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Genetic cardiomyopathies are disorders of the cardiac muscle, most often explained by pathogenic mutations in genes encoding sarcomere, cytoskeleton, or ion channel proteins. Clinical phenotypes such as heart failure and arrhythmia are classically treated with generic drugs, but aetiology-specific and targeted treatments are lacking. As a result, cardiomyopathies still present a major burden to society, and affect many young and older patients. The Translational Committee of the Heart Failure Association (HFA) and the Working Group of Myocardial Function of the European Society of Cardiology (ESC) organized a workshop to discuss recent advances in molecular and physiological studies of various forms of cardiomyopathies. The study of cardiomyopathies has intensified after several new study setups became available, such as induced pluripotent stem cells, three-dimensional printing of cells, use of scaffolds and engineered heart tissue, with convincing human validation studies. Furthermore, our knowledge on the consequences of mutated proteins has deepened, with relevance for cellular homeostasis, protein quality control and toxicity, often specific to particular cardiomyopathies, with precise effects explaining the aberrations. This has opened up new avenues to treat cardiomyopathies, using contemporary techniques from the molecular toolbox, such as gene editing and repair using CRISPR-Cas9 techniques, antisense therapies, novel designer drugs, and RNA therapies. In this article, we discuss the connection between biology and diverse clinical presentation, as well as promising new medications and therapeutic avenues, which may be instrumental to come to precision medicine of genetic cardiomyopathies. [ABSTRACT FROM AUTHOR]

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person's beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

The article discusses the management of arrhythmia-induced cardiomyopathy (AIC), a form of cardiomyopathy characterized by left ventricular (LV) systolic dysfunction. Topics discussed include the association between AIC and heart failure (HF), the causes, clinical features and diagnosis of AIC, and the management of AIC in children and adults with congenital heart disease.

Perioperative risk assessment and stratification before craniotomy is necessary to identify and optimize modifiable risk factors. Due to the high costs of diagnostic testing and concerns for delaying surgery, some have questioned whether and when surgery delays are warranted and supported by the current body of literature. The objective of this scoping review was to evaluate the available evidence on the prognostic value of preoperative risk assessment before anesthesia for elective craniotomy. In this scoping review, we reviewed 156 papers that assess preoperative risk assessment before elective craniotomy, of which 27 papers were included in the final analysis. There is little high-quality evidence to suggest significant risk reduction when 4 common preexisting abnormalities are present: preoperative chronic aspirin therapy, cardiac arrhythmias, deep vein thrombosis, or hyperglycemia. The risk of delaying craniotomy should ultimately be weighed against the perceived risks associated the patient's comorbid conditions and should be considered on an individualized basis. [ABSTRACT FROM AUTHOR]

This document is a position paper published by the Canadian Journal of Psychiatry, providing updated curriculum guidelines for training in substance use disorders (SUDs) in psychiatry. The paper highlights the limited training in SUDs compared to other psychiatric disorders, despite the increasing prevalence of SUDs in psychiatric care settings. It emphasizes the importance of psychiatry residents developing the necessary knowledge, skills, and attitudes for managing patients with SUDs. The paper also discusses the challenges in training, such as a shortage of experienced faculty and negative attitudes towards patients with SUDs. It provides recommendations for clinical content, seminar content, and clinical rotations and entrustable professional activities (EPAs) related to SUDs. The goal is to promote uniform knowledge, skill, and attitude development for residents nationally. [Extracted from the article]

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases. [ABSTRACT FROM AUTHOR]

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most prevalent form of arrhythmia, with a rising incidence and prevalence worldwide, posing significant implications for public health. In this paper, we introduce an approach that combines the Recurrence Plot (RP) technique and the ResNet architecture to predict AF. Our method involves three main steps: using wavelet filtering to remove noise interference; generating RPs through phase space reconstruction; and employing a multi-level chained residual network for AF prediction. To validate our approach, we established a comprehensive database consisting of electrocardiogram (ECG) recordings from 1008 AF patients and 48,292 Non-AF patients, with a total of 2067 and 93,129 ECGs, respectively. The experimental results demonstrated high levels of prediction precision (90.5%), recall (89.1%), F1 score (89.8%), accuracy (93.4%), and AUC (96%) on our dataset. Moreover, when tested on a publicly available AF dataset (AFPDB), our method achieved even higher prediction precision (94.8%), recall (99.4%), F1 score (97.0%), accuracy (97.0%), and AUC (99.7%). These findings suggest that our proposed method can effectively extract subtle information from ECG signals, leading to highly accurate AF predictions. [ABSTRACT FROM AUTHOR]

Detecting cardiac abnormalities promptly is critical for preventing unexpected and premature fatalities. In this research, four types of cardiac arrhythmias such as Ventricular Tachycardia, Premature Ventricular Contraction, Normal Sinus Rhythm and Supraventricular Tachycardia are detected from the amassed Physiobank MIT-BIH cardiac arrhythmia database. Dimensionality reduction techniques like Stochastic Neighbour Embedding (SNE), Neighbourhood Preserving Embedding, Linear Local Tangent Space Alignment and Gaussian Process Latent Variable Model are used to reduce the dimension of the ECG signals. The appropriate features of dimensionally reduced ECG signals are selected by the Elephant Search Optimization (ESO) technique. Finally, classification is performed using the relevant classifiers, such as Support Vector Machine, Adaboost, Modest Adaboost based on Ridge Regression (Modest Adaboost.RR), Extreme Gradient Boost (XGboost) and Naïve Bayes (NBC) classifiers. Multiple classifiers without and with ESO feature selection for different cardiac cases have an average classification accuracy of 62.23% and 73.61%, respectively. These multiple classifiers are defined by a set of control parameters known as hyper-parameters, which must be tuned in order to achieve optimal results. Experts have developed many approaches for detecting cardiac arrhythmias, but these multiple classifiers do not always perform well when the usual parameters for machine learning classification models are employed. In this paper, various classifiers are used in conjunction with the Stochastic Gradient Descent (SGD), Particle Swarm Optimization (PSO) and Bayesian Tree-structured Parzen Estimator (BTPE) to enhance the cardiac arrhythmia classification accuracy via hyper-parameter tuning. Multiple classifiers with SGD, PSO and BTPE hyper-parameters tuning techniques for various cardiac cases have an average classification accuracy of 80.13%, 90.67% and 94.96%, respectively. The Classifier's performance is analysed based on metrics like Classification Accuracy, F1 score, Error Rate, Matthew's correlation coefficient, Jaccard Index and Cohen's Kappa Coefficient with and without ESO features selection method and hyper-parameters tuning techniques. The analysis utilizes the MATLAB R2014a software for result evaluation. The results show that the SNE-ESO approach, along with the XGboost-BTPE, achieved the highest classification accuracy of 99.89% for detecting {Ventricular Tachycardia}-{Normal Sinus Rhythm} cases. In terms of classification benchmarks, the results exhibit that the BTPE hyper-parameter tuning technique surpasses the SGD and PSO techniques. [ABSTRACT FROM AUTHOR]

Deep learning classification is commonly used to assess biomedical data. Fetal heart rate (FHR) signal data monitor the health of both the mother and the fetus and prevent mobility and death in fetuses at risk of hypoxia. But, the data imbalance is a major problem and happens frequently. To overcome this problem, a Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm (HBA) is proposed in this paper for Fetal Arrhythmia Detection and Classification from ECG signals (DSDACNN-HBA-FAC). The input ECG signals are collected from the fetal ECG dataset. Then, the ECG data are pre-processed using coherence shock filtering (CSF), which eliminates artifacts, like baseline drift, contact noise, muscle artifacts, electrode motion, electromyography artifacts. Using the improved non-subs sampled Shearlet transform (INSST) method, the pre-processed ECG data recover the mean, standard deviation, root mean square, skewness, kurtosis, zero-crossing, autoregressive coefficients, estimated entropy, and Higuchi's fractal dimension. The DSDACNN classifier utilizes the features extracted to categorize the output as normal and arrhythmia. In general, DSDACNN does not agree on any optimization techniques to define the optimum parameters and to ensure accurate classification. Therefore, HBA is employed to optimize the DSDACNN weight parameters. The proposed method is implemented in MATLAB. The performance metrics, like sensitivity, precision, recall, f-measure, specificity, accuracy, computation time, RoC, and error rate are evaluated. The performance of DSDACNN-HBA-FAC achieves 3.101%, 7.12%, 7.73% high accuracy, 51.136%, 59.04%, 44.51% lower computation Time and 2.292%, 5.365%, 1.551% greater AUC compared with the existing techniques, like Fetal Arrhythmia Detection with Classification from ECG Signals: A NonInvasive Method (ANN-FAC), Semi-supervised active transfer learning for fetal ECG arrhythmia identification (DNN-FAC), fetal arrhythmia detection using adaptive single channel electrocardiogram extraction (CNN-FAC), respectively. [ABSTRACT FROM AUTHOR]

The real-time and accurate recognition of premature ventricular contractions (PVC) in dynamic 12-lead ECGs poses a clinical challenge due to noise and variability. The accurate location of the QRS complex is crucial for efficient PVC heartbeat recognition. This study proposes a robust PVC recognition approach, combining a self-adaptive multi-detector fusion model for R-peak detection and a multi-parameter squeeze–excitation ResNet-based heartbeat classifier. The detection results of multiple detectors are weighted with coefficients, and decision fusion is performed through adaptive threshold comparison. Tested on the INCART arrhythmia and 2018 China Physiological Signal Challenge databases, the R-peak detection results exhibit that our proposed fusion model outperforms majority, mean, and median voting strategies, with sensitivity improvements of 0.33%, 0.78%, and 0.41% for INCART dataset and 0.28%, 0.61%, and 0.34% for 2018 Physiological Signal dataset. In addition, our model is also superior to the best single annotator used in this paper. Evaluation of the multi-parameter SE-ResNet classifier reveals increased F1 scores of PVC heartbeat recognition by 4.33%, 3.44%, 5.04%, 12.41%, and 1.56% using INCART dataset compared to CNN, Inception, MLP, AlexNet, and LSTM, respectively, and 2.51%, 1.8%, 2.32%, 12.54%, and 2.27% using 2018 Physiological Signal dataset. Finally, the Se and Sp metrics also show improvement on the two datasets using the SE-ResNet. [ABSTRACT FROM AUTHOR]

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person's beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy. [ABSTRACT FROM AUTHOR]

All-pass transformation (APT)-based variable digital filters (VDFs) can be used in different biomedical signal-processing applications, particularly in electrocardiograph denoising. In this paper, arithmetic optimised APT-VDF is proposed by modifying the hardware structure of APT-VDF based on arithmetic perception to enhance the performance in terms of area, speed and power consumption. The core blocks of the suggested Arithmetic Optimised APT-VDF (AOAPT-VDF)are adders and multipliers. This paper introduces a new reusable Vedic multiplier with redundant detection unit to identify redundant computations while generating the partial products. Also, a new Carry select adder (CSLA) with simplified combinational logics is proposed to improve the performance of the CSLA by simplifying the partial sum and carry generation logics without requiring Ripple Carry Adders. The proposed Reusable Vedic Multiplier with Redundant Detection Logic (RVM-RDL) also reuses the same vertical and cross-product generation units repetitively to reduce the area while increasing the bit length. The effectiveness of the suggested AOAPT is analysed utilising the MIT-BIH Arrhythmia Database. The proposed AOAPT-VDF consumes only 0.21 $$W$$ W power and achieves a 235.50 MHz operating frequency. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to −16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%. [ABSTRACT FROM AUTHOR]

Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy. [ABSTRACT FROM AUTHOR]

Dilated cardiomyopathy (DCM) is a common cause of heart failure (HF) and heart transplantation (HTx), with genetic factors playing a significant role. In recent years, the RNA-binding protein motif 20 (RBM20), which affects the gene splicing of various proteins with different cellular functions, was identified as the first DCM gene with regulatory properties. Variants of RBM20 have been associated with severe forms of DCM. The aim of this critical systematic review was to analyse RBM20 cardiomyopathy clinical features and outcomes. According to PRISMA guidelines, a search was run in the PubMed, Scopus and Web of Science electronic databases using the following keywords: "RBM20"; "cardiomyopathy"; "arrhythmias"; "heart failure". A total of 181 records were screened, of which 27 studies were potentially relevant to the topic. Through the application of inclusion and exclusion criteria, eight papers reporting 398 patients with RBM20 pathogenic variants were analysed. The mean age at presentation was 41 years. Familiarity with cardiomyopathy was available in 59% of cases, with 55% of probands reporting a positive family history. Imaging data indicated a mild reduction of left ventricular ejection fraction (mean LVEF 40%), while tissue characterization was reported in 24.3% of cases, showing late gadolinium enhancement in 33% of patients. Composite outcomes of sustained monomorphic ventricular tachycardia or ventricular fibrillation occurred in 19.4% of patients, with 12% undergoing HTx. There were no gender differences in arrhythmic outcomes, while 96.4% of patients who underwent HTx were male. In conclusion, RBM20 cardiomyopathy exhibits a severe phenotypic expression, both in terms of arrhythmic burden and HF progression. [ABSTRACT FROM AUTHOR]

Introduction: This paper explores the potential influence of a single nucleotide variant in the ANK-2 gene on COVID-19 myocarditis-related ventricular tachycardia. Case Description: A 53-year-old female with a history of Crohn's disease and asthma developed COVID-19. Shortly after infection, she experienced symptoms of chest pressure, palpitations, and shortness of breath, leading to the eventual diagnosis of myocarditis complicated by recurrent ventricular tachycardia. Treatment with mechanistically driven anti-arrhythmic therapy and beta-blockers suppressed this highly symptomatic ventricular tachycardia. Genetic testing to further risk stratify and influence long term care identified a single nucleotide variant in the ANK-2 gene, which is known to be associated with arrhythmic risk. Discussion: This case study highlights the use of rationally selected anti-arrhythmic therapy, mexiletine, in the management of ventricular tachycardia associated with COVID-19 myocarditis and the presence of a single nucleotide variant in ANK-2, raising the possibility of its contribution to VT susceptibility and severity. Our patient demonstrated significant improvement with administered therapeutics, including the resolution of myocarditis and ventricular tachycardia. The normalization of the QT interval during the resolution phase further supports the potential influence of the genetic variant in ANK-2 on potassium channel activity. [ABSTRACT FROM AUTHOR]

Studies indicate cardiac arrhythmia is one of the leading causes of death in the world. The risk of a stroke may be reduced when an irregular and fast heart rate is diagnosed. Since it is non-invasive, electrocardiograms are often used to detect arrhythmias. Human data input may be error-prone and time-consuming because of these limitations. For early detection of heart rhythm problems, it is best to use deep learning models. In this paper, a hybrid bio-inspired algorithm has been proposed by combining whale optimization (WOA) with adaptive particle swarm optimization (APSO). The WOA is a recently developed meta-heuristic algorithm. APSO is used to increase convergence speed. When compared to conventional optimization methods, the two techniques work better together. MIT-BIH dataset has been utilized for training, testing and validating this model. The recall, accuracy, and specificity are used to measure efficiency of the proposed method. The efficiency of the proposed method is compared with state-of-art methods and produced 98.25 % of accuracy. [ABSTRACT FROM AUTHOR]

Objective: The 22q11.2 Deletion Syndrome (22q11.2DS) is the most common microdeletion in humans with over 180 phenotypic expressions. Approximately 30–40% of affected individuals will develop psychosis and 25% meet the criteria for schizophrenia. Despite this, pharmacotherapy for managing psychosis in 22q11.2DS is poorly understood and 22q11.2DS psychosis is frequently labelled as treatment resistant. The objectives of this paper are to evaluate the effectiveness and tolerability of pharmacotherapy for 22q11.2DS psychosis and evaluate the evidence for treatment resistance. Method: A systematic search was performed using CINAHL, The Cochrane Library (Cochrane Database of Systematic Reviews; Cochrane Central Register of Controlled Trials and Cochrane Clinical Answers), EMBASE, PsycINFO, PubMed, Scopus and Web of Science Core Collection from inception to December 2022. It yielded 39 case reports, 6 case series and 1 retrospective study which met the inclusion criteria. Results: Based on the current literature, individuals with 22q11.2DS psychosis experience a greater rate of medical co-morbidities such as cardiac arrhythmias, seizures and movement disorders, which complicate pharmacotherapy. Poor tolerability rather than poor clinical response motivates the switching of antipsychotics, which may explain the labelling of treatment resistance in the literature. Conclusion: There are insufficient data to recommend a single antipsychotic for 22q11.2DS psychosis. Nonetheless, with proactive management of co-morbidities, antipsychotic medication in 22q11.2DS psychosis is an effective treatment commonly resulting in improvement in quality of life. [ABSTRACT FROM AUTHOR]

Cardiac fibrosis stands as one of the most critical conditions leading to lethal cardiac arrhythmias. Identifying the precise location of cardiac fibrosis is crucial for planning clinical interventions in patients with various forms of ventricular and atrial arrhythmias. As fibrosis impedes and alters the path of electrical waves, detecting fibrosis in the heart can be achieved through analyzing electrical signals recorded from its surface. In current clinical practices, it has become feasible to record electrical activity from both the endocardial and epicardial surfaces of the heart. This paper presents a computational method for reconstructing 3D fibrosis using unipolar electrograms obtained from both surfaces of the ventricles. The proposed method calculates the percentage of fibrosis in various ventricular segments by analyzing the local activation times and peak-to-peak amplitudes of the electrograms. Initially, the method was tested using simulated data representing idealized fibrosis in a heart segment; subsequently, it was validated in the left ventricle with fibrosis obtained from a patient with nonischemic cardiomyopathy. The method successfully determined the location and extent of fibrosis in 204 segments of the left ventricle model with an average error of 0.0±4.3% (N = 204). Moreover, the method effectively detected fibrotic scars in the mid-myocardial region, a region known to present challenges in accurate detection using electrogram amplitude as the primary criterion. [ABSTRACT FROM AUTHOR]

Denoising electrocardiogram (ECG) signals is a challenge because of the unavoidable artifacts. In this paper, an adaptive and effective signed error normalized least mean square algorithm (SE-NLMS) is proposed to improve denoising and reduce the mean square error (MSE). The performance of the SE-NLMS algorithm with unfolding technique (SE-UNLMS) has been compared with that of the normalized least mean square algorithm with unfolding structure (UNLMS) as well as the least mean square algorithm with unfolding structure (ULMS). SE-NLMS has the advantage of NLMS, namely an adaptive convergence rate and reduced computational complexity. We have implemented all three algorithms, namely LMS, NLMS and SE-NLMS, using an unfolding architecture that offers minimum delay and is suitable for real-time applications. Performance metrics such as an MSE of 0.0062 and a signal-to-noise ratio (SNR) improvement of about 10.2% are achieved through the proposed method of SE-UNLMS compared to other denoising methods. Additionally, exploration has been carried out for the application of denoised ECG signals under various arrhythmia conditions. The time-domain features were extracted from various denoised ECG signals and classified with support vector machines (SVM) to yield a good classification accuracy of 96.5%. The SE-UNLMS denoising method is demonstrated to be a valuable implementation for the detection and diagnosis of arrhythmias in real-world medical situations via this classification process. For real-time validation, the proposed method SE-UNLMS was implemented on a field-programmable gate array (FPGA) using Spartan 6 VPTB-20. The FPGA implementation highlights the feasibility of the proposed SE-UNLMS for real-time ECG monitoring applications. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to diagnose the cardiac arrhythmias. Automatic diagnosis of cardiac arrhythmias is crucial for successful treatment of heart diseases, and nowadays, machine learning is used to facilitate this purpose. For accurate classification of arrhythmias, it is vital to extract some appropriate features which distinguish different classes. In this research, a deep convolutional neural network is used to extract the features. Since the heart rates of various patients are very different, arrhythmias of a specific class will be placed in different locations of feature space. To reduce the intra-class variations, each patient's heart rate is mapped with a dedicated function to increase its similarity to the heart rate of a training patient. The proposed patient-specific mapping decreases the intra-class variation and significantly increases the classification accuracy of cardiac arrhythmias. The calculated accuracy is about 91.08%, which shows the better performance compared to other similar works. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) pose a significant global health threat due to their complex pathogenesis and high incidence, imposing a substantial burden on global healthcare systems. Integrins, a group of heterodimers consisting of α and β subunits that are located on the cell membrane, have emerged as key players in mediating the occurrence and progression of CVDs by regulating the physiological activities of endothelial cells, vascular smooth muscle cells, platelets, fibroblasts, cardiomyocytes, and various immune cells. The crucial role of integrins in the progression of CVDs has valuable implications for targeted therapies. In this context, the development and application of various integrin antibodies and antagonists have been explored for antiplatelet therapy and anti-inflammatory-mediated tissue damage. Additionally, the rise of nanomedicine has enhanced the specificity and bioavailability of precision therapy targeting integrins. Nevertheless, the complexity of the pathogenesis of CVDs presents tremendous challenges for monoclonal targeted treatment. This paper reviews the mechanisms of integrins in the development of atherosclerosis, cardiac fibrosis, hypertension, and arrhythmias, which may pave the way for future innovations in the diagnosis and treatment of CVDs. [ABSTRACT FROM AUTHOR]

The main goal of this study is to determine typical values of dose area product (DAP) and difference in the effective dose (ED) for pediatric electrophysiological procedures on the heart in relation to patient body mass. This paper also shows DAP and ED in relation to the indication, the arrhythmia substrate determined during the procedure, and in relation to the reason for using radiation. Organ doses are described as well. The subjects were children who have had an electrophysiological study done with a 3D mapping system and X-rays in two healthcare institutions. Children with congenital heart defects were excluded. There were 347 children included. Significant difference was noted between mass groups, while heavier children had higher values of DAP and ED. Median DAP in different mass groups was between 4.00 (IQR 1.00–14.00) to 26.33 (IQR 8.77–140.84) cGycm2. ED median was between 23.18 (IQR 5.21–67.70) to 60.96 (IQR 20.64–394.04) µSv. The highest DAP and ED in relation to indication were noted for premature ventricular contractions and ventricular tachycardia—27.65 (IQR 12.91–75.0) cGycm2 and 100.73 (IQR 53.31–258.10) µSv, respectively. In arrhythmia substrate groups, results were similar, and the highest doses were in ventricular substrates with DAP 29.62 (IQR 13.81–76.0) cGycm2 and ED 103.15 (IQR 60.78–266.99) µSv. Pediatric electrophysiology can be done with very low doses of X-rays when using 3D mapping systems compared to X-rays-based electrophysiology, or when compared to pediatric interventional cardiology or adult electrophysiology. [ABSTRACT FROM AUTHOR]

Respiratory interactions with the heart have remained a challenging physiological phenomenon since their discovery more than two hundred and fifty years ago. In the course of translating the seminal publications of Carl Ludwig and his disciple Pavel Petrovich Einbrodt into English, we became aware of some under-appreciated aspects of their work that contain useful insights into the history of the phenomenon now called respiratory arrhythmia. Ludwig observed arrhythmic effects of respiratory movements in experiments on dogs and horses and published his findings in 1847. He subsequently undertook further work on this problem, together with Einbrodt. Already in 1847 Ludwig had mentioned an exciting observation on the possible role of mechanical factors of the respiratory movements on the action of the heart in a dog in whom he had artificially induced bouts of coughing. Einbrodt decided to systematically develop methods to increase or decrease the pressure of the air the animal had to breathe. He observed that this procedure led to a greater or lesser degree of compression or decompression of all the organs in the thoracic cavity without apparently causing harmful consequences during the time of its application. How the mechanical influence of breathing affects cardiac activity during respiratory arrhythmia has been the subject of scientific discussions and controversies over a period of more than 150 years and is still unresolved. Recent publications suggest that cardiac mechano-electrical coupling plays an important role in the emergence of cardio-respiratory interdependence. [ABSTRACT FROM AUTHOR]

In this paper we build upon recent organizational research drawing upon the work of Henri Lefebvre, and feminist engagements with his theory of "Rhythmanalysis", to examine how the COVID‐19 pandemic disrupted women's everyday lives and the impacts on their relationship with work. Drawing upon interviews with 38 women from diverse socio‐cultural backgrounds living in Aotearoa New Zealand, we describe the pandemic as an "arrhythmia", a radical rupture to the familiar rhythms of their everyday social and working lives. We describe how the pandemic arrhythmia was felt in and through bodies (i.e., sleep, weight), and how women responded by creating counter rhythms (i.e., hobbies, exercise, food) as strategies to support their own and others wellbeing. Furthermore, radically disrupting linear repetitions of everyday work, social and family life, the pandemic prompted many to reflect differently upon how pre‐pandemic rhythms shaped by the social, economic and gendered structures of neoliberalism were causing various forms of alienation (i.e., from their own health and wellbeing, meaningful social connections, ethical and sustainable working practices, and from pleasure). For some women, the pandemic arrhythmia was a puncturing of their normalized time‐space gendered routines, leading to critique and transformation to their everyday work‐life patterns. Engaging a feminist reading of rhythmanalysis, this paper brings into focus how neoliberal gender regimes are reconstituted and disrupted in the rhythms and routines of women's everyday lives. In so doing, we highlight the potential in feminist engagement with arrhythmia to extend understandings of the gendered politics of everyday life during and beyond pandemic times, and the value in such approaches for organizational scholars interested in understanding the gendered rhythms of daily life and their effects on relationships with work. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a crucial tool for assessing cardiac health in humans. Aiming to enhance the accuracy of ECG signal classification, a novel approach is proposed based on relative position matrix and deep learning network information features for the classification task in this paper. The approach improves the feature extraction capability and classification accuracy via techniques of image conversion and attention mechanism. In terms of the recognition strategy, this paper presents an image conversion using relative position matrix information. This information is utilized to describe the relative spatial relationships between different waveforms, and the image identification is successfully applied to the Gam-Resnet18 deep learning network model with a transfer learning concept for classification. Ultimately, this model achieved a total accuracy of 99.30%, an average positive prediction rate of 98.76%, a sensitivity of 98.90%, and a specificity of 99.84% with the relative position matrix approach. To evaluate the effectiveness of the proposed method, different image conversion techniques are compared on the test set. The experimental results demonstrate that the relative position matrix information can better reflect the differences between various types of arrhythmias, thereby improving the accuracy and stability of classification. [ABSTRACT FROM AUTHOR]

This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell‐autonomous and cell–cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co‐ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction. [ABSTRACT FROM AUTHOR]

Temporary pacing wires are often used following cardiac surgery to optimise the heart rhythm. Although setting and checking temporary pacemakers is typically undertaken by anaesthetists, intensivists, and nursing staff who care for post-cardiac surgical patients, almost all patients with temporary pacing wires are transferred to the ward with the pacing wires left in situ, where surgical, often junior, staff become responsible for temporary pacing wire management. Thus, knowledge is required not only of temporary pacing wire indications, types, and positioning at surgery, but also of practical skills in performing a pacing check, setting the pacemaker, and troubleshooting common problems. The available literature targets clinicians well-versed in temporary pacing wire management. However, this paper provides a practical 'how to' for surgical staff managing temporary pacing wires in a non-critical care environment. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies often rely on self-reported health problems and validation greatly improves study quality. In a study of late effects after childhood cancer, we validated self-reported cardiovascular problems by contacting general practitioners (GPs). This paper describes: (a) the feasibility of this approach; and (b) the agreement between survivor-reports and reports from their GP. Methods: The Swiss Childhood Cancer Survivor Study (SCCSS) contacts all childhood cancer survivors registered in the Swiss Childhood Cancer Registry since 1976 who survived at least 5 years from cancer diagnosis. We validated answers of all survivors who reported a cardiovascular problem in the questionnaire. Reported cardiovascular problems were hypertension, arrhythmia, congestive heart failure, myocardial infarction, angina pectoris, stroke, thrombosis, and valvular problems. In the questionnaire, we further asked survivors to provide a valid address of their GP and a consent for contact. We sent case-report forms to survivors' GPs and requested information on cardiovascular diagnoses of their patients. To determine agreement between information reported by survivors and GPs, we calculated Cohen's kappa (κ) coefficients for each category of cardiovascular problems. Results: We used questionnaires from 2172 respondents of the SCCSS. Of 290 survivors (13% of 2172) who reported cardiovascular problems, 166 gave consent to contact their GP and provided a valid address. Of those, 135 GPs (81%) replied, and 128 returned the completed case-report form. Survivor-reports were confirmed by 54/128 GPs (42%). Of the 54 GPs, 36 (28% of 128) confirmed the problems as reported by the survivors; 11 (9% of 128) confirmed the reported problem(s) and gave additional information on more cardiovascular outcomes; and seven GPs (5% of 128) confirmed some, but not all cardiovascular problems. Agreement between GPs and survivors was good for stroke (κ = 0.79), moderate for hypertension (κ = 0.51), arrhythmias (κ = 0.41), valvular problems (κ = 0.41) and thrombosis (κ = 0.56), and poor for coronary heart disease (κ = 0.15) and heart failure (κ = 0.32). Conclusions: Despite excellent GP compliance, it was found unfeasible to validate self-reported cardiovascular problems via GPs because they do not serve as gatekeepers in the Swiss health care system. It is thus necessary to develop other validation methods to improve the quality of patient-reported outcomes. [ABSTRACT FROM AUTHOR]

Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion. Federated learning is a reasonable idea to train distributed multicenter data without direct access; however, a central server to merge and distribute models is needed, which is expensive and hardly approved due to various legal regulations. This paper proposes a continual learning framework for a multicenter study, which does not require a central server and can prevent catastrophic forgetting of previously trained knowledge. The proposed framework contains the continual learning method selection process, assuming that a single method is not omnipotent for all involved datasets in a real-world setting and that there could be a proper method to be selected for specific data. We utilized the fake data based on a generative adversarial network to evaluate methods prospectively, not ex post facto. We used four independent electrocardiogram datasets for a multicenter study and trained the arrhythmia detection model. Our proposed framework was evaluated against supervised and federated learning methods, as well as finetuning approaches that do not include any regulation to preserve previous knowledge. Even without a central server and access to the past data, our framework achieved stable performance (AUROC 0.897) across all involved datasets, achieving comparable performance to federated learning (AUROC 0.901). [ABSTRACT FROM AUTHOR]

Digital surgery technologies, such as interventional robotics and sensor systems, not only improve patient care but also aid in the development and optimization of traditional invasive treatments and methods. Atrial Fibrillation (AF) is the most common cardiac arrhythmia with critical clinical relevance today. Delayed intervention can lead to heart failure, stroke, or sudden cardiac death. Although many advances have been made in the field of radiofrequency (RF) catheter ablation (CA), it can be further developed by incorporating sensor technology to improve its efficacy and safety. Automation can be utilized to shorten the duration of RF ablation, provided that the interactions between the tissue and the RF tools are well understood and adequately modeled. Further research is needed to develop the optimal catheter design. This paper describes the systematic methodology developed to support robot-assisted RF CA characterization measurements. The article describes the custom instruments developed for the experiments, particularly the contact force limiter, the measurement procedure, and the evaluation of the results, as enablers for new results. The aim was to establish an objective, repeatable, robust measurement method and adjacent procedure. [ABSTRACT FROM AUTHOR]

Introduction: Premature ventricular complexes (PVCs) are the most common ventricular arrhythmia that are encountered in the clinical practice. Recent data suggests that high PVC burden may lead to the development of PVC‐induced cardiomyopathy (PVC‐CM) even in patients without structural heart disease. Treatment for effective suppression of PVCs, can reverse PVC‐CM. Both antiarrhythmic drugs (AADs) and catheter ablation (CA) are recognized treatment modalities for any cardiac arrhythmias. However, with increasing preference of CA, the role of AADs needs further defining regarding their efficacy, safety, indications and patient selection to treat PVC‐CM. Methods: To ascertain the role of AADs to treat PVC‐CM; whether they are indicated to treat PVC‐CM, and if so, when, we interrogated PubMed and other search engines for English language publications with key words premature ventricular complexes (PVCs), cardiomyopathy, anti‐arrhythmic drugs, catheter ablation, and pharmacological agents. All publications were carefully reviewed and scrutinized by the authors for their inclusion in the review paper. For illustration of cases, ethical standard was observed as per the 1975 Declaration of Helsinki, and the patient was treated as per the prevailing standard of care. Informed consent was obtained from the patient for conducting the ablation procedure. Results: Our literature search specifically the pharmacological treatment of PVC‐CM with AADs revealed significant paradigm shift in treatment approach for PVCs and PVC‐induced cardiomyopathy. No major large, randomized control trials of AADs versus CA for PVC‐CM were found. We found that beta‐blockers and calcium channel blockers are particularly effective in the treatment of PVCs originating from right ventricular outflow tract. For Class Ic AADs ‐ flecainide and propafenone, small clinical studies showed Class Ic AADs to be effective in PVC suppression, but their usage was not recommended in patients with significant coronary artery disease. Mexiletine was found to have modest effect on PVC suppression. Studies showed sotalol to significantly reduce PVCs frequency in patients receiving both low and high doses. Studies also showed amiodarone to have higher successful PVC suppression, but not recommended as a first‐line treatment for patients with idiopathic PVCs in the absence of symptoms and left ventricular dysfunction. For dronedarone, no major clinical data were available. Conclusions: Based on the available data in the literature, we conclude that AADs play important role in the treatment of PVC‐induced cardiomyopathy. However, appropriate patient selection criteria are vitally important, and in general terms AADs are indicated or polymorphic PVCs, epicardial PVCs; and when CA procedure is contraindicated, or not feasible or failed. [ABSTRACT FROM AUTHOR]

This paper targets the area of optimizing machine learning (ML) training data by constructing compact data. The methods of optimizing ML training have improved and become a part of artificial intelligence (AI) system development. Compact data learning (CDL) is an alternative practical framework to optimize a classification system by reducing the size of the training dataset. CDL originated from compact data design, which provides the best assets without handling complex big data. CDL is a dedicated framework for improving the speed of the machine learning training phase without affecting the accuracy of the system. The performance of an ML-based arrhythmia detection system and its variants with CDL maintained the same statistical accuracy. ML training with CDL could be maximized by applying an 85% reduced input dataset, which indicated that a trained ML system could have the same statistical accuracy by only using 15% of the original training dataset. [ABSTRACT FROM AUTHOR]

Worldwide, Cardiovascular Diseases (CVDs) are the leading cause of death. Patients at high cardiovascular risk require long-term follow-up for early CVDs detection. Generally, cardiac arrhythmia detection through the electrocardiogram (ECG) signal has been the basis of many studies. This technique does not provide sufficient information in addition to a high false alarm potential. In addition, the electrodes used to record the ECG signal are not suitable for long-term monitoring. Recently, the photoplethysmogram (PPG) signal has attracted great interest among scientists as it provides a non-invasive, inexpensive, and convenient source of information related to cardiac activity. In this paper, the PPG signal (online database Physio Net Challenge 2015) is used to classify different cardiac arrhythmias, namely, tachycardia, bradycardia, ventricular tachycardia, and ventricular flutter/fibrillation. The PPG signals are pre-processed and analyzed utilizing various signal-processing techniques to eliminate noise and artifacts, which forms a stage of signal preparation prior to the feature extraction process. A set of 41 PPG features is used for cardiac arrhythmias' classification through the application of four machine-learning techniques, namely, Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNNs), and Ensembles. Principal Component Analysis (PCA) technique is used for dimensionality reduction and feature extraction while preserving the most important information in the data. The results show a high-throughput evaluation with an accuracy of 98.4% for the KNN technique with a sensitivity of 98.3%, 95%, 96.8%, and 99.7% for bradycardia, tachycardia, ventricular flutter/fibrillation, and ventricular tachycardia, respectively. The outcomes of this work provide a tool to correlate the properties of the PPG signal with cardiac arrhythmias and thus the early diagnosis and treatment of CVDs. [ABSTRACT FROM AUTHOR]

This review paper delves into the current body of evidence, offering a thorough analysis of the impact of large-conductance Ca2+-activated K+ (BKCa or BK) channels on the electrical dynamics of the heart. Alterations in the activity of BKCa channels, responsible for the generation of the overall magnitude of Ca2+-activated K+ current at the whole-cell level, occur through allosteric mechanisms. The collaborative interplay between membrane depolarization and heightened intracellular Ca2+ ion concentrations collectively contribute to the activation of BKCa channels. Although fully developed mammalian cardiac cells do not exhibit functional expression of these ion channels, evidence suggests their presence in cardiac fibroblasts that surround and potentially establish close connections with neighboring cardiac cells. When cardiac cells form close associations with fibroblasts, the high single-ion conductance of these channels, approximately ranging from 150 to 250 pS, can result in the random depolarization of the adjacent cardiac cell membranes. While cardiac fibroblasts are typically electrically non-excitable, their prevalence within heart tissue increases, particularly in the context of aging myocardial infarction or atrial fibrillation. This augmented presence of BKCa channels' conductance holds the potential to amplify the excitability of cardiac cell membranes through effective electrical coupling between fibroblasts and cardiomyocytes. In this scenario, this heightened excitability may contribute to the onset of cardiac arrhythmias. Moreover, it is worth noting that the substances influencing the activity of these BKCa channels might influence cardiac electrical activity as well. Taken together, the BKCa channel activity residing in cardiac fibroblasts may contribute to cardiac electrical function occurring in vivo. [ABSTRACT FROM AUTHOR]

Metabolic dysfunction-associated steatotic liver disease (MASLD) typically presents with hepatic fibrosis in advanced disease, resulting in increased liver stiffness. A subset of patients further develops liver cirrhosis and hepatocellular carcinoma. Cardiovascular disease is a common comorbidity in patients with MASLD and its prevalence is increasing in parallel. Recent evidence suggests that especially liver stiffness, whether or not existing against a background of MASLD, is associated with heart diseases. We conducted a narrative review on the role of liver stiffness in the prediction of highly prevalent heart diseases including heart failure, cardiac arrhythmias (in particular atrial fibrillation), coronary heart disease, and aortic valve sclerosis. Research papers were retrieved from major scientific databases (PubMed, Web of Science) until September 2023 using 'liver stiffness' and 'liver fibrosis' as keywords along with the latter cardiac conditions. Increased liver stiffness, determined by vibration-controlled transient elastography or hepatic fibrosis as predicted by biomarker panels, are associated with a variety of cardiovascular diseases, including heart failure, atrial fibrillation, and coronary heart disease. Elevated liver stiffness in patients with metabolic liver disease should lead to considerations of cardiac workup including N-terminal pro-- B-type natriuretic peptide/B-type natriuretic peptide determination, electrocardiography, and coronary computed tomography angiography. In addition, patients with MASLD would benefit from heart disease case-finding strategies in which liver stiffness measurements can play a key role. In conclusion, increased liver stiffness should be a trigger to consider a cardiac workup in metabolically compromised patients. [ABSTRACT FROM AUTHOR]

Arrhythmia disorders are the leading cause of death worldwide and are primarily recognized by the patient's irregular cardiac rhythms. Wearable Internet of Things (IoT) devices can reliably measure patients' heart rhythms by producing electrocardiogram (ECG) signals. Due to their non-invasive nature, ECG signals have been frequently employed to detect arrhythmias. The manual procedure, however, takes a long time and is prone to error. Utilizing deep learning models for early automatic identification of cardiac arrhythmias is a preferable approach that will improve diagnosis and therapy. Though ECG analysis using cloud-based methods can perform satisfactorily, they still suffer from security issues. It is essential to provide secure data transmission and storage for IoT medical data because of its significant development in the healthcare system. So, this paper proposes a secure arrhythmia classification system with the help of effective encryption and a deep learning (DL) system. The proposed method mainly involved two phases: ECG signal transmission and arrhythmia disease classification. In the ECG signal transmission phase, the patient's ECG data collected through the IoT sensors is encrypted using the optimal key-based elgamal elliptic curve cryptography (OKEGECC) mechanism, and the encrypted data is securely transmitted to the cloud. After that, in the arrhythmia disease classification phase, the system collects the data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database to perform training. The collected data is preprocessed by applying the continuous wavelet transform (CWT) to improve the quality of the ECG data. Next, the feature extraction is carried out by deformable attention-centered residual network 50 (DARNet-50), and finally, the classification is performed using butterfly-optimized Bi-directional long short-term memory (BOBLSTM). The experimental outcomes showed that the proposed system achieves 99.76% accuracy, which is better than the existing related schemes. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of death and more than half million people were died around the world. However, cardiovascular health monitoring is crucial for effective heart disease diagnosis and management. In this paper, a novel deep learning-based YOLO-ECG model is proposed to ECG arrhythmia classification method for portable monitoring. Initially, the ECG signals are gathered using 12-lead electrodes in the real time and these signals are denoised using two-dimensional stationary wavelet transform (2D-SWT). In SWT, zeros are inserted between filter taps rather than decimal points to eliminate repetitions and increase robustness. The denoised ECG signals are fed into the deep learning-based YOLO network with Gaussian error linear unit (GELU) activation function for detecting the ECG abnormalities of arrythmia. ECG waveforms are analyzed for the local fractal dimension at each sample point before heartbeat waveforms are extracted within a set length window. A squeeze and excitation attention (SEAN) module is introduced in the YOLO network for selecting size of 1D convolution kernel, and the dimension is preserved during local cross-channel interactions, decrease network complexity and enhance model efficiency. The classification findings demonstrate that the proposed YOLO-ECG model performs better by ECG recordings from the MIT-BIH arrhythmia dataset. From the experimental analysis, the proposed YOLO-ECG model yields the overall accuracy of 99.16% for efficient classification of arrythmia ECG signals. [ABSTRACT FROM AUTHOR]

Paroxysmal supraventricular tachycardia (PSVT) is a common arrhythmia in adults. Its occurrence depends on the presence of the reentry circuit and the trigger of the paroxysm. Stress, emotional factors, and comorbidities favour the occurrence of such an episode. We hypothesized that the occurrence of PSVT follows extreme thermal episodes. The retrospective analysis was based on the data collected from three hospital emergency departments in Poland (Olsztyn, Radom, and Wroclaw) involving 816 admissions for PSVT in the period of 2016–2021. To test the hypothesis, we applied the Universal Climate Thermal Index (UTCI) to objectively determine exposure to cold or heat stress. The risk (RR) for PSVT increased to 1.37 (p = 0.006) in cold stress and 1.24 (p = 0.05) in heat stress when compared to thermoneutral conditions. The likelihood of PSVT during cold/heat stress is higher in women (RR = 1.59, p< 0.001 and RR = 1.36, p = 0.024, respectively) than in men (RR = 0.64 at p = 0.088 and RR = 0.78, p = 0.083, respectively). The susceptibility for PSVT was even higher in all groups of women after exclusion of perimenopausal group of women, in thermal stress (RR = 1.74, p< 0.001, RR = 1.56, p = 0.029, respectively). Females, particularly at the perimenopausal stage and men irrespective of age were less likely to develop PSVT under thermal stress as compared to thermoneutral conditions. Progress in climate change requires searching for universal methods and tools to monitor relationships between humans and climate. Our paper confirms that the UTCI is the universal tool describing the impact of thermal stress on the human body and its high usefulness in medical researches. [ABSTRACT FROM AUTHOR]

The prediction of heart disease has gained great importance in recent years. Efficient monitoring of cardiac patients can save tremendous number of lives. This paper presents a method for classification and prediction of electrocardiogram data obtained from 452 patients representing the risk of cardiac arrhythmia. The aim of the study is to select highly related features with arrhythmia risk by using three different feature selection algorithms. In addition, various machine learning models are utilized for the classification task such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM) and Decision Tree (DT). The experimental results show that combination of a purposed feature selection method which later is called "Matched Selection" using SVM classifier outperforms other combinations and have an accuracy of 81.27% while k-NN and DT classifiers have an accuracy of 69.66% and 73.50% respectively. The study, in which detailed analyses are presented comparatively, is promising for the future studies. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a cardiac arrhythmia caused by electrophysiological anomalies in the atrial tissue, tissue degradation, structural abnormalities, and comorbidities. A direct relationship exists between AF and altered mitochondrial activity resulting from membrane potential loss, contractile dysfunction, or decreased ATP levels. This review aimed to elucidate the role of mitochondrial oxidative mechanisms in AF pathophysiology, the impact of mitochondrial oxidative stress on AF initiation and perpetuation, and current therapies. This review followed the Preferred Reporting Items for Systematic Reviews and the Meta-Analysis Extension for Scoping Reviews. PubMed, Excerpta Medica Database, and Scopus were explored until June 2023 using "MESH terms". Bibliographic references to relevant papers were also included. Oxidative stress is an imbalance that causes cellular damage from excessive oxidation, resulting in conditions such as AF. An imbalance in reactive oxygen species production and elimination can cause mitochondrial damage, cellular apoptosis, and cardiovascular diseases. Oxidative stress and inflammation are intrinsically linked, and inflammatory pathways are highly correlated with the occurrence of AF. AF is an intricate cardiac condition that requires innovative therapeutic approaches. The involvement of mitochondrial oxidative stress in the pathophysiology of AF introduces novel strategies for clinical treatment. [ABSTRACT FROM AUTHOR]

Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both one dimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmented communication frameworks. [ABSTRACT FROM AUTHOR]

Healthcare is indeed an inevitable part of life for everyone. In recent days, most of the deaths have been happening because of noncommunicable diseases. Despite the significant advancements in medical diagnosis, cardiovascular diseases are still the most prominent cause of mortality worldwide. With recent innovations in Machine Learning (ML) and Deep Learning (DL) techniques, there has been an enormous surge in the clinical field, especially in cardiology. Several ML and DL algorithms are useful for predicting cardiovascular diseases. The predictive capability of these algorithms is promising for various cardiovascular diseases like coronary artery disease, arrhythmia, heart failure, and others. We also review the lung interactions during heart disease. After the study of various ML and DL models with different datasets, the performance of the various strategies is analyzed. In this study, we focused on the analysis of various ML and DL algorithms to diagnose cardiovascular disease. In this paper, we also presented a detailed analysis of heart failure detection and various risk factors. This paper may be helpful to researchers in studying various algorithms and finding an optimal algorithm for their dataset. [ABSTRACT FROM AUTHOR]

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. "Arrhythmia detection", "arrhythmia classification" and "arrhythmia detection and classification" are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, "performance analysis" and "science mapping", were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research. [ABSTRACT FROM AUTHOR]