Search Results (51)

Cardiovascular diseases are the leading cause of death worldwide, with arrhythmia being a prevalent and potentially fatal condition. The multi-lead electrocardiogram (ECG) is the primary tool for detecting arrhythmias. However, existing detection methods have shortcomings: they cannot dynamically integrate inter-lead correlations with multi-scale temporal changes in cardiac electrical activity. They also lack mechanisms to simultaneously focus on key leads and time segments, and thus fail to address multi-lead redundancy or capture comprehensive spatial-temporal relationships. To solve these problems, we propose a Multi-Scale Lead-Temporal Co-Attention Framework (MS-LTCAF). Our framework incorporates two key components: a Lead-Temporal Co-Attention Residual (LTCAR) module that dynamically weights the importance of leads and time segments, and a multi-scale branch structure that integrates features of cardiac electrical activity across different time periods. Together, these components enable the framework to automatically extract and integrate features within a single lead, between different leads, and across multiple time scales from ECG signals. Experimental results demonstrate that MS-LTCAF outperforms existing methods. On the PTB-XL dataset, it achieves an AUC of 0.927, approximately 1% higher than the current optimal baseline model (DNN_zhu’s 0.918). On the LUDB dataset, it ranks first in terms of AUC (0.942), accuracy (0.920), and F1-score (0.745). Furthermore, the framework can focus on key leads and time segments through the co-attention mechanism, while the multi-scale branches help capture both the details of local waveforms (such as QRS complexes) and the overall rhythm patterns (such as RR intervals). Full article

This study assesses how classical and modern features extracted from a single ECG lead (II) influence automated arrhythmia classification. Using the Large Scale 12-Lead Electrocardiogram Database for Arrhythmia Study and MATLAB^®, we compared traditional morphological measures (e.g., QRS duration, QT interval, atrial/ventricular rates) with advanced time-, frequency-, and nonlinear-domain descriptors. The method classifies ECGs into four or eight categories using 15–39 features, either automatically selected or combined. In the eight-class task, 29–39 features yielded 69% accuracy; in the four-class task, 15 MRMR-selected features achieved 94.2% accuracy. A key strength is efficiency: relying on a single lead reduces preprocessing, storage, and classification time by a factor of ~12 compared with 12-lead approaches. These findings show that advanced descriptors from a single lead can match multi-lead performance, enabling practical, scalable clinical applications. Full article

Cardiovascular diseases are known as major contributors to death globally. Accurate identification and classification of cardiac arrhythmias from electrocardiogram (ECG) signals is essential for early diagnosis and treatment of cardiovascular diseases. This research introduces an innovative deep learning architecture that integrates Convolutional Neural Networks with a channel attention mechanism, enhancing the model’s capacity to concentrate on essential aspects of the ECG signals. Unlike most prior studies that depend on single-lead data or complex hybrid models, this work presents a novel yet simple deep learning architecture to classify five arrhythmia classes that effectively utilizes both 2-lead and 12-lead ECG signals, providing more accurate representations of clinical scenarios. The model’s performance was evaluated on the MIT-BIH and INCART arrhythmia datasets, achieving accuracies of 99.18% and 99.48%, respectively, along with F1 scores of 99.18% and 99.48%. These high-performance metrics demonstrate the model’s ability to differentiate between normal and arrhythmic signals, as well as accurately identify various arrhythmia types. The proposed architecture ensures high accuracy without excessive complexity, making it well-suited for real-time and clinical applications. This approach could improve the efficiency of healthcare systems and contribute to better patient outcomes. Full article

Electrocardiogram (ECG) devices are commonly used to monitor heart rate (HR) and heart rate variability (HRV), but their signal quality under non-upright or torso-dominant activities may suffer due to motion artifact and interference from surrounding musculature. We compared ECG signal quality during treadmill walking, circuit training, and an obstacle course using three chest-worn commercial devices (Polar H10, Equivital EQ-02, and Zephyr BioHarness 3.0) and a multi-lead ECG monitor (BIOPAC). Signal quality was quantified using the beat signal quality index (SQI), and HR data were rejected if SQI fell below 0.7 or if values were physiologically implausible. Signal rejection rate was calculated as the proportion of low-quality observations across device and activity type. Significant effects of both device (p < 0.001) and activity (p < 0.001) were observed, with greater signal rejection during the obstacle course and circuit training compared to treadmill walking (p < 0.01). The Zephyr exhibited significantly higher rejection rates than the Polar (p = 0.018) and BIOPAC (p = 0.017), while the Polar showed lower average rejection rates across all activities. These findings underscore the importance of including dynamic, non-upright tasks in ECG validation protocols and suggest that certain commercial devices may be more robust under realistic conditions. Full article

The electrocardiogram (ECG), which records variations in surface electrical potential over time, has been widely used in the diagnosis of cardiovascular diseases. In recent years, the artificial intelligence (AI) + ECG paradigm has attracted considerable interest, but the two intrinsic characteristics of the ECG, namely, inter-lead correlations and multi-label classification, are often overlooked. Given that this oversight may constrain the full potential of AI models to enhance diagnostic performance, this study focuses on investigating methods for fusing information from a 12-lead ECG. A series of comprehensive experiments was conducted to evaluate the performance of various lead configurations, that is, 1-, 3-, 6-, 9-, and 12-lead combinations, with different fusion strategies. Innovatively integrating medical theory, we propose a novel five-lead-grouping strategy and develop a neural network architecture named Lead-5-Group Net (L5G-Net). After ranking the 12 leads with the AUC, we found that the aVR, V5, and V6 leads are particularly informative for single-lead ECG diagnosis. Furthermore, in multi-lead ECG classification, adopting an orthogonal lead-selection strategy which is based on the hypothesis of spatial interdependence among ECG leads was shown to enhance performance by ensuring that the information provided by each lead is complementary. Finally, the proposed L5G-Net demonstrates outstanding performance, achieving a macro-AUC of 0.9357 on the PTB-XL multi-label dataset without the use of data augmentation, attention mechanisms, or other strategies. Furthermore, considerable performance gains were observed after the five-lead-grouping strategy was applied to DenseNet and ResNet. These results imply that the proposed strategy can be seamlessly integrated into various network architectures and considerably enhance performance. Full article

This paper introduces a reverse demand response scheme that uses electric vehicles in an isolated microgrid system, aiming to solve the renewable energy curtailment issue. We focus on an off-grid system where the system operator faces a stabilization problem due to surplus energy production, while electric vehicles seek to charge energy at a lower price. In our system model, the operator determines the incentive to encourage more charging facilities and electric vehicles to participate in the reverse demand response program. Charging facilities, acting as brokers, use a portion of these incentives to further encourage electric vehicle engagement. Electric vehicles follow the decisions made by the broker and system operator to determine their charging strategy within the system. Consequently, charging energy and incentives are allocated to the electric vehicles in proportion to their decisions. The paper investigates the economic benefits of individual participants and the contribution of power stabilization by implementing a hierarchical decision-making heterogeneous multi-leaders multi-followers Stackelberg game. By demonstrating the existence of a unique Nash Equilibrium, we show the effectiveness of the proposed model in an isolated microgrid environment. Full article

Ventricular arrhythmias (VAs) are critical cardiovascular diseases that require rapid and accurate detection. Conventional approaches relying on multi-lead ECG or deep learning models have limitations in computational cost, interpretability, and real-time applicability on wearable devices. To address these issues, a lightweight and interpretable framework based on multiple complex networks was proposed for the detection of life-threatening VAs using short-term single-lead ECG signals. The input signals were decomposed using the fixed-frequency-range empirical wavelet transform, and sub-bands were subsequently analyzed through multiscale visibility graphs, recurrence networks, cross-recurrence networks, and joint recurrence networks. Eight topological features were extracted and input into an XGBoost classifier for VA identification. Ten-fold cross-validation results on the MIT-BIH VFDB and CUDB databases demonstrated that the proposed method achieved a sensitivity of 99.02 ± 0.53%, a specificity of 98.44 ± 0.43%, and an accuracy of 98.73 ± 0.02% for 10 s ECG segments. The model also maintained robust performance on shorter segments, with 97.23 ± 0.76% sensitivity, 98.85 ± 0.95% specificity, and 96.62 ± 0.02% accuracy on 2 s segments. The results outperformed existing feature-based and deep learning approaches while preserving model interpretability. Furthermore, the proposed method supports mobile deployment, facilitating real-time use in wearable healthcare applications. Full article

We introduce a lightweight 1D ConvNeXtV2–based neural network for the robust detection of atrial fibrillation (AFib) and atrial flutter (AFL) from single-lead ECG signals. Trained on multiple public datasets (Icentia11k, CPSC-2018/2021, LTAF, PTB-XL, PCC-2017) and evaluated on MIT-AFDB, MIT-ADB, and NST, our model attained a state-of-the-art F1-score of 0.986 on MIT-AFDB. With only 770 k parameters and 46 MFLOPs per 10 s window, the network remained computationally efficient. Guided Grad-CAM visualizations confirmed attention to clinically relevant P-wave morphology and R–R interval irregularities. This interpretable architecture is, therefore, well-suited for deployment in resource-constrained wearable or bedside monitors. Future work will extend this framework to multi-lead ECGs and a broader spectrum of arrhythmias. Full article

Background/Objectives: Accurate and efficient diagnosis of heart disease through electrocardiogram (ECG) analysis remains a critical challenge in clinical practice due to noise interference, morphological variability, and the complexity of overlapping cardiac signals. Methods: This study presents a comprehensive deep learning (DL) framework that integrates advanced ECG signal segmentation with transfer learning-based classification, aimed at improving diagnostic performance. The proposed ECG segmentation algorithm introduces a distinct and original approach compared to prior research by integrating adaptive preprocessing, histogram-based lead separation, and robust point-tracking techniques into a unified framework. While most earlier studies have addressed ECG image processing using basic filtering, fixed-region cropping, or template matching, our method uniquely focuses on automated and precise reconstruction of individual ECG leads from noisy and overlapping multi-lead images—a challenge often overlooked in previous work. This innovative segmentation strategy significantly enhances signal clarity and enables the extraction of richer and more localized features, boosting the performance of DL classifiers. The dataset utilized in this work of 12 lead-based standard ECG images consists of four primary classes. Results: Experiments conducted using various DL models—such as VGG16, VGG19, ResNet50, InceptionNetV2, and GoogleNet—reveal that segmentation notably enhances model performance in terms of recall, precision, and F1 score. The hybrid VGG19 + SVM model achieved 98.01% and 100% accuracy in multi-class classification, along with average accuracies of 99% and 97.95% in binary classification tasks using the original and reconstructed datasets, respectively. Conclusions: The results highlight the superiority of deep, feature-rich models in handling reconstructed ECG signals and confirm the value of segmentation as a critical preprocessing step. These findings underscore the importance of effective ECG segmentation in DL applications for automated heart disease diagnosis, offering a more reliable and accurate solution. Full article

At the core of AI-driven electrocardiogram diagnosis lies the precise localization of the QRS complex. While QRS detection methods for multiple leads have been researched adequately in the last few decades, their multi-lead strategies still need to be designed manually. Therefore, a QRS detector that can fuse multiple leads automatically is still worth investigating. Methods: The proposed QRS detector comprises a leads-distillation module (LDM) and a QRS detection module. The LDM can distill multi-lead signals into single-lead ones. This procedure minimizes the weight proportions assigned to noisy leads, enabling the network to generate a novel signal that facilitates the recognition of QRS waves. The QRS detection module, utilizing U-Net, is capable of discerning QRS complexes from the novel signal. Results: Our method demonstrates outstanding performance with a parameter count of only 5216. It achieves an excellent F1 score of 99.83 on the MITBIHA database and 99.77 on the INCART database, specifically in the inter-patient pattern. In the cross-database pattern, our approach maintains a strong performance with an F1 score of 99.22 on the INCART database and an F1 score of 99.09 on the MITBIHA database. Conclusion: Our method provides a novel idea for universal multi-lead QRS detection. It possesses advantages, such as reduced computational parameters, enhanced precision, and heightened compatibility. Significance: Our method canceled the repeated deployment of the QRS detection function to different lead configurations in the electrocardiogram (ECG) diagnostic system. Moreover, the scaling operation may become a simple tool to decrease the computational load of the network. Full article

The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (RPM) programs to optimize patient outcomes while reducing healthcare costs. This paper examines the role of wearable devices with electrocardiogram (ECG) capabilities for continuous cardiac monitoring, a crucial aspect for the timely detection and management of various cardiac conditions. The functionality of current wearable technology is scrutinized to determine its effectiveness in meeting clinical needs, employing a proposed ABCD guide (accuracy, benefit, compatibility, and data governance) for evaluation. While smartwatches show promise in detecting arrhythmias like atrial fibrillation, their broader diagnostic capabilities, including the potential for monitoring corrected QT (QTc) intervals during pharmacological interventions and approximating multi-lead ECG information for improved myocardial infarction detection, are also explored. Recent advancements in machine learning and deep learning for cardiac health monitoring are highlighted, alongside persistent challenges, particularly concerning signal quality and the need for further validation for widespread adoption in older adults and Hospital at Home settings. Ongoing improvements are necessary to overcome current limitations and fully realize the potential of wearable ECG technology in providing optimal care for high-risk patients. Full article

With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design. Full article

Background/Objectives: This study aims to address the critical need for accessible, early, and accurate cardiac di-agnostics, especially in resource-limited or remote settings. By shifting focus from traditional multi-lead ECG analysis to single-lead ECG data, this research explores the potential of advanced deep learning models for classifying cardiac conditions, including Nor-mal, Abnormal, Previous Myocardial Infarction (PMI), and Myocardial Infarction (MI). Methods: Five state-of-the-art deep learning architectures—Inception, DenseNet201, MobileNetV2, NASNetLarge, and VGG16—were systematically evaluated on individual ECG leads. Key performance metrics, such as model accuracy, inference time, and size, were analyzed to determine the optimal configurations for practical applications. Results: VGG16 emerged as the most accurate model, achieving an F1-score of 98.11% on lead V4 with a prediction time of 4.2 ms and a size of 528 MB, making it suitable for high-precision clinical settings. MobileNetV2, with a compact size of 13.4 MB, offered a balanced performance, achieving a 97.24% F1-score with a faster inference time of 3.2 ms, positioning it as an ideal candidate for real-time monitoring and telehealth applications. Conclusions: This study bridges a critical gap in cardiac diagnostics by demonstrating the feasibility of lightweight, scalable, single-lead ECG analysis using advanced deep learning models. The findings pave the way for deploying portable diagnostic tools across diverse settings, enhancing the accessibility and efficiency of cardiac care globally. Full article

Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection. The MLMCL method utilizes the multi-level feature representations of the encoder to perform multiple contrastive learning to fully exploit temporal consistency, channel consistency, and label consistency. Meanwhile, it combines labeled and unlabeled data for pre-training to obtain robust features for downstream tasks. In addition, it uses the domain knowledge in the field of AF diagnosis for domain knowledge augmentation to generate hard samples and improve the distinguishability of ECG representations. In the cross-dataset testing mode, MLMCL had better performance and good stability on different test sets, demonstrating its effectiveness and robustness in the AF detection task. The comparison results with existing studies show that MLMCL outperformed existing methods in external tests. The MLMCL method can be extended and applied to multi-lead scenarios and has reference significance for the development of contrastive learning methods for other arrhythmia. Full article

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. Full article