Heart Rate Signals Research Articles

Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar.

Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar's coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation.

Analysis of Physiological Signals to Detect the Symptoms of Paralysis Disorder using Neural Network Approach: a Cohort Study

As per World Medical Associations, Paralysis attack is one of the leading causes of disorder in the human subjects after other chronic disorders such as Cancer and Heart Attack. Due to the attack of Paralysis, the body parts of human body become un responsive due to which the person may be physically disabled from hands or legs-based locomotion. There is scarcity in the methodologies who can early indicate the symptoms and alert the medical practitioners or human subject under surveillance. In this study, the health-related signals were studied and as per consultation of medical experts, total eight key parameters were identified and then based on that the output decision that weather person has symptoms of paralysis or not. The observatory data base has been prepared from more than 40,000 human subjects for the duration January 2018 to December 2023. As per categorical analysis based on training, validation and testing of three-layer Recurrent Neural Network, it has been observed that the ECG, Blood pressure, Heart Rate and PPG signals are key indicators to judge the susceptance of Paralysis disorder in the human subjects. Based on the evaluation parameters of machine learning models, the conjunction of response in association with selected input parameters gives 94% accuracy than other models. Our study helps research community to develop such methodologies by which the early indication of such disorders can be helpful to human health.

Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos

Heart rate (HR) is an important indicator reflecting the overall physical and mental health of the human body, playing a crucial role in diagnosing cardiovascular and neurological diseases. Recent research has revealed that variations in the light absorption of human skin captured through facial video over the cardiac cycle, due to changes in blood volume, can be utilized for non-contact HR estimation. However, most existing methods rely on single-modal video sources (such as RGB or NIR), which often yield suboptimal results due to noise and the limitations of a single information source. To overcome these challenges, this paper proposes a multimodal information fusion architecture named the spatiotemporal sensitive network (SS-Net) for non-contact heart rate estimation. Firstly, spatiotemporal feature maps are utilized to extract physiological signals from RGB and NIR videos effectively. Next, a spatiotemporal sensitive (SS) module is introduced to extract useful physiological signal information from both RGB and NIR spatiotemporal maps. Finally, a multi-level spatiotemporal context fusion (MLSC) module is designed to fuse and complement information between the visible light and infrared modalities. Then, different levels of fused features are refined in task-specific branches to predict both remote photoplethysmography (rPPG) signals and heart rate (HR) signals. Experiments conducted on three datasets demonstrate that the proposed SS-Net achieves superior performance compared to existing methods.

Deep Learning Approach for Fetal Health Prediction

Monitoring the foetus's health is crucial during pregnancy to avoid complications that may worsen the course of pregnancy and delivery. Cardiotocography (CTG) is a tool that provides complex information by monitoring the foetus's heart rate signal. Obstetricians visually interpret these signals to predict potential risks and draw clinical inferences. The interpretation, however, relies on the expertise of the obstetrician, leading to a significant false positive rate. Thus, the study uses deep learning techniques to effectively identify foetal health states as 'Normal', 'Suspect' and 'Pathological'. The dataset used is CTG data drawn from the Kaggle repository. The optimal subset of features is obtained by comparing traditional feature selection and meta-heuristic-based feature selection techniques. Deep learning algorithms, namely, Convolution Neural Networks, Artificial Neural Networks, and Radial Basis Function Networks, are applied to train multi-class classification models that predict foetal health status. The models are then evaluated using performance metrics.

Design a Simple Portable Electrocardiograph Based on the IOT Concept

Abstract There are many cases of heart disease with death associated. The symptoms are not always clearly visible and require examination using an electrocardiogram (ECG) device to be detected. Recording human electrical activity can be done in various organs of the human body, one of which is in the heart. An ECG is a medical test to detect electrical activity signals produced by the heart by exchanging the signals on a monitor or graph on paper. This study aimed to design, build, test, and analyze a portable ECG device that is compact and easy to use. This portable electrocardiogram helps monitor human heart rate to check heart rate periodically. Tool design is done by making tools equipped with a monitoring system. The proposed system uses the AD8232 sensor to detect a person’s cardiac activity. Arduino Nano is a microcontroller connected to ESP32 with a WiFi feature. Heart activity monitors can be done anywhere by applying the Internet of Things concept, namely with the Ubidots application. A portable tool using Li-Ion batteries as a tool voltage source and BMS 3S as a charger, this tool is also equipped with a 3.2-inch TFT screen as a graph viewer of the results of monitoring heart activity. The results showed that the system could monitor the user’s heart for 5 minutes when sitting, walking, and running. The system created has been able to send heart signal data to be processed by Arduino Nano and ESP32 as a data transfer tool so that the data generated in the form of electrocardiogram graphs can be displayed on the Ubidots application. TFT LCDs can display electrocardiogram graphs generated by the electrical activity of the human heart and beats per minute values. The AD8232 sensor can measure beat per minute (bpm) values with an average increase in bpm values for walking conditions of 17% and running conditions of 19.17%. The AD8232 Sensor Performance can detect the user’s heart rate with a percentage of data difference between walking and running conditions of 2.88% with an accuracy rate of 97.12%.

Lipopolysaccharide Induces Trained Innate Immune Tolerance in the Heart Through Interferon Signaling in a Model of Stress-Induced Cardiomyopathy.

Although the ability of the heart to adapt to environmental stress has been studied extensively, the molecular and cellular mechanisms responsible for cardioprotection are not yet fully understood. We administered Toll-like receptor (TLR) agonists or a diluent to wild-type mice and assessed their potential to induce cardiac protection against injury from a high intraperitoneal dose of isoproterenol (ISO) administered 7 days later. Cardioprotective effects were analyzed through serum cardiac troponin I levels, immune cell profiling via flow cytometry, echocardiography, and multiomic single-nuclei RNA and ATAC sequencing. Pretreatment with the TLR4 agonist lipopolysaccharide (LPS), but not TLR1/2 or TLR3 agonists, conferred cardioprotection against ISO, as demonstrated by reduced cardiac troponin I leakage, decreased inflammation, preservation of cardiac structure and function, and improved survival. Remarkably, LPS-induced tolerance was reversed by β-glucan treatment. Multiomic analysis showed that LPS-tolerized hearts had greater chromatin accessibility and upregulated gene expression compared to hearts treated with LPS and β-glucan (reverse-tolerized). The LPS tolerance was associated with upregulation of interferon response pathways across various cell types, including cardiac myocytes and stromal cells. Blocking both type 1 and type 2 interferon signaling eliminated LPS-induced tolerance against ISO, while pretreatment with recombinant type 1 and 2 interferons conferred cardiac protection. Multiomic sequencing further revealed enhanced cytoprotective signaling in interferon-treated hearts. Analysis of cell-cell communication networks indicated increased autocrine signaling by cardiac myocytes, as well as greater paracrine signaling between stromal cells and myeloid cells, in LPS-tolerized versus reverse-tolerized hearts. LPS pretreatment confers cardiac protection against ISO-induced injury through TLR4 mediated type 1 and 2 interferon signaling, consistent with trained innate immune tolerance. The observation that LPS-induced protection in cardiac myocytes involves both cell-autonomous and non-cell-autonomous mechanisms underscores the complexity of innate immune tolerance in the heart, warranting further investigation into this cardioprotective phenotype. What is new?: The Toll-like receptor 4 (TLR4) agonist lipopolysaccharide (LPS) confers cardiac protection against isoproterenol-mediated injury in a manner consistent with trained innate immune tolerance, which is reversed by β-glucan treatment.Activation of type 1 and 2 interferon signaling, which is downstream of Toll-like receptor 4, is necessary and sufficient for LPS-induced cardiac protection.LPS-tolerized hearts show heightened autocrine signaling by cardiac myocytes and, to a greater degree, increased cell-cell communication between cardiac myocytes and stromal and myeloid cells compared to reverse-tolerized hearts.What are the clinical implications?: TLR4 and interferon signaling play key roles in the establishment of cardiac protection and LPS-induced trained innate immune tolerance.The protective effects of LPS are mediated by cell-autonomous and non-cell-autonomous mechanisms, suggesting that a deeper understanding of the molecular and cellular signatures of innate immune tolerance is required for the development of targeted approaches to modulate trained innate immunity, and consequently cytoprotection, in the heart.

MG53's non-physiologic interaction with insulin receptor: lack of effect on insulin-stimulated Akt phosphorylation in muscle, heart and liver tissues.

MG53's known function in facilitating tissue repair and anti-inflammation has broad applications to regenerative medicine. There is controversy regarding MG53's role in the development of type 2 diabetes mellitus. This study aims to address this controversy - whether MG53's myokine function contributes to inhibition of insulin signaling in muscle, heart, and liver tissues. We determined the binding affinity of the recombinant human MG53 (rhMG53) to the insulin receptor extracellular domain (IR-ECD) and found low affinity of interaction with Kd (>480 nM). Using cultured C2C12 myotubes and HepG2 cells, we found no effect of rhMG53 on insulin-stimulated Akt phosphorylation (p-Akt). We performed in vivo assay with C57BL/6J mice subjected to insulin stimulation (1 U/kg, intraperitoneal injection) and observed no effect of rhMG53 on insulin-stimulated p-Akt in muscle, heart and liver tissues. Overall, our data suggest that rhMG53 can bind to the IR-ECD, however has a low likelihood of a physiologic role, as the Kd for binding is ~10,000 higher than the physiologic level of MG53 present in the serum of rodents and humans (~10 pM). Our findings question the notion proposed by Xiao and colleagues - whether targeting circulating MG53 opens a new therapeutic avenue for type 2 diabetes mellitus and its complications.

Mitogen-activated protein kinase signalling in rat hearts during postnatal development: MAPKs, MAP3Ks, MAP4Ks and DUSPs

Mammalian cardiomyocytes become terminally-differentiated during the perinatal period. In rodents, cytokinesis ceases after a final division cycle immediately after birth. Nuclear division continues and most cardiomyocytes become binucleated by ∼11 days. Subsequent growth results from an increase in cardiomyocyte size. The mechanisms involved remain under investigation. Mitogen-activated protein kinases (MAPKs) regulate cell growth/death: extracellular signal-regulated kinases 1/2 (ERK1/2) promote proliferation, whilst c-Jun N-terminal kinases (JNKs) and p38-MAPKs respond to cellular stresses. We assessed their regulation in rat hearts during postnatal development (2, 7, 14, and 28 days, 12 weeks) during which time there was rapid, substantial downregulation of mitosis/cytokinesis genes (Cenpa/e/f, Aurkb, Anln, Cdca8, Orc6) with lesser downregulation of DNA replication genes (Orcs1–5, Mcms2–7). MAPK activation was assessed by immunoblotting for total and phosphorylated (activated) kinases. Total ERK1/2 was downregulated, but not JNKs or p38-MAPKs, whilst phosphorylation of all MAPKs increased relative to total protein albeit transiently for JNKs. These profiles differed from activation of Akt (also involved in cardiomyocyte growth). Dual-specificity phosphatases, upstream MAPK kinase kinases (MAP3Ks), and MAP3K kinases (MAP4Ks) identified in neonatal rat cardiomyocytes by RNASeq were differentially regulated during postnatal cardiac development. The MAP3Ks that we could assess by immunoblotting (RAF kinases and Map3k3) showed greater downregulation of the protein than mRNA. MAP3K2/MAP3K3/MAP4K5 were upregulated in human failing heart samples and may be part of the “foetal gene programme” of re-expressed genes in disease. Thus, MAPKs, along with kinases and phosphatases that regulate them, potentially play a significant role in postnatal remodelling of the heart.

Sacubitril/valsartan alleviates myocardial infarction-induced inflammation in mice by promoting M2 macrophage polarisation via regulation of PI3K/Akt pathway

Background Macrophage polarisation-mediated inflammation plays a critical role in ventricular remodelling after myocardial infarction (MI). Sacubitril/Valsartan (Sac/Val) is an angiotensin receptor-neprilysin inhibitor that has shown beneficial effects on MI and heart failure. This study aims to further explore the mechanisms by which Sac/Val exerts its protective effects against MI. Methods A mouse MI model was induced by ligating the left anterior descending coronary artery, followed by Sac/Val administration. TTC staining and Masson trichrome staining were employed for estimating myocardial infarct size and fibrosis, respectively. The expression levels of proinflammatory factors were determined by ELISA and RT-qPCR. Flow cytometry and immunofluorescence staining were implemented to detect CD206-positive cell infiltration in mouse hearts. Western blotting was conducted to assess protein levels of Arg1, pro-fibrotic factors, and PI3K/Akt signalling-related markers. Results Sac/Val treatment reduced myocardial infarct size and fibrosis in mice after MI. Sac/Val administration decreased proinflammatory cytokine production and facilitated M2 macrophage polarisation in MI mouse cardiac tissues. Sac/Val activated PI3K/Akt signalling in MI mouse hearts. Blocking PI3K/Akt signalling counteracted Sac/Val-mediated protective effects in MI mice. Conclusion Sac/Val ameliorates MI-induced inflammation by facilitating M2 macrophage polarisation and activating PI3K/Akt signalling.

Enhancing heart disease diagnosis through ECG image vectorization-based classification

Heart disease is a major issue, and the severity of its effects can be reduced through early detection and prevention. ECG is an effective diagnostic tool. Automating ECG analysis increases the possibility of timely analysis and prediction of heart conditions, improving patient outcomes. The extraction of heart-related features further enhances the accuracy of ECG-based classification models, paving the way for more effective and efficient online detection and prevention of heart diseases.The image-vectorization technique suggested in this study produces a vector representation that precisely captures the distinctive features of the heart signal. It involves image cropping, erasing the ECG grid lines, and assigning pixels to distinguish the heart signal from the background. Compared to the feature vector produced by VGG16, the extracted feature vector is 589 times shorter than the feature vector produced by VGG16, which significantly decreased the amount of memory required, increased algorithm convergence, and required less computing power. The feature vector extracted using image-vectorization is used to create the training dataset, which is used to train artificial neural networks (ANNs). The results demonstrate that using image-vectorization techniques improved the performance of machine learning algorithms compared to using conventional feature extraction algorithms like CNNs and VGG16.

Dynamics of gas exchange and heart rate signal entropy in standard cardiopulmonary exercise testing during critical periods of growth and development.

Standard cardiopulmonary exercise testing (CPET) produces a rich dataset but its current analysis is often limited to a few derived variables such as maximal or peak oxygen uptake (V̇O2). We tested whether breath-by-breath CPET data could be used to determine sample entropy (SampEn) in 81 healthy children and adolescents (age 7-18 years old, equal sex distribution). To overcome challenges of the relatively small time-series CPET data size and its nonstationarity, we developed a Python algorithm for short-duration physiological signals. Comparing pre- and post-ventilatory threshold (VT1) CPET phases, we found: (1) SampEn decreased by 9.46% for V̇O2 and 5.01% for V̇CO2 (p < 0.05), in the younger, early-pubertal participants; and (2) HR SampEn fell substantially by 70.8% in the younger and 77.5% in the older participants (p < 0.001). Across all ages, females exhibited greater HR SampEn than males during both pre- and post VT1 CPET phases by 14.10% and 23.79%, respectively, p < 0.01. In females, late-pubertal had 17.6% lower HR SampEn compared to early-pubertal participants (p < 0.05). Breath-by-breath gas exchange and HR data from CPET are amenable to SampEn analysis that leads to novel insight into physiological responses to work intensity, and sex and maturational effects.

Dynamic upregulation of retinoic acid signal in the early postnatal murine heart promotes cardiomyocyte cell cycle exit and maturation

The adult mammalian heart has extremely limited cardiac regenerative capacity. Most cardiomyocytes live in a state of permanent cell-cycle arrest and are unable to re-enter the cycle. Cardiomyocytes switch from cell proliferation to a maturation state during neonatal development. Although several signaling pathways are involved in this transition, the molecular mechanisms by which these inputs coordinately regulate cardiomyocyte maturation are not fully understood. Retinoic acid (RA) plays a pivotal role in development, morphogenesis, and regeneration. Despite the importance of RA signaling in embryo heart development, little is known about its function in the early postnatal period. We found that mRNA expression of aldehyde dehydrogenase 1 family member A2 (Aldh1a2), which encodes the key enzyme for synthesizing all-trans retinoic acid (ATRA) and is an important regulator for RA signaling, was transiently upregulated in neonatal mouse ventricles. Single-cell transcriptome analysis and immunohistochemistry revealed that Aldh1a2 expression was enriched in cardiac fibroblasts during the early postnatal period. Administration of ATRA inhibited cardiomyocyte proliferation in cultured neonatal rat cardiomyocytes and human cardiomyocytes. RNA-seq analysis indicated that cell proliferation-related genes were downregulated in prenatal rat ventricular cardiomyocytes treated with ATRA, while cardiomyocyte maturation-related genes were upregulated. These findings suggest that RA signaling derived from cardiac fibroblasts is one of the key regulators of cardiomyocyte proliferation and maturation during neonatal heart development.

An intelligent garment for long COVID-19 real-time monitoring

As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and supervised learning-based medical image analysis have proven to be useful. Current research on these two technical roadmaps has various drawbacks, despite their respective benefits. Wearable systems allow only the real-time monitoring of physiological parameters (heart rate, temperature, blood oxygen saturation, or SpO2). Therefore, they are unable to conduct in-depth investigations or differentiate COVID-19 from other illnesses that share similar symptoms. Medical image analysis using supervised learning-based models can be used to conduct in-depth analyses and provide precise diagnostic decision support. However, these methods are rarely used for real-time monitoring. In this regard, we present an intelligent garment combining the precision of supervised learning-based models with real-time monitoring capabilities of wearable systems. Given the relevance of electrocardiogram (ECG) signals to long COVID-19 symptom severity, an explainable data fusion strategy based on multiple machine learning models uses heart rate, temperature, SpO2, and ECG signal analysis to accurately assess the patient's health status. Experiments show that the proposed intelligent garment achieves an accuracy of 97.5 %, outperforming most of the existing wearable systems. Furthermore, it was confirmed that the two physiological indicators most significantly affected by the presence of long COVID-19 were SpO2 and the ST intervals of ECG signals.

A deep neural network approach to heart murmur detection using spectrogram and peak interval features

Congenital heart disease affects about 1% of newborns, posing risks like heart failure and mortality. Developing countries often lack resources for diagnosis and treatment. The George B. Moody PhysioNet Challenge 2022 aims to develop systems for detecting murmurs and clinical outcome events using phonocardiogram (PCG) data, offering a cost-effective method for diagnosing cardiac diseases without invasive procedures. We proposed a deep learning model for this task and achieved the 5th rank out of 40 teams in both Track 1 (murmur detection) and Track 2 (clinical outcome prediction). This paper describes our methods and additional experiments. To extract features from the PCG, we employed techniques including Constant Q Transform (CQT) and Mel-scaled spectrogram (Mel-spectrogram) to generate two-dimensional representations in the frequency–time domain. Additionally, we extracted the Peak Interval (PI) feature, which measures the distance between peaks in the PCG data. This feature is useful because the peak intervals should be shorter in PCG recordings with murmurs. We also considered the sequence and mean of PI as additional features. Our proposed system, titled ‘Phonocardiogram-based Heart murmur Detection using Spectrogram and PI features (SpectroHeart),’ employs the Mel-spectrogram and PI features to detect heart murmurs and assess clinical outcomes. We believe that our deep learning-based system has great potential for automatically detecting heart signals from PCG.

Detection of Arrhythmias Using Heart Rate Signals from Smartwatches

According to the World Health Organization (WHO), cardiovascular illnesses, including arrhythmia, are the primary cause of mortality globally, responsible for over 31% of all fatalities each year. To reduce mortality, early and precise diagnosis is essential. Although the analysis of electrocardiograms (ECGs) is the primary means of detecting arrhythmias, it depends significantly on the expertise and subjectivity of the health professional reading and interpreting the ECG, and errors may occur in detection. Artificial intelligence provides tools, techniques, and models that can support health professionals in detecting arrhythmias. However, these tools are based only on ECG data, of which the process of obtaining is an invasive, high-cost method requiring specialized equipment and personnel. Smartwatches feature sensors that can record real-time signals indicating the heart’s behavior, such as ECG signals and heart rate. Using this approach, we propose a machine learning- and deep learning-based approach for detecting arrhythmias using heart rate data obtained with smartwatches. Heart rate data were collected from 252 patients with and without arrhythmias who attended a clinic in Arequipa, Peru. Heart rates were also collected from 25 patients who wore smartwatches. Ten machine learning algorithms were implemented to generate the most effective arrhythmia recognition model, with the decision tree algorithm being the most suitable. The results were analyzed using accuracy, sensitivity, and specificity metrics. Using Holter data yielded values of 93.2%, 91.89%, and 94.59%, respectively. Using smartwatch data yielded values of 70.83%, 91.67%, and 50%, respectively. These results indicate that our model can effectively recognize arrhythmias from heart rate data. The high sensitivity score suggests that our model adequately recognizes true positives; that is, patients with arrhythmia. Likewise, its specificity suggests an adequate recognition of false positives.

Influences of heart rate feedback and autistic traits on affective mindreading

Although mindreading is an important prerequisite for successful social interactions, the underlying mechanisms are still matter of debate. It is unclear, for example, if inferring others’ and own mental states are distinct processes or are based on a common mechanism. Using an affect-induction experimental set-up with an acoustic heart rate feedback that addresses affective mindreading in self and others, we investigated if non-autistic study participants relied on similar information for self- and other-directed mindreading. We assumed that due to altered mindreading capacities in autism, mainly individuals with low autistic traits would focus on additional sensory cues, such as heart rate, to infer their own and their gambling partner’s affective states. Our analyses showed that the interpretation of a heart rate signal differed in self- and other-directed mindreading trials. This effect was modulated by autistic traits suggesting that individuals with higher autistic traits might not have interpreted the heart rate feedback for gambling partner ratings and differentiated less between self- and other-directed mindreading trials. We discuss these results in the context of a common mechanism underlying self- and other-directed mindreading and hypothesize that the weighting of internal and external sensory information might contribute to how we make sense of our and others’ mental states.

Abstract Tu120: Sarm1 promotes diabetic cardiomyopathy by regulating HIF signaling, lipotoxicity and mitochondrial dysfunction

Heart failure occurs at twice the rate in diabetic patients as compared to normal patients, and reduced NAD levels are associated with diabetic cardiomyopathy. Sterile Alpha and Tir Motif Containing 1 (SARM1) is an intracellular NAD hydrolase. We hypothesized that SARM1 promotes NAD decline and diabetic cardiomyopathy. We subjected wild type (WT) and global SARM1 knockout mice to chronic diabetic stress induced by streptozotocin injections. We showed that 16-week diabetic stress promoted progressive decline in systolic and diastolic function, and in NAD levels, which were ameliorated by SARM1 deficiency despite similar plasma glucose and lipid levels. Gene Module Network Analysis of cardiac transcriptomics identified upregulation of genes in fatty acid and glucose metabolic processes, and suppression of genes in mitochondrial processes in diabetic WT hearts. SARM1 deficiency reversed upregulated fatty acid metabolic genes but had no impact on glucose metabolic and mitochondrial gene expressions in diabetic hearts. By transmission electron microscopy and Oil Red O staining, we observed increases in lipid droplet sizes and lipid accumulation in diabetic WT hearts, which were suppressed by SARM1 deficiency. SARM1 deficiency did not reverse mitochondrial gene expression, but it mitigated the reduction in ADP-driven respiration, mitochondrial fragmentation, and abnormal cristae structure in diabetic WT hearts. Diabetic WT hearts had increased phospho-PDH and PDK4 levels as well as activated HIF signaling, exhibiting an increase in expression of HIF-1a target genes (55 genes) , many of which were related to lipid storage. SARM1 deficiency suppressed expression of HIF1-a target genes (53 genes) , and reduced phospho-PDH and PDK4 levels. These data suggest that SARM1 regulates pseudohypoxic HIF-dependent signaling in diabetic hearts. In summary, our current data support the hypothesis that SARM1 regulates HIF-dependent transcription events, particularly those influencing lipid metabolism of diabetic hearts. However, SARM1 promotes mitochondrial dysfunction in diabetic hearts independent of transcription of mitochondrial genes. Further studies will continue to elucidate the role of SARM1 in diabetic cardiomyopathy, and its potential as a novel therapeutic target.

Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals Related to E-Cigarette Use

Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals Related to E-Cigarette Use

A wavelet subband based LSTM model for 12-lead ECG synthesis from reduced lead set.

Synthesis of a 12-lead electrocardiogram from a reduced lead set has previously been extensively studied in order to meet patient comfort, minimise complexity, and enable telemonitoring. Traditional methods relied solely on the inter-lead correlation between the standard twelve leads for learning the models. The 12-lead ECG possesses not only inter-lead correlation but also intra-lead correlation. Learning a model that can exploit this spatio-temporal information in the ECG could generate lead signals while preserving important diagnostic information. The proposed approach takes leverage of the enhanced inter-lead correlation of the ECG signal in the wavelet domain. Long-short-term memory (LSTM) networks, which have emerged as a powerful tool for sequential data mining, are a type of recurrent neural network architecture with an inherent capability to capture the spatiotemporal information of the heart signal. This work proposes the deep learning architecture that utilizes the discrete wavelet transform and the LSTM to reconstruct a generic 12-lead ECG from a reduced lead set. The experimental results are evaluated using different diagnostic measures and similarity metrics. The proposed framework is well founded, and accurate reconstruction is possible as it can capture clinically significant features and provides a robust solution against noise.

Computer Simulation of Catheter Cryoablation for Pulmonary Vein Isolation.

Cryoablation is a well-established medical procedure for surgically treating atrial fibrillation. Cryothermal catheter therapy induces cellular necrosis by freezing the insides of pulmonary veins, with the goal of disrupting abnormal electrical heart signals. Nevertheless, tissue damage induced by cold temperatures may also lead to other complications after cardiac surgery. In this sense, the simulation of catheter ablation can provide safer environments for training and the performance of cryotherapy interventions. Therefore, in this paper, we propose a novel approach to help better understand how temperature rates can affect this procedure by using computer tools to develop a simulation framework to predict lesion size and determine optimal temperature conditions for reducing the risk of major complications. The results showed that a temperature profile of around -40 °C caused less penetration, reduced necrotic damage, and smaller lesion size in the tissue. Instead, cryotherapy close to -60 °C achieved a greater depth of temperature flow inside the tissue and a larger cross-section area of the lesion. With further development and validation, the framework could represent a cost-effective strategy for providing personalized modeling, better planning of cryocatheter-based treatment, and preventing surgical complications.