleep. Rats are usually anesthetized and autonomic nervous system (ANS) could be blocked. This study aimed to assess the effect of anesthesia on ANS activity during OSA episodes. Seven male Sprague-Dawley rats were anesthetized intraperitoneally with urethane (1g/kg). The experiments were conducted applying airway obstructions, simulating 15s-apnea episodes for 15 minutes. Five signals were acquired: respiratory pressure and flow, SaO2, ECG and photoplethysmography (PPG). In total, 210 apnea episodes were studied. Normalized power spectrum of Pulse Rate Variability (PRV) was analyzed in the Low Frequency (LF) and High Frequency (HF) bands, for each episode in consecutive 15s intervals (before, during and after the apnea). All episodes showed changes in respiratory flow and SaO₂ signal. Conversely, decreases in the amplitude fluctuations of PPG (DAP) were not observed. Normalized LF presented extremely low values during breathing (median=7,67%), suggesting inhibition of sympathetic system due to anesthetic effect. Subtle increases of LF were observed during apnea. HRV and PPG analysis during apnea could be an indirect tool to assess the effect and deep of anesthesia.

JTD Keywords: electrocardiography, fluctuations, medical disorders, medical signal detection, medical signal processing, neurophysiology, photoplethysmography, pneumodynamics, sleep, ECG, SaO₂ flow, SaO₂ signal, airway obstructions, amplitude fluctuations, anesthesia effects, anesthetized nervous system, autonomic evaluation, autonomic nervous system, breathing, heart rate variability, high-frequency bands, low-frequency bands, male Sprague-Dawley rats, normalized power spectrum, obstructive sleep apnea, photoplethysmography, physiological mechanisms, pulse rate variability, rat model, respiratory flow, respiratory pressure, signal acquisition, sympathetic system inhibition, time 15 min, time 15 s, Abstracts, Atmospheric modeling, Computational modeling, Electrocardiography, Rats, Resonant frequency

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

JTD Keywords: cardiovascular system, diseases, feature extraction, geriatrics, medical signal processing, oscillations, pneumodynamics, signal classification, time-frequency analysis, Cheyne-Stokes respiration, apnea, cardiac systems, chronic heart failure, classification problems, discriminant band, diseases, elderly patients, feature selection process, frequency variability, interquartile ranges, linear discriminant analysis, nonperiodic breathing, oscillatory breathing pattern, periodic breathing, respiratory How signal, respiratory systems, signal segmentation, time 5 min, time-frequency parameters, Accuracy, Aging, Frequency modulation, Heart, Senior citizens, Time-frequency analysis

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1_st and 2_nd ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO₂ and periodic breathing cycles significantly increased with acclimatization (p-value <; 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO₂, through a significant negative correlation (p-value <; 0.01). Higher Pm is observed at climbing periods visually labeled as PB with >; 5 periodic breathing cycles through a significant positive correlation (p-value <; 0.01). Our data demonstrate that quantification of the respiratory volum- signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

JTD Keywords: Frequency domain analysis, Frequency modulation, Heart, Sleep apnea, Ventilation, Visualization, Cardiology, Medical disorders, Medical signal processing, Plethysmography, Pneumodynamics, Sensitivity analysis, Sleep, Spectral analysis, Cheyne-Stokes respiration, Climbing periods, Dataset, Heart failure patients, High altitude PB, High altitude periodic breathing, Hypobaric hypoxia, Linear discriminant analysis, Pathophysiologic aspects, Physical activity, Physiologic mechanisms, Power spectral density, Receiver operating characteristic curve, Respiratory control, Respiratory frequency, Respiratory inductive plethysmography, Respiratory pattern, Respiratory volume signal, Sleep apnea, Spectral analysis, Spectral parameters

This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.

JTD Keywords: Chronic heart failure, AR modeling, Respiratory pattern, Discriminant band, Periodic and nonperiodic breathing

A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropy-based spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.

JTD Keywords: Autoregressive (AR) modeling, Chronic heart failure (CHF), Correntropy spectral density (CSD), Linear classification, Periodic breathing (PB)

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

JTD Keywords: Practical, Theoretical or Mathematical, Experimental/cardiology diseases, Fourier transforms, Medical signal processing, Pattern classification, Pneumodynamics, Spectral analysis, Statistical analysis, Stochastic processes/ correntropy based nonlinearity test, Chronic heart failure, Correntropy function, Respiratory pattern nonlinearities, CHF patients, Nonperiodic breathing pattern, Dataset statistical distribution, Dataset temporal structure, Nonlinear information, Null hypothesis, Gaussian linear stochastic process, Statistical test, Correntropy spectral density, Iteratively refined amplitude adjusted Fourier transform, Surrogate data, Periodic breathing pattern