Arrhythmia Detection Research Articles

Effectiveness of photoplethysmography-based rhythm monitoring after atrial fibrillation ablation using a smartphone application

Abstract Introduction Recurrence of atrial fibrillation (AF) after AF ablation is frequent. Monitoring recurrence is essential for attributing patient symptoms and for assessing both AF burden and treatment effectiveness. The conventional approach relies on periodic (long-term) electrocardiograms (ECG) and is constrained by limited monitoring time, measurement dispersion, and cost. Furthermore, patients are unable to prioritize measurements based on symptoms. Photoplethysmography (PPG) emerges as a technology that could potentially transform any smartphone into a rhythm monitoring device, thereby mitigating the limitations associated with current practices. Selected applications leveraging this technology have demonstrated excellent usability and accuracy for the detection of AF. However, the effectiveness of PPG-based follow-up remains uncertain. Purpose To compare the effectiveness of digital follow-up utilizing a PPG-based smartphone application with conventional ECG-based follow-up for the detection of atrial arrhythmia recurrence after ablation. Methods Patients undergoing AF ablation were consecutively enrolled and monitored using a 24h-ECG at 3-6-12 months on top of ECGs conducted for clinical indications (conventional follow-up). Additionally, patients were instructed to perform PPG measurements twice daily or whenever symptoms were perceived, over the course of one year (digital follow-up). The primary outcome was recurrence of atrial arrhythmia. Results In total, 96 patients (69% male, mean age 64 [58 – 68] years) performed 39,895 PPG measurements. The compliance rate (n performed/prescribed) was 92.6% for ECG and 78.2% for PPG recordings. After one year follow-up, atrial arrhythmia recurrence was detected by conventional follow-up in 17.9% and in 38.5% of patients by digital follow-up (odds ratio, 3.4; 95% CI: 1.7-7.1). The confidence interval lower limit exceeded the predefined non-inferiority margin (p for non-inferiority > 0.001). Hence, superiority was tested (p for superiority = 0.001). The negative predictive value of digital follow-up for atrial arrhythmia detected with conventional follow-up was 98.3% (95% CI: 90.1-99.9%). Symptoms were reported in 81.1% of PPG-detected atrial arrhythmias and episodes sustained for 3.9 [1.1 – 22.7] hours. Conclusions Among patients with AF ablation, digital PPG-based rhythm monitoring with a smartphone, was non-inferior in the detection of atrial arrhythmia recurrence after ablation. Moreover, the detection of atrial arrhythmia was significantly increased compared to conventional ECG-based monitoring.

Does ChatGPT-4 succeed in the ECG interpretation: friend or foe to cardiologists?

Abstract Background/Introduction Artificial intelligence (AI) and large language models (LLMs) are increasingly being tested for medical purposes, especially in clinical settings and one of the most well-known LLMs is OpenAI’s ChatGPT-4. Recent explorations have considered using ChatGPT to answer medical questions, and the new ChatGPT-4 also allows graphical inputs, which could aid medical doctors in interpreting electrocardiograms (ECGs) in everyday practice. AI-powered ECG interpretation has shown promising results, improving the detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities. However, whether ChatGPT4’s interpretation of ECGs is comparable to that of experienced cardiologists has yet to be established. Purpose We sought to test whether ChatGPT-4 could analyze and interpret ECGs similarly to experienced cardiologists. Methods We tasked ChatGPT-4 with interpreting, for scientific purposes, 12-lead ECGs, from 150 patients included in our prospective CaRD registry, presenting with various cardiac pathologies, including acute coronary syndrome, arrhythmias (primarily atrial fibrillation), conduction abnormalities, pacemaker rhythms and normal ECGs. Four experienced cardiologists reviewed the ECG interpretations provided by ChatGPT-4. The model provided information on rate, rhythm, axis, P-wave, PR interval, QRS complex, ST segment, T wave, differential diagnoses, most likely diagnosis and further diagnostic step. Results In this observational study, ChatGPT-4 demonstrated low overall accuracy in interpreting ECGs, with only 24% of the most likely diagnosis being correct. ChatGPT-4 performed the best when interpreting normal ECGs with sinus rhythm, achieving 62% accuracy rate, followed by 17%, 10% and 7.3% for ECGs with arrhythmias, acute ischemic changes, and conduction abnormalities or pacing rhythms, respectively. Conclusion ChatGPT-4 exhibited very low accuracy in interpreting different categories of pathology in 12-lead ECG, yielding less than 20% success rate. While the use of AI in medical diagnostics is an appealing concept in theory, the results suggest that, in its current state, ChatGPT-4 would not provide significant aid to experienced cardiologists in clinical settings.

Effect of implantable cardioverter defibrillator intracardiac electrogram morphology changes on hospitalization and life quality

Abstract Background Implantable Cardioverter Defibrillators (ICD) are standard therapy for patients at risk of life-threatening ventricular tachyarrhythmias. ICDs use morphology discrimination to compare ventricular electrogram during tachycardia with reference template during baseline rhythm. Baseline morphology match score (MMS) changes may predict arrhythmic events, but usefulness as a parameter for heart failure (HF) is still unknown. Methods The prospective single-center study included consecutive 88 patients undergoing device control at cardiology department from July 2022 to May 2023. Inclusion criteria were patients implanted with ICD or CRT for at least 3 months; the exclusion criteria were defined as communication barriers (n=1), depleted device battery during checks (n=1), total AV block (n=3), and auto-template activation (n=4). The data were analysed for changes in the baseline (MMS) relationship with demographic, clinical characteristics, quality of life (QOL), and hospitalisation with HF. Results We analyzed 79 patients with a mean age 64,9 13,6 and an average MMS of 98 1,8 morphology. During follow-up there were 12 (15%) patients hospitalized due to HF. ROC curve analysis showed that a MMS ≤ 97% had 73% sensitivity and 59% specificity for hospitalization (p: 0,033 Area 0.695). No significant differences were observed in demographics between the two groups. Although there was no significant difference in EF and BNP levels, patients with MMS <97% had lower QOL scores and more hospitalizations for heart failure (HF). Conclusion EGM baseline MMS changes in ICD were associated with hospitalization for HF and QOL, apart from arrhythmia detection indications. It may be a follow-up parameter for HF, in addition to the current device algorithms.Graphical AbstractTable 1

Device programming and SMART pass algorithm activation in subcutaneous implantable defibrillator patients: data from a remote monitoring database

Abstract Background In subcutaneous implantable cardioverter-defibrillator (S-ICD) recipients, a previous study demonstrated very low rates of delivered shocks and inappropriate shocks on programming a conditional zone between 200 and 250 bpm and a shock zone for arrhythmias >250 bpm. The extent to which this programming approach is adopted in clinical practice has been little studied, as the activation status of the SMART Pass filter, that was implemented to reduce inappropriate shocks and enhanced over time. Purpose We assessed device programming during follow-up and the rate of detected arrhythmias in a cohort of consecutive S-ICD recipients. Methods We analyzed data from 670 S-ICD patients followed on the remote network at 17 Italian centers for a median of 31 months (25th-75th percentile: 16-51). The enhanced SMART Pass version was available for all patients after October 2022. It makes the deactivation monitor less sensitive to premature ventricular contractions or other transient amplitude changes while maintaining arrhythmia sensitivity and specificity, and it was expected to reduce unintentional deactivation rate. Results At the last remote data transmission, the median programmed conditional zone cut-off was set to 210 bpm (25th-75th percentile: 200-220), the shock zone cut-off was 250 bpm (25th-75th percentile: 240-250), and the SMART Pass was enabled in 586 (87%) patients. During follow-up, 194 automatic deactivation events (0.10/patient-year) were reported in 118 (18%) patients. The SMART Pass-disabled time was 13% of the total observation period. Shocks were delivered in 129 (19%) patients (rate: 0.13/patient-year), and untreated arrhythmias were recorded in 136 (20%) patients (rate: 0.14/patient-year). The rate of shocks was lower when SMART Pass was enabled – 0.12/patient-year (95% CI: 0.10-0.14) versus 0.20 (95%CI: 0.15-0.26) (p=0.002), as it was the rate of untreated arrhythmias – 0.12/patient-year (95% CI: 0.11-0.14) versus 0.23 (95%CI: 0.18-0.30) (p=0.001). The enhanced SMART Pass version was associated with a lower rate of deactivations – 0.04/patient-year (95%CI: 0.02-0.05) versus 0.14 (95% CI: 0.12-0.16) (p<0.001), and with a reduction in treated and untreated arrhythmias (Incidence rate ratios: 0.40 (95%CI: 0.28-0.53) and 0.40 (95%CI: 0.30-0.55), respectively (p<0.001)). Conclusions In current clinical practice, S-ICD implantation centers often program high cut-off rates for arrhythmia detection. Most patients have SMART Pass consistently enabled during follow-up. This is associated with significantly lower rates of detected and treated arrhythmias. The enhanced SMART Pass version seems associated with lower deactivation rate, and with further decrease in treated and untreated detected arrhythmias.

A review of signal processing techniques for electrocardiogram (ECG) signals

The accurate diagnosis of cardiovascular conditions relies heavily on Electrocardiogram (ECG) signals, yet persistent interference challenges, including baseline wander, powerline interference, and muscle artifacts, compromise clinical accuracy. This study comprehensively explores cutting-edge preprocessing techniques aimed at addressing multifaceted challenges in ECG signal processing. Investigating noise removal, baseline correction, feature extraction, arrhythmia detection, and heart rate variability (HRV) analysis, we synthesize insights from recent research to provide a thorough understanding of current state-of-the-art methodologies. Each facet plays a crucial role in enhancing the reliability of ECG signals for accurate cardiovascular diagnoses. In a landscape where clinical accuracy is paramount, this review critically assesses advancements in signal processing techniques, shedding light on innovative strategies and potential breakthroughs.

Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection.

Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low-power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life-threatening arrhythmias. Incorporated with a simplified spike-timing-dependent plasticity learning rule, the CIM architecture is suitable for robust, area- and energy-efficient in-memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.

A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

Long-term continuous monitoring of arrhythmias in pigs with insertable cardiac monitors

Arrhythmia detection is essential when assessing the safety of novel drugs and therapies in preclinical studies. Many short-term arrhythmia monitoring methods exist, including non-invasive ECG and Holter. However, there are no reliable, long-term, non-invasive, or minimally invasive methods for cardiac arrhythmia follow-up in large animals that allows free movement with littermates. A long follow-up time is needed when estimating the impact of long-lasting drugs or therapies, such as gene therapy. We evaluated the feasibility and performance of insertable cardiac monitors (ICMs) in pigs for minimally invasive, long-term monitoring of cardiac arrhythmias that allows free movement and species-specific behavior. Multiple implantation sites were tested to assess signal quality. ICMs recognized reliably many different arrhythmias but failed to detect single extrasystoles. They also over-diagnosed T-waves, resulting in oversensing. Muscle activity and natural startles of the animals caused noise, leading to a heterogeneous signal requiring post-recording evaluation. In spite of these shortcomings, the ICMs showed to be very useful for minimally invasive long-term monitoring of cardiac rhythm in pigs.

A multimodal deep learning tool for detection of junctional ectopic tachycardia in children with congenital heart disease

Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease. It has a marked resemblance to normal sinus rhythm, often leading to delay in diagnosis and management. The study sought to develop a novel multimodal automated arrhythmia detection tool that outperforms existing JET detection tools. This is a cohort study performed on 40 patients with congenital heart disease at Texas Children's Hospital. Electrocardiogram and central venous pressure waveform data produced by bedside monitors are captured by the Sickbay platform. Convolutional neural networks (CNNs) were trained to classify each heartbeat as either normal sinus rhythm or JET based only on raw electrocardiogram signals. Our best model improved the area under the curve from 0.948 to 0.952 and the true positive rate at 5% false positive rate from 71.8% to 80.6%. Using a 3-model ensemble further improved the area under the curve to 0.953 and the true positive rate at 5% false positive rate to 85.2%. Results on a subset of data show that adding central venous pressure can significantly improve area under the receiver-operating characteristic curve from 0.646 to 0.825. This study validates the efficacy of deep neural networks to notably improve JET detection accuracy. We have built a performant and reliable model that can be used to create a bedside alarm that diagnoses JET, allowing for precise diagnosis of this life-threatening postoperative arrhythmia and prompt intervention. Future validation of the model in a larger cohort is needed.

Extravascular Implantable Cardioverter-Defibrillator Sensing and Detection in a Large Global Population

BackgroundThe extravascular (EV) implantable cardioverter-defibrillator (ICD) includes features to address sensing and arrhythmia detection challenges presented by its substernal lead location. ObjectivesIn this study, the authors sought to evaluate sensing and detection performance in 299 patients discharged with an EV-ICD in the global pivotal study. MethodsWe reviewed and adjudicated all induced ventricular fibrillation (VF) episodes and spontaneous device-stored episodes that satisfied rate and duration criteria in a programmed ventricular tachycardia (VT)/VF therapy zone. ResultsAt implantation, all EV-ICDs detected induced VF at the programmed sensitivity; 95.9% detected VF with a 3× safety margin. In follow-up, EV-ICDs detected all 59 VT/VF episodes that sustained until therapy. Of 1,034 non-VT/VF episodes, oversensing caused 87.9% and supraventricular tachycardia caused 12.1%. Therapy was withheld in 80.9%, aborted in 10.6%, and delivered in 8.5%. The most common causes of oversensing were myopotentials (61.2%) and P-wave oversensing (PWOS) (19.9%). Inappropriate shocks occurred in only 3.2% of myopotential episodes, but in 21.8% of PWOS episodes. Myopotential oversensing was more common with Ring-Can sensing (P < 0.0001) and correlated with low R-wave amplitude (P < 0.0001). PWOS occurred almost exclusively with Ring1-Ring2 sensing (P = 0.0001) and began with transient decrease in R-wave or increase in P-wave amplitude (P < 0.0001). In software emulation, a new PWOS discriminator significantly reduced total inappropriate detections. ConclusionsIn a global population, EV-ICD detected induced and spontaneous VT/VF accurately. Although discriminators withheld detection from most non-VT/VF episodes, inappropriate shocks were common. The most common cause was PWOS, which may be reduced by optimizing sensing at implantation and incorporation of the PWOS discriminator, which is now in the current device. (Extravascular ICD Pivotal Study [EV ICD]; NCT04060680)

Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification.

Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers. This paper presents a novel deep representation learning method for the efficient detection of arrhythmic beats. To mitigate the issues associated with the imbalanced data distribution, a novel re-sampling strategy is introduced. Unlike the existing oversampling methods, the proposed technique transforms majority-class samples into minority-class samples with a novel translation loss function. This approach assists the model in learning a more generalized representation of crucially important minority class samples. Moreover, by exploiting an auxiliary feature, an augmented attention module is designed that focuses on the most relevant and target-specific information. We adopted an inter-patient classification paradigm to evaluate the proposed method. The experimental results of this study on the MIT-BIH arrhythmia database clearly indicate that the proposed model with augmented attention mechanism and over-sampling strategy significantly learns a balanced deep representation and improves the classification performance of vital heartbeats.

ECG Arrhythmia Classification using Deep Learning

An electrocardiogram (ECG) is a painless, noninvasive way to help diagnose numerous common heart problems. ECG plays an important role in diagnosing various Cardiac ailments. In recent years, Deep learning techniques have shown remarkable promise in achieving accurate and automated ECG arrhythmia classification. The primary goal of the system is to develop a robust and accurate system for the automated detection and classification of arrhythmias in electrocardiogram (ECG) data. By leveraging state-of-the-art techniques such as Convolutional Neural Networks (CNNs), we analyze pattern recognition within ECG signals to detect arrhythmias. Furthermore, we address the challenge of dataset scarcity by augmenting the data through nine different image cropping methods during the training phase. The implementation of techniques like Batch Normalization and data augmentation will further enhance the model's adaptability to diverse data sources, making it an invaluable tool for healthcare professionals. The CNN will be trained and tested using the ECG Dataset obtained from the MIT-BIH Database and from it, seven types of signals of arrhythmia will be classified. These seven signals are Premature Ventricular contractions (PVC), Paced beat (PAB), Right bundle branch block beat (RBB), Left bundle branch block beat (LBB), Atrial premature contraction (APC), Ventricular escape beat (VEB) and Normal beat. This system bridges the gap between advanced technology and healthcare, offering a transformative approach to ECG arrhythmia classification that has the potential to significantly improve patient outcomes and reduce the burden of manual diagnosis

Seven- and thirty-day mortality in digoxin poisoning: Results from the DIGITOX study

BackgroundDigoxin poisonings are relatively common and potentially fatal, requiring immediate therapeutic intervention, with special attention to the patient's hemodynamic status and the presence of electrocardiographic and electrolytic disturbances. ObjectiveTo identify factors associated with seven-day and thirty-day mortality in digoxin poisoning. Design, settings and participantsA retrospective, observational, multicenter study was conducted across 15 Hospital Emergency Departments (HED) in Spain. All patients over 18 years of age who presented to participating HEDs from 2015 to 2021 were included. The inclusion criteria encompassed individuals meeting the criteria for digoxin poisoning, whether acute or chronic. Outcomes measure and analysisTo identify independent factors associated with 7-day and 30-day mortality, a multivariate analysis was conducted. This analysis included variables of clinical significance, as well as those exhibiting a trend (p < 0.1) or significance in the bivariate analysis. Main findingsA total of 658 cases of digoxin poisoning were identified. Mortality rates were 4.5% (30 patients) at seven days and 11.1% (73 patients) at thirty days. Regarding 7-day mortality, the mean age of deceased patients was comparable to survivors (84.7 (8.9) vs 83.9 (7.9) years; p = ns). The multivariate analysis revealed that factors independently associated with 7-day mortality encompassed the extent of dependence assessed by the Barthel Index (BI 60–89 OR 0.28; 95% CI 0.10–0.77; p = 0.014 and BI>90 OR 0.22; 95% CI 0.08–0.63; p = 0.005), the identification of ventricular arrhythmias (OR 1.34; 95% CI 1.34–25.21; p = 0.019), and the presence of circulatory (OR 2.84; 95% CI 1.19–6.27; p = 0.019) and neurological manifestations (OR 2.67; 95% CI 1.13–6.27; p = 0.025). Factors independently associated with 30-day mortality encompassed extent of dependence (BI 60–89 OR 0.37; 95% CI 0.20–0.71; p = 0.003 and BI>90 OR 0.18; 95% CI 0.09–0.39; p < 0.001) and the identification of circulatory (OR 2.13; 95% CI 1.10–4.15; p = 0.025) and neurological manifestations (OR 2.39; 95% CI 1.25–3.89; p = 0.006). ConclusionsThe study identifies the degree of dependency assessed by the Barthel Index and the presence of cardiovascular and neurological symptoms as independent predictors of both 7-day and 30-day mortality. Additionally, the detection of ventricular arrhythmia is also an independent factor for 7-day mortality.

Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythmia. In pursuit of increasing the accuracy of automated methods, many studies have been conducted. However, in none of the previous articles, the relationship and structure between the heart leads have not been included in the model. It seems that the structure of ECG data can help develop the accuracy of arrhythmia detection. Therefore, in this study, a new structure of Electrocardiogram (ECG) data was introduced, and the Graph Convolution Network (GCN), which has the possibility of learning the structure, was used to develop the accuracy of cardiac arrhythmia diagnosis. Considering the relationship between the heart leads and clusters based on different ECG poles, a new structure was introduced. In this structure, the Mutual Information(MI) index was used to evaluate the relationship between the leads, and weight was given based on the poles of the leads. Weighted Mutual Information (WMI) matrices (new structure) were formed by R software. Finally, the 15-layer GCN network was adjusted by this structure and the arrhythmia of people was detected and classified by it. To evaluate the performance of the proposed new network, sensitivity, precision, specificity, accuracy, and confusion matrix indices were used. Also, the accuracy of GCN networks was compared by three different structures, including WMI, MI, and Identity. Chapman’s 12-lead ECG Dataset was used in this study. The results showed that the values of sensitivity, precision, specificity, and accuracy of the GCN-WMI network with 15 intermediate layers were equal to 98.74%, 99.08%, 99.97% & 99.82%, respectively. This new proposed network was more accurate than the Graph Convolution Network-Mutual Information (GCN-MI) with an accuracy equal to 99.71% and GCN-Id with an accuracy equal to 92.68%. Therefore, utilizing this network, the types of arrhythmia were recognized and classified. Also, the new network proposed by the Graph Convolution Network-Weighted Mutual Information (GCN-WMI) was more accurate than those conducted in other studies on the same data set (Chapman). Based on the obtained results, the structure proposed in this study increased the accuracy of cardiac arrhythmia diagnosis and classification on the Chapman data set. Achieving such accuracy for arrhythmia diagnosis is a great achievement in clinical sciences.

Detection of Arrhythmias Using Smartwatches-A Systematic Literature Review.

Smartwatches represent one of the most widely adopted technological innovations among wearable devices. Their evolution has equipped them with an increasing array of features, including the capability to record an electrocardiogram. This functionality allows users to detect potential arrhythmias, enabling prompt intervention or monitoring of existing arrhythmias, such as atrial fibrillation. In our research, we aimed to compile case reports, case series, and cohort studies from the Web of Science, PubMed, Scopus, and Embase databases published until 1 August 2023. The search employed keywords such as "Smart Watch", "Apple Watch", "Samsung Gear", "Samsung Galaxy Watch", "Google Pixel Watch", "Fitbit", "Huawei Watch", "Withings", "Garmin", "Atrial Fibrillation", "Supraventricular Tachycardia", "Cardiac Arrhythmia", "Ventricular Tachycardia", "Atrioventricular Nodal Reentrant Tachycardia", "Atrioventricular Reentrant Tachycardia", "Heart Block", "Atrial Flutter", "Ectopic Atrial Tachycardia", and "Bradyarrhythmia." We obtained a total of 758 results, from which we selected 57 articles, including 33 case reports and case series, as well as 24 cohort studies. Most of the scientific works focused on atrial fibrillation, which is often detected using Apple Watches. Nevertheless, we also included articles investigating arrhythmias with the potential for circulatory collapse without immediate intervention. This systematic literature review provides a comprehensive overview of the current state of research on arrhythmia detection using smartwatches. Through further research, it may be possible to develop a care protocol that integrates arrhythmias recorded by smartwatches, allowing for timely access to appropriate medical care for patients. Additionally, continuous monitoring of existing arrhythmias using smartwatches could facilitate the assessment of the effectiveness of prescribed therapies.

Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction

Cardiovascular diseases are a pervasive global health concern, contributing significantly to morbidity and mortality rates worldwide. Among these conditions, arrhythmia, characterized by irregular heart rhythms, presents formidable diagnostic challenges. This study introduces an innovative approach utilizing deep learning techniques, specifically Convolutional Neural Networks (CNNs), to address the complexities of arrhythmia classification. Leveraging multi-lead Electrocardiogram (ECG) data, our CNN model, comprising six layers with a residual block, demonstrates promising outcomes in identifying five distinct heartbeat types: Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat. Through rigorous experimentation, we highlight the transformative potential of our methodology in enhancing diagnostic accuracy for cardiovascular arrhythmias. Arrhythmia diagnosis remains a critical challenge in cardiovascular care, often relying on manual interpretation of ECG signals, which can be time-consuming and prone to subjectivity. To address these limitations, we propose a novel approach that leverages deep learning algorithms to automate arrhythmia classification. By employing advanced CNN architectures and multi-lead ECG data, our methodology offers a robust solution for precise and efficient arrhythmia detection. Through comprehensive evaluation, we demonstrate the effectiveness of our approach in facilitating more accurate clinical decision-making, thereby improving patient outcomes in managing cardiovascular arrhythmias.

Arrhythmias and ion channelopathies causing sudden cardiac death in Hispanic/Latino and Indigenous populations.

The limited literature and increasing interest in studies on cardiac electrophysiology, explicitly focusing on cardiac ion channelopathies and sudden cardiac death in diverse populations, has prompted a comprehensive examination of existing research. Our review specifically targets Hispanic/Latino and Indigenous populations, which are often underrepresented in healthcare studies. This review encompasses investigations into genetic variants, epidemiology, etiologies, and clinical risk factors associated with arrhythmias in these demographic groups. The review explores the Hispanic paradox, a phenomenon linking healthcare outcomes to socioeconomic factors within Hispanic communities in the United States. Furthermore, it discusses studies exemplifying this observation in the context of arrhythmias and ion channelopathies in Hispanic populations. Current research also sheds light on disparities in overall healthcare quality in Indigenous populations. The available yet limited literature underscores the pressing need for more extensive and comprehensive research on cardiac ion channelopathies in Hispanic/Latino and Indigenous populations. Specifically, additional studies are essential to fully characterize pathogenic genetic variants, identify population-specific risk factors, and address health disparities to enhance the detection, prevention, and management of arrhythmias and sudden cardiac death in these demographic groups.

Analysing and Evaluating the Performance of Deep-Learning-Based Arrhythmia Detection Using Electrocardiogram Signals

Cardiac arrhythmia is a cardiac irregularity that impacts a significant number of individuals globally. Certain arrhythmias may be benign or occur only once, while recurring arrhythmias have the potential to cause organ failure, increase the risk of stroke by a factor of five, and even lead to sudden cardiac death. Thus, in order to identify and treat arrhythmia and prevent potentially fatal cardiac problems, the rapid detection and categorization of Electrocardiogram (ECG) signals is of paramount importance. The non-invasive technique employs electrodes to examine the electrical potentials of the heart, facilitating the detection of structural and functional irregularities that contribute to the diagnosis of cardiovascular illnesses. Nevertheless, the high likelihood of manual interpretation, which is both time-consuming and susceptible to error caused by weariness, poses a significant challenge for cardiologists in identifying and diagnosing cardiac issues. In recent times, there has been a growing utilization of Deep Learning (DL) models in the field of arrhythmia prediction, with the aim of enhancing clinical decision-making and potentially mitigating the likelihood of valetudinarian fatality. These models enhance the diagnostic capabilities of electrocardiography by detecting pathological situations, extracting anatomically meaningful data, evaluating cardio-motion, and assessing the quality of echo images. As a result, they serve as an alternative tool for precise diagnosis and treatment of arrhythmias. In order to forecast and categorize arrhythmias based on ECG signal data, this study provides a comprehensive review of various deep learning approaches. The initial section of the study provides a concise overview of various deep learning-based prediction models that have been developed by multiple researchers for echocardiography systems. Subsequently, a comparative analysis is undertaken to comprehend the limitations of those algorithms and propose a novel approach to improve the accuracy of cardiac view classification in echocardiography systems.

A Smartphone-Based M-Health Monitoring System for Arrhythmia Diagnosis.

Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals. In order to train its two generators and two discriminators simultaneously, we explore an unsupervised pre-training strategy to initialize the generator and accelerate the convergence speed during training. Second, we propose an arrhythmia diagnosis model based on the time convolution network (TCN). This model can identify 34 common arrhythmia events using eight-lead ECG signals, and we deploy such a model on the Android platform to develop an at-home ECG monitoring system. Experimental results have demonstrated that our approach outperforms the existing noise reduction methods and arrhythmia diagnosis models in terms of denoising effect, recognition accuracy, model size, and operation speed, making it more suitable for deployment on mobile devices for m-health monitoring services.

Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm for Fetal Arrhythmia Detection and Classification from ECG Signals

Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm for Fetal Arrhythmia Detection and Classification from ECG Signals