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This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, and their benefits, shortcomings and areas for technological improvement. Although wearables for rhythm diagnosis and management have not been examined in large randomized clinical trials, undoubtedly the usage of wearables has quickly escalated in clinical practice. This document is the first of a planned series in which we will update information on wearables as they are revised and released to consumers.

This article provides an overview of the top 10 papers in arrhythmias published in 2023, focusing on advancements in the understanding and treatment of atrial fibrillation (AF) and long QT syndrome (LQTS). The article discusses the use of the HARMS2-AF score for screening high-risk individuals for AF and the cost-effectiveness of AF screening. It also examines the clinical implications of oral anticoagulation in subclinical AF and the role of catheter ablation in treating AF. Additionally, the article explores a new threshold for defining AF recurrences and the potential for epicardial arrhythmogenic substrate ablation in LQTS. [Extracted from the article]

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]

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.

Graph: Graphical Abstract (A) Comprehensive clinical, electrocardiographic, and genetic overview of the various diseases associated with VA or SCD as reported in the 2022 ESC Guidelines for VA and SCD.[1] The VA/SCD Guidelines provide many updated recommendations for the management of patients with congenital heart disease, idiopathic VF, acquired Long QT, Brugada and early repolarization syndrome, as well as catecholaminergic polymorphic VT, and short QT syndrome. Effect of MRI-guided fibrosis ablation vs conventional catheter ablation on atrial arrhythmia recurrence in patients with persistent atrial fibrillation: the DECAAF II randomized clinical trial. In these patients, rhythm control is generally not considered whilst RAFAS suggests early ablation may be beneficial.[10] Larger well-controlled clinical trials on catheter ablation in patients with AF detected early after acute ischaemic stroke are definitely needed to settle the issue. [Extracted from the article]

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]

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]

This paper reviews arrhythmia classification studies using electrocardiogram (ECG) signals. Research on automatically diagnosing arrhythmia in daily life has been actively underway for early detection and treatment of heart disease. Development of automatic arrhythmia classification using ECG signal began based on handcrafted morphological feature extraction and machine learning-based classification methods. As deep neural networks (DNN) show excellent performance in the signal processing field, studies using various types of DNN are also being conducted in ECG classification. However, these DNN-based studies have extremely high computational complexity, making it challenging to perform real-time classification, and are unsuitable for low-power environments such as wearable devices due to high power consumption. Currently, research based on spiking neural network (SNN), which mimics the low-power operation of the human nervous system, is attracting attention as a method that can dramatically reduce complexity and power consumption. The classification accuracy of the SNN-based ECG classification studies is close to that of the DNN-based studies. When combined with neuromorphic hardware, it shows ultra-low-power performance, suggesting the possibility of use in lightweight devices. In this paper, the SNN-based ECG classification studies for low-power environments are mainly reviewed, and prior to this, conventional and DNN-based ECG classification studies are also reviewed. We hope that this review will be helpful to researchers and engineers interested in the field of ECG classification. [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 population of patients with congenital heart disease (CHD) is continuously increasing with more and more patients reaching adulthood. A significant portion of these young adults will suffer from arrhythmias due to the underlying congenital heart defect itself or as a sequela of interventional or surgical treatment. The medical community will encounter an increasing challenge as even most of the individuals with complex congenital heart defects nowadays become young adults. Within the past 20 years, management of patients with arrhythmias has gained remarkable progress including pharmacological treatment, catheter ablation, and device therapy. Catheter ablation in patients with CHD has paralleled the advances of this technology in pediatric and adult patients with structurally normal hearts. Growing experience and introduction of new techniques like the 3D mapping systems into clinical practice have been particularly beneficial for this growing population of patients with abnormal cardiac anatomy and physiology. Finally, device therapies allowing maintanence of chronotropic competence and AV conduction, improving haemodynamics by cardiac resynchronization, and preventing sudden death are increasingly used. For pharmacological therapy, ablation procedures, and device therapy decision making requires a deep understanding of the individual pathological anatomy and physiology as well as detailed knowledge on natural history and long-term prognosis of our patients. Composing expert opinions from cardiology and paediatric cardiology as well as from non-invasive and invasive electrophysiology this position paper was designed to state the art in management of young individuals with congenital heart defects and arrhythmias. [ABSTRACT FROM AUTHOR]

Introduction and aim. Patients after myocardial infarction (MI) are prone to develop ventricular tachycardia (VT). Sustained VT might result in hemodynamic instability and sudden cardiac death (SCD). Thus, antiarrhythmic pharmacotherapy, ablation and implantable cardioverter defibrillator (ICD) implantation might be required. Description of the case. A 75-year-old multimorbid male with a recent history of (1) non-ST-segment elevation MI, treated with drug-eluting stent implantation, (2) chronic heart failure with reduced left ventricular ejection fraction (HFrEF) and (3) arrhythmia, initially misdiagnosed as atrial flatter with right bundle branch block aberration, was admitted to the Cardiology Department due to palpitations. Electrocardiography showed wide QRS complex tachycardia with ventricular rate of ca. 130/min. VT was recognized and terminated by electrical cardioversion. Recurrent episode of VT was terminated by administration of lidocaine. Implantable cardioverter defibrillator (ICD) was implanted as a secondary prevention measure. Subsequently, VT reoccurred, thus electrophysiology study (EPS) was performed. EPS revealed VT originating from the basal-septal region, as well as self-limiting VT originating from the left ventricular outflow tract. The former origin was ablated. Pharmacotherapy with amiodarone was initiated. After 10 months, patient was readmitted due to electrical storm. VT was terminated after several attempts of anti-tachycardia pacing. EPS showed VT originating from the basal-septal region, where two post-MI scars were located. Another catheter ablation was performed. No VT episodes were recorded after the procedure. Conclusion. In post-MI patients with sustained VT and symptomatic HFrEF, ICD implantation should be considered in SCD prevention. Catheter ablation might additionally reduce the number of VT episodes and ICD interventions. Pharmacotherapy with amiodarone, and eventually ablation, should be considered in patients experiencing recurrent episodes of VT or electrical storm. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%. [ABSTRACT FROM AUTHOR]

These papers address how bioengineering principles can be harnessed to develop targeted therapies for a myriad of health conditions, from antimicrobial endotracheal tubes to lipid-polymer nanoparticles for cancer treatment. i I We hope that this special issue serves as an inspiration and a reference point for researchers and clinicians alike, highlighting the immense potential of biomedical engineering to drive advances in healthcare. i I Editors, i I Cellular and Molecular Bioengineering i I Biomedical Engineering Society i I 2023 Special Issue: Young Innovators i Thanks ChatGPT, we couldn't have said it better ourselves! Introduction to the Special Issue I We are thrilled to present the "Young Innovators" special issue of Cellular and Molecular Bioengineering, featuring groundbreaking work in biomedical engineering. We are pleased to present this year's twelve Young Innovators of Cellular and Molecular Bioengineering, whose original research is featured in this August issue. [Extracted from the article]

Cardiovascular diseases (CVDs) are highly prevalent, sudden onset, and relatively fatal, posing a significant public health burden. Long-term dynamic electrocardiography, which can continuously record the long-term dynamic ECG activities of individuals in their daily lives, has high research value. However, ECG signals are weak and highly susceptible to external interference, which may lead to false alarms and misdiagnosis, affecting the diagnostic efficiency and the utilization rate of healthcare resources, so research on the quality of dynamic ECG signals is extremely necessary. Aimed at the above problems, this paper proposes a dynamic ECG signal quality assessment method based on CNN and LSTM that divides the signal into three quality categories: the signal of the Q1 category has a lower noise level, which can be used for reliable diagnosis of arrhythmia, etc.; the signal of the Q2 category has a higher noise level, but it still contains information that can be used for heart rate calculation, HRV analysis, etc.; and the signal of the Q3 category has a higher noise level that can interfere with the diagnosis of cardiovascular disease and should be discarded or labeled. In this paper, we use the widely recognized MIT-BIH database, based on which the model is applied to realistically collect exercise experimental data to assess the performance of the model in dealing with real-world situations. The model achieves an accuracy of 98.65% on the test set, a macro-averaged F1 score of 98.5%, and a high F1 score of 99.71% for the prediction of Q3 category signals, which shows that the model has good accuracy and generalization performance. [ABSTRACT FROM AUTHOR]

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]

The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU. [ABSTRACT FROM AUTHOR]

This document is an editorial from the International Journal of Molecular Sciences titled "Molecular Research on Heart Protection." It discusses recent advancements in molecular research on heart protection and their potential impact on diagnosing and treating heart injuries and disorders. The editorial presents original research papers and reviews that explore molecular mechanisms and therapeutic opportunities for heart protection. The studies mentioned in the editorial include investigations on chronic anthracycline-induced cardiomyopathy, genetically modified mesenchymal stem cells for cardiac tissue restoration, the effects of sEVs on cell proliferation and angiogenesis, the role of Nr1d1 in cellular senescence and cardiac aging, the effects of MSC injection on post-infarction arrhythmia, the association between obesity, exercise, and cardiovascular problems, modRNA-based therapy for myocardial infarction, and the protective effect of young blood on mammalian organs. The editorial concludes by emphasizing the importance of integrating emerging molecular events with known networks to improve cardiac function and advance diagnostic and treatment strategies. [Extracted from the article]

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]

In the last ten years, the characterization and expectation of cardiac illnesses based on ECG signals have become increasingly important for doctors and patients. This paper delivers a deep learning technique that has recently been created for the order of ECG information with Normal Sinus Rhythm (NSR), Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF). A sum of 162 ECG signals is open, including 96 arrhythmias, 30 Congestive Heart failures, and 36 Normal Sinus Rhythm signals. To exhibit the classification performance of deep learning architectures, this paper studied ECG using the two CNN models GoogleNet and DenseNet201. The proposed study's classification accuracy is 91% and 100% respectively. The outcomes uncover that the proposed profound learning architecture is more precise than traditional machine learning classifiers at classifying ECG signals [ABSTRACT FROM AUTHOR]

As a common and high-risk type of disease, heart disease seriously threatens people's health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decodermodel. Themodel uses Long Short-TermMemory (LSTM) to consider the influence of time series features on classification results. Simultaneously, it is trained and tested by the MIT-BIH arrhythmia database. Besides, Generative Adversarial Networks (GAN) is adopted as a method of data equalization for solving data imbalance problem. The simulation results show that for the inter-patient arrhythmia classification, the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy, of which the accuracy can reach 94.05%. Especially, it has a better advantage for the classification effect of supraventricular ectopic beats (class S) and fusion beats (class F). [ABSTRACT FROM AUTHOR]

The article presents abstracts related to equine veterinary medicine which include repair of third tarsal bone slab fracture in 17 thoroughbred racehorses, outcome foals that undergo neonatal encephalopathy, and heart rate and arrhythmia frequency of horses after an endurance race.

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Networks (RNN), Transformers, and Convolutional Neural Networks (CNN). While CNN is capable of extracting local spatial information from images, it lacks the ability to learn global spatial features and temporal memory features. Conversely, RNN relies on time and can retain significant sequential features. However, they are not proficient in extracting lengthy dependencies of sequence data in practical scenarios. The self-attention mechanism in the Transformer model has the capability of global feature extraction, but it does not adequately prioritize local features and cannot extract spatial and channel features. This paper proposes STFAC-ECGNet, a model that incorporates CAMV-RNN block, CBMV-CNN block, and TSEF block to enhance the performance of the model by integrating the strengths of CNN, RNN, and Transformer. The CAMV-RNN block incorporates a coordinated adaptive simplified self-attention module that adaptively carries out global sequence feature retention and enhances spatial–temporal information. The CBMV-CNN block integrates spatial and channel attentional mechanism modules in a skip connection, enabling the fusion of spatial and channel information. The TSEF block implements enhanced multi-scale fusion of image spatial and sequence temporal features. In this study, comprehensive experiments were conducted using the PTB-XL large publicly available ECG dataset and the China Physiological Signal Challenge 2018 (CPSC2018) database. The results indicate that STFAC-ECGNet surpasses other cutting-edge techniques in multiple tasks, showcasing robustness and generalization. [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]

Atrial flutter, a prevalent cardiac arrhythmia, is primarily characterized by reentrant circuits in the right atrium. However, atypical forms of atrial flutter present distinct challenges in terms of diagnosis and treatment. In this study, we examine three noteworthy clinical cases of atypical atrial flutter, which offer compelling evidence indicating the implication of the lesser-known Septopulmonary Bundle (SPB). This inference is based on the identification of distinct electrocardiographic patterns observed in these patients and their favorable response to catheter ablation, which is a standard treatment for atrial flutter. Remarkably, in each case, targeted ablation at the anterior portion of the left atrial roof effectively terminated the arrhythmia, thus providing further support for the hypothesis of SPB involvement. These insightful observations shed light on the potential significance of the SPB in the etiology of atypical atrial flutter and introduce a promising therapeutic target. We anticipate that this paper will stimulate further exploration into the role of the SPB in atrial flutter and pave the way for the development of targeted ablation strategies. [ABSTRACT FROM AUTHOR]

With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layerbased CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute (ANSI) standards and the Association for the Advancement of Medical Instruments (AAMI). The Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia dataset is used for the experiment. The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0% and a positivity predictively 99.2% in the detection of a Ventricular Ectopic Beat (VEB). Moreover, it also gained a sensitivity of 99.0% and positivity predictively of 99.2% for the detection of a supraventricular ectopic beat (SVEB). The overall accuracy of the proposed model is 99.68%. [ABSTRACT FROM AUTHOR]

Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into twodimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices. [ABSTRACT FROM AUTHOR]

Arrhythmia is a subnormal heartbeat that causes Tachycardia or Bradycardia. ECG signal is extensively used for the analysis of heart diseases. The ECG is used to measure the rate and regularity of heartbeats. With the help of the study of the ECG signal, the presence of any damage to the heart can be identified. Arrhythmia classification is an important part of the research of medical images and signal processing. This paper aims to apply the K-SVD technique so that both the best features selected, and the accuracy of arrhythmia classification are improved. Additionally, the required space for storage has been reduced. For each ECG, we extracted 100 features of each ECG signal. Afterward, we applied statistical descriptors for extracted features which include Mean, Energy, Variance, Covariance, and Standard Deviation (S.D). The next step is the training process. We applied MLP neural networks. We have used two kinds of datasets in our research paper, UCI Machine Learning Repository and the MIT-BIH dataset. The accuracy rate of the proposed method is 98.90% and 99.14% for the UCI and MIT-BIH Dataset, respectively. [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]

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]

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]

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]

Recently, Electrocardiography (ECG) signals are commonly used in diagnosing the cardiac arrhythmia that shows up with the loss of the regular movement of the heart. Approximately 5% of the world population have cardio motor disorders. Therefore, usage of the ECG signals in biomedical signal processing algorithms and machine learning methods for automated diagnosis of this widespread health problem is a popular research topic. In this paper, the Particle Swarm Optimization (PSO) technique is implemented to tune the parameters of Tunable Q-Factor Wavelet Transform (TQWT) and the new generation feature generator Hamsi Hash Function (Hamsi-Pat) is used to obtain the characteristics of the signal. Sub-signals of 10 s obtained from the original ECG signal are divided into their sub-bands of 25 levels with PSO and TQWT. Each of these low pass filters generates 536 dimensional features by applying Hamsi-Pat and statistical methods. Then, all these features are combined and 536 × 25 = 13400-dimensional feature set is obtained. The features in the set are reduced and the best of them are selected by using the Iterative Neighborhood Component Analysis (INCA) method. Finally, the k-Nearest Neighbors (kNN) classification method is applied to the best features according to the City Block measurement criterion. All studies cited to compare the results in this paper also use the MIT-BIH Arrhythmia ECG database. Hence, the difference could be observed in the used techniques. In contrast to the existing studies, this study shows its superior performance by classifying all 17 classes simultaneously by applying a "fused" approach. The method in the paper reached 98.5% classification accuracy on the 17 classes of the MIT-BIH Arrhythmia ECG database. The results indicate that the proposed method showed better rates from the existing studies related to arrhythmia diagnosis using ECG signals in the literature. [ABSTRACT FROM AUTHOR]