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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.

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]

Copyright of Medizinische Klinik: Intensivmedizin & Notfallmedizin is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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.

Objectives: Junctional ectopic tachycardia (JET), an arrhythmia of substantial clinical relevance, is no less than an eternal nemesis in cardiac critical care. Hence, we hereby present a bibliometric study evaluating the research trends in the subject. Material and Methods: A Scopus search-based bibliometric analysis of the keyword “Junctional Tachycardia” OR “Junctional Ectopic Tachycardia” restricted to original articles and reviews was undertaken after excluding the veterinary-related papers. The metadata thus obtained was analyzed using Scimago Graphica 1.0.42 and VOSviewer version 1.6.20 to generate a graphical representation of the trends and the timelines based on the author keywords. Results: A total of 926 papers of interest were identified and selected for the analysis, which revealed the geographical distribution of productivity being primarily concentrated in the Western developed nations, topic receptiveness largely appreciated in cardiovascular-related journals, and increased yearly output of the JETassociated papers. Further perusal identified 79 most frequently observed author keywords when limited to a minimum of 5 co-occurrences, which were grouped into seven color-coded clusters by VOSviewer, and mapped into keyword as well as author network, overlay, and density projections. Conclusion: Bibliometric analysis of JET papers from 1967 to 2024 shows a growing interest in the topic, awaiting newer insights into the molecular mechanisms and the preventative treatment strategies. [ABSTRACT FROM AUTHOR]

This paper applies literary rhythmanalysis to six queer life writing texts from The incompleteness book, an Australasian anthology of writings produced in 2020 under early COVID-19 home isolation protocols. The analysis is steered by Halberstam's theory of queer time in articulation with rhythmanalysis as theorised by Lefebvre and Régulier. The paper includes discussion of these driving theories, followed by the literary rhythmanalysis itself. Across the six texts, two common themes arise. The first is geo-temporal dissociation. The second is an acute sense of pain stemming from social arrhythmias that preceded the pandemic but were exacerbated or made more noticeable by it. The concluding section reflects from a current day vantage on what these findings reveal about queer people's relationships to dominant social rhythms before and during 2020 and how this can inform future situations. [ABSTRACT FROM AUTHOR]

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]

Copyright of Der Kardiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Arrhythmia detection in electrocardiogram (ECG) signals is essential for monitoring cardiovascular health. Current automated arrhythmia classification methods frequently encounter difficulties in detecting multiple cardiac abnormalities, particularly when dealing with imbalanced datasets. This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual connections to enhance the feature extraction efficiency and classification accuracy. To guarantee superior input signals, we preprocess the ECG signals by removing baseline drift with a high-pass Butterworth filter, denoising via discrete wavelet transform, and segmenting heartbeat cycles through R-peak detection. Additionally, we rectify the class imbalance in the MIT-BIH Arrhythmia Database by applying the Synthetic Minority Oversampling Technique (SMOTE), therefore enhancing the model's ability to detect infrequent arrhythmia types. The suggested system achieves a classification accuracy of 99.09% on the MIT-BIH dataset, surpassing conventional convolutional neural networks and other state-of-the-art methodologies. Compared to existing approaches, our strategy exhibits superior effectiveness and robustness in managing diverse irregular heartbeats and arrhythmias. [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]

Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value. [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]

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]

The real-time processing and transfer of ECG (Electrocardiogram) signals using mobile phones have become increasingly important in modern healthcare systems. This paper presents a system that captures ECG signals from a patient via a portable ECG sensor, processes the data in real time on a mobile device, and transfers it to healthcare professionals or remote monitoring systems through wireless networks. The proposed system leverages the processing power of modern smartphones to filter, analyze, and extract significant features from the ECG signals, such as heart rate and arrhythmia detection, using signal processing algorithms. By using mobile communication technologies (e.g., 4G/5G), the processed data is then transmitted to cloud servers or medical centers, enabling continuous monitoring and timely diagnosis. This approach enhances patient mobility, reduces the need for bulky medical equipment, and supports real-time decision-making for critical heart conditions. The system also integrates user-friendly interfaces, ensuring accessibility for both patients and clinicians. Initial results demonstrate the system's efficiency in providing accurate ECG analysis with low latency, showing potential for widespread use in telemedicine and remote healthcare monitoring applications. [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]

The major cause of death worldwide are cardiovascular diseases with a disproportionate impact on various underserved populations. This research paper helps to improve remote cardiac diagnosis analysis and monetary with the help of cloud connected ECG devices especially in the population where the resources are limited. There are many recent advancements in iot based ECG monitoring devices which help for real time data collection data transmission analysis and monitoring, which have been studied in this research paper. The potential components which make the systems very support you are variable ECG sensors mobile applications and cloud-based platforms for processing and storage of the data. The cloud connected ECG devices are very popular and the increased use of them is because of applications like accessibility to continuous monitoring potential for early detection of diseases like arrhythmias. The upcoming research challenges as reviewed in this paper are the quality of the signal patient compliance and the data security. The automation of ECG interpretation and risk stratification can be done by using the latest AI and ML algorithms. To summarise this cloud connected ECG devices are helpful in representing a positive approach and extending cardiac care beyond the expected traditional health care and environment. The work done in this paper can be taken further explore research ideas to optimise the clinical utility and also to address the various implementation barriers. [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]

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]

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]

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]

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]

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.

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN). The model integrates three convolutional branch layers with different kernel sizes and dilation rates to capture features across varying temporal scales. A multi-head self-attention mechanism dynamically allocates weights, integrating features and correlations from different branches to enhance the recognition capability for difficult-to-classify samples. Additionally, the temporal convolutional network employs multi-layer dilated convolutions to progressively expand the receptive field for extracting long-term dependencies. To tackle data imbalance, a novel data augmentation strategy is implemented, and focal loss is utilized to increase the weight of minority classes, while Bayesian optimization is employed to fine-tune the model's hyperparameters. The results from five-fold cross-validation on the MIT-BIH Arrhythmia Database demonstrate that the proposed method achieves an overall accuracy of 98.75%, precision of 96.60%, sensitivity of 97.21%, and F1 score of 96.89% across five categories of ECG signals. Compared to other studies, this method exhibits superior performance in arrhythmia classification, significantly improving the recognition rate of minority classes. [ABSTRACT FROM AUTHOR]

The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar. However, by adopting such representations, state-of-the-art approaches usually rely on the magnitude information alone, while the important phase information is often neglected. Motivated by these considerations, the focus of this paper is aimed at investigating the effect of fusing the magnitude and phase of the continuous wavelet transform (CWT), known as the scalogram and phasogram, respectively. Scalograms and phasograms are fused in a simple CNN-based architecture by using several fusion strategies, which fuse the information in the input layer, some intermediate layers, or in the output layer. Numerical results evaluated on the PhysioNet MIT-BIH Arrhythmia database show the effectiveness of the proposed ideas. Although a simple architecture is used, their competitiveness is high compared to other state-of-the-art approaches, by obtaining an overall accuracy of about 98.5% and sensitivity and specificity of 98.5% and 95.6%, respectively. [ABSTRACT FROM AUTHOR]

Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field. While these multi-lead ECG-based models perform well in automatic arrhythmia detection, their complexity often restricts their use on resource-constrained devices. In this paper, we propose an efficient, lightweight arrhythmia classification model using a knowledge distillation technique to train a student model from a teacher model, tailored for embedded intelligence in wearable devices. The results show that the student model achieves 96.32% accuracy, which is comparable to the teacher model, with a remarkable compression ratio that is 1242.58 times smaller, outperforming other lightweight models. Enabled by the proposed model, we developed a wearable ECG monitoring system based on the STM32F429 Discovery kit and ADS1292R chip, achieving real-time arrhythmia detection on small wearable devices. [ABSTRACT FROM AUTHOR]