Search Results (198)

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

The electrocardiogram (ECG) is one of the vital physiological signals for human health. Lightweight neural network (NN) models integrated into a low-resource wearable device can benefit the user with a low-power, real-time edge computing system for continuous and daily monitoring. This work introduces a novel edge-computing wearable device for real-time beat-by-beat ECG arrhythmia classification. The proposed wearable integrates the light AI model into a 32-bit ARM^® Cortex^™-based custom printed circuit board (PCB). The work analyzes the performance of artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) models for real-time wearable implementation. The wearable is capable of real-time QRS detection and feature extraction from raw ECG data. The QRS detection algorithm offers high reliability with a 99.5% F1 score and R-peak position error (RPE) of 6.3 ms for R-peak-to-R-peak intervals. The proposed method implements a combination of top time series, spectral, and signal-specific features for model development. Lightweight, pretrained models are deployed on the custom wearable and evaluated in real time using mock data from the MIT-BIH dataset. We propose an LSTM model that provides efficient performance over accuracy, inference latency, and memory consumption. The proposed model offers 98.1% accuracy, with 98.2% sensitivity and 99.5% specificity while testing in real time on the wearable. Real-time inferencing takes 20 ms, and the device consumes as low as 5.9 mA of power. The proposed method achieves efficient performance in real-time testing, which indicates the wearable can be effectively used for real-time continuous arrhythmia detection. Full article

Accurate classification of electrocardiogram (ECG) signals is vital for reliable arrhythmia diagnosis and informed clinical decision-making, yet real-world datasets often suffer severe class imbalance that degrades recall and F1-score. To address these limitations, we introduce MAK-Net, a hybrid deep learning framework that combines: (1) a four-branch multiscale convolutional module for comprehensive feature extraction across diverse waveform morphologies; (2) an efficient channel attention mechanism for adaptive weighting of clinically salient segments; (3) bidirectional gated recurrent units (BiGRU) to capture long-range temporal dependencies; and (4) Kolmogorov–Arnold Network (KAN) layers with learnable spline activations for enhanced nonlinear representation and interpretability. We further mitigate imbalance by synergistically applying focal loss and the Synthetic Minority Oversampling Technique (SMOTE). On the MIT-BIH arrhythmia database, MAK-Net attains state-of-the-art performance—0.9980 accuracy, 0.9888 F1-score, 0.9871 recall, 0.9905 precision, and 0.9991 specificity—demonstrating superior robustness to imbalanced classes compared with existing methods. These findings validate the efficacy of multiscale feature fusion, attention-guided learning, and KAN-based nonlinear mapping for automated, clinically reliable arrhythmia detection. Full article

ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices. Full article

Cross-database electrocardiogram (ECG) classification remains a critical challenge due to variations in patient populations, recording conditions, and annotation granularity. Existing methodologies for ECG arrhythmia classification have primarily utilized datasets with either fine-grained or coarse-grained labels, but seldom both simultaneously. Fine-grained labels provide beat-level annotations, whereas coarse-grained labels offer only record-level labels. In this study, we propose an innovative cross-database learning framework that utilizes both fine-grained and coarse-grained labels in tandem, thereby enhancing classification performance across heterogeneous datasets. Specifically, our approach begins with the pretraining of a CNN-based beat classifier that takes ECG signals as the input and predicts beat types on a finely labeled dataset, namely the MIT-BIH Arrhythmia Database (MITDB). The pretrained model is then fine-tuned using weakly supervised learning on two coarsely labeled datasets: the SPH one, which contains four rhythm classes, and the PTB-XL one, which involves binary classification between the sinus rhythm (SR) and atrial fibrillation (AFIB). Once the beat classifier is adapted to a new dataset, it generates a two-dimensional beat-score-map (BSM) representation from the input ECG signal. This 2D BSM is subsequently utilized as the input for arrhythmia rhythm classification. The proposed method achieves F1 scores of 0.9301 on the SPH dataset and 0.9267 on the PTB-XL dataset, corresponding to the multi-class and binary rhythm classification tasks described above. These results demonstrate a robust cross-database classification of complex cardiac arrhythmia rhythms. Furthermore, t-SNE visualizations of the 2D BSM representations, after adaptation to the coarsely labeled SPH and PTB-XL datasets, validate how our method significantly enhances the ability to differentiate between various arrhythmia rhythm types, thus highlighting its effectiveness in cross-database ECG analysis. Full article

With the widespread application of machine learning techniques in time series analysis, the interpretability of models trained on time series data has attracted increasing attention. Most existing explanation methods are based on time-domain features, making it difficult to reveal how complex models focus on time–frequency information. To address this, this paper proposes a time–frequency domain-based time series interpretation method aimed at enhancing the interpretability of models at the time–frequency domain. This method extends the traditional LIME algorithm by combining the ideas of short-time Fourier transform (STFT), inverse STFT, and local interpretable model-agnostic explanations (LIME), and introduces a self-designed TFHS (time–frequency homogeneous segmentation) algorithm. The TFHS algorithm achieves precise homogeneous segmentation of the time–frequency matrix through peak detection and clustering analysis, incorporating the distribution characteristics of signals in both frequency and time dimensions. The experiment verified the effectiveness of the TFHS algorithm on Synthetic Dataset 1 and the effectiveness of the TF-LIME algorithm on Synthetic Dataset 2, and then further evaluated the interpretability performance on the MIT-BIH dataset. The results demonstrate that the proposed method significantly improves the interpretability of time-series models in the time–frequency domain, exhibiting strong generalization capabilities and promising application prospects. Full article

Atrial fibrillation (AF) is a type of cardiac arrhythmia with a worldwide prevalence of more than 37 million among the adult population. This elusive disease is a major risk factor for ischemic stroke, along with increased rates of significant morbidity and eventual mortality. It is clinically diagnosed using medical-grade electrocardiogram (ECG) sensors in ambulatory settings. The recent emergence of consumer-grade wearables equipped with photoplethysmography (PPG) sensors has exhibited considerable promise for non-intrusive continuous monitoring in free-living conditions. However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). We derive and feed heart rate variability (HRV) and pulse rate variability (PRV) features as auxiliary inputs to the framework for robustness. A larger teacher model is trained using the MIT-BIH Arrhythmia ECG dataset. Through transfer learning (TL), its learned representation is adapted to a compressed student model (32x smaller) variant by using knowledge distillation (KD) for classifying AF with the UMass and MIMIC-III datasets of PPG signals. This results in the student model yielding average improvements in accuracy, sensitivity, F1 score, and Matthews correlation coefficient of 2.0%, 15.05%, 11.7%, and 9.85%, respectively, across both PPG datasets. Additionally, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to confer a notion of interpretability to the model decisions. We conclude that through a combination of techniques such as TL and KD, i.e., pre-trained initialization, we can utilize learned ECG concepts for scarcer PPG scenarios. This can reduce resource usage and enable deployment on edge devices. Full article

In real-world scenarios, ECG data are collected from a diverse range of heterogeneous devices, including high-end medical equipment and consumer-grade wearable devices, each with varying computational capabilities and constraints. This heterogeneity presents significant challenges in developing a highly accurate deep learning (DL) global model for ECG classification, as traditional centralized approaches struggle to address privacy concerns, scalability issues, and model inconsistencies arising from diverse device characteristics. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without sharing raw data, thus preserving privacy and security. However, standard FL assumes uniform device capabilities and model architectures, which is impractical given the varied nature of ECG data collection devices. Although heterogeneity has been explored in other domains, its impact on ECG classification and the classification of similar time series physiological signals remains underexplored. In this study, we adopted HeteroFL, a technique that enables model heterogeneity to reflect real-world resource constraints. By allowing local models to vary in complexity while aggregating their updates, HeteroFL accommodates the computational diversity of different devices. This study evaluated the applicability of HeteroFL for ECG classification using the MIT-BIH Arrhythmia dataset, identifying both its strengths and limitations. Our findings establish a foundation for future research on improving FL strategies for heterogeneous medical data, highlighting areas for further optimization and adaptation in real-world deployments. Full article

Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional arrhythmia detection methods are time-consuming, error-prone, and often subjective, making it difficult for doctors to discern between distinct patterns of arrhythmia. To understand ECG signals, this study presents a multi-class classifier and an autoencoder with long short-term memory (LSTM) network layers for extracting signal properties on a dataset from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital (MIT-BIH). The suggested model had an accuracy rate of 98.57% on the arrhythmia dataset and 97.59% on the supraventricular dataset. In contrast to other deep learning models, the proposed model eliminates the problem of the gradient disappearing in classification tasks. Full article

This paper seeks to solve the classification problem of cardiac arrhythmias by using a small number of electrocardiogram (ECG) recordings. To offer a reasonable solution to this problem, a technique that combines a common matrix approach (CMA)-based classifier model with the Fourier decomposition method (FDM) is proposed. The FDM is responsible for generating time–frequency (T-F) representations of ECG recordings. The classification process is performed with feature images applied as input to the classifier model. The feature images are obtained after two-dimensional principal component analysis (2DPCA) of data matrices related to ECG recordings. Each data matrix is created by concatenating the ECG record itself, the Fourier transform, and the T-F representation on a single matrix. To verify the efficacy of the proposed method, various experiments are conducted with the MIT-BIH, Chapman, and PTB-XL databases. In the assessments using the MIT-BIH database under the inter-patient paradigm, we achieved a mean overall accuracy rate of 99.81%. The proposed method outperforms the majority of recent efforts, yielding rates exceeding 99% on nearly five performance metrics for the recognition of V- and S-class arrhythmias. It is found that, in the classification of four types of arrhythmias using ECG recordings from the Chapman database, our model surpasses recent works by reaching mean overall accuracy rates of 99.76% and 99.45% for the raw and de-noised ECG recordings, respectively. Similarly, five different forms of arrhythmias from the PTB-XL database were recognized with a mean overall accuracy of 98.71%. Full article

To address the need for accurate classification of electrocardiogram (ECG) signals, we employ an interpretable KAN to classify arrhythmia diseases. Experimental evaluation of the MIT-BIH and PTB datasets demonstrates the significant superiority of the KAN in classifying arrhythmia diseases. Specifically, preprocessing steps such as sample balancing and variance sorting effectively optimized the feature distribution and significantly enhanced the model’s classification performance. In the MIT-BIH, the KAN achieved classification accuracy and precision rates of 99.08% and 99.07%, respectively. Similarly, on the PTB dataset, both metrics reached 99.11%. In addition, experimental results indicate that compared to the traditional multi-layer perceptron (MLP), the KAN demonstrates higher classification accuracy and better fitting stability and adaptability to complex data scenarios. Applying three clustering methods demonstrates that the features extracted by the KAN exhibit clearer cluster boundaries, thereby verifying its effectiveness in ECG signal classification. Additionally, convergence analysis reveals that the KAN’s training process exhibits a smooth and stable loss decline curve, confirming its robustness under complex data conditions. The findings of this study validate the applicability and superiority of the KAN in classifying ECG signals for arrhythmia and other diseases, offering a novel technical approach to the classification and diagnosis of arrhythmias. Finally, potential future research directions are discussed, including the KAN in early warning and rapid diagnosis of arrhythmias. This study establishes a theoretical foundation and practical basis for advancing interpretable networks in clinical applications. Full article

Cardiovascular diseases (CVDs) are the primary cause of death worldwide. For accurate diagnosis of CVDs, robust and efficient ECG denoising is particularly critical in ambulatory cases where various artifacts can degrade the quality of the ECG signal. None of the present denoising methods preserve the morphology of ECG signals adequately for all noise types, especially at high noise levels. This study proposes a novel Fully-Gated Denoising Autoencoder (FGDAE) to significantly reduce the effects of different artifacts on ECG signals. The proposed FGDAE utilizes gating mechanisms in all its layers, including skip connections, and employs Self-organized Operational Neural Network (self-ONN) neurons in its encoder. Furthermore, a multi-component loss function is proposed to learn efficient latent representations of ECG signals and provide reliable denoising with maximal morphological preservation. The proposed model is trained and benchmarked on the QT Database (QTDB), degraded by adding randomly mixed artifacts collected from the MIT-BIH Noise Stress Test Database (NSTDB). The FGDAE showed the best performance on all seven error metrics used in our work in different noise intensities and artifact combinations compared with state-of-the-art algorithms. Moreover, FGDAE provides reliable denoising in extreme conditions and for varied noise compositions. The significantly reduced model size, 61% to 73% reduction, compared with the state-of-the-art algorithm, and the inference speed of the FGDAE model provide evident benefits in various practical applications. While our model performs best compared with other models tested in this study, more improvements are needed for optimal morphological preservation, especially in the presence of electrode motion artifacts. Full article

Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. Methods: We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. Results: These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. Conclusions: This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data. Full article

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal processing. We propose a dual-channel feature fusion strategy designed to enhance the precision and objectivity of ECG analysis. Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. Post-extraction, the features from both channels are fused and classified. When evaluated on the MIT-BIH database, our method achieves a classification accuracy of 97.80%. Compared to other methods, our approach of two-channel feature fusion has a significant improvement in overall performance by adding a 2D convolutional network. This methodology represents a substantial advancement in ECG signal processing, offering significant potential for clinical applications and improving patient care efficiency and accuracy. Full article

Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we developed an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks. Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. This model processes a triad of cardio cycles—the preceding, current, and following cycles—to capture temporal dependencies and hidden features related to arrhythmias. Third, we implemented an interpretation method that explains CNN’s decisions using clinically relevant features, making the results understandable to clinicians. Using the MIT-BIH database, our approach achieved an accuracy of 99.43%, with F1-scores approaching 100% for major arrhythmia classes. The integration of these methods enhances both the performance and transparency of arrhythmia detection systems. Full article