Search Results (567)

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person’s cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF–APA–Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals. Full article

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems. Full article

This paper explores the development of a Smart e-Textile Singlet designed to enhance geriatric care through continuous monitoring of vital health parameters. The proposed garment integrates various sensors to measure core body temperature, blood oxygen saturation, respiration rate, blood pressure, pulse, electrocardiogram (ECG), activity level, and risk of falls. Leveraging advanced technologies such as inertial measurement unit (IMU) sensors, thermoelectric materials, and piezoelectric fibers, the e-textile ensures both functionality and sustainability. Additionally, artificial intelligence algorithms are employed to provide near-real-time feedback and early warnings, significantly improving health management for elderly individuals. This innovative approach not only promotes autonomy and well-being among the elderly but also alleviates the workload of healthcare providers. The Smart e-Textile Singlet represents a multi-sensor solution by offering a holistic monitoring system. Full article

With the development of decentralized artificial intelligence (AI) networks, federated learning (FL) has received extensive attention for its ability to enable collaborative modeling without sharing raw data. However, existing methods are prone to convergence instability under non-independent and identically distributed (non-IID) conditions, lack robustness in adversarial settings, and have not yet sufficiently addressed fairness and incentive issues in multi-source heterogeneous environments. This paper proposes a Symmetry-Preserving Federated Learning (SPFL) framework that integrates blockchain auditing and fairness-aware incentive mechanisms. At the optimization layer, the framework employs group-theoretic regularization to maintain parameter symmetry and mitigate gradient conflicts; at the system layer, it leverages blockchain ledgers and smart contracts to verify and trace client updates; and at the incentive layer, it allocates rewards based on approximate Shapley values to ensure that the contributions of weaker clients are recognized. Experiments conducted on four datasets, MIMIC-IV ECG, AG News-Large, FEMNIST + Sketch, and IoT-SensorStream, show that SPFL improves average accuracy by about 7.7% compared to FedAvg, increases Jain’s Fairness Index by 0.05–0.06 compared to FairFed, and still maintains around 80% performance in the presence of 30% Byzantine clients. Convergence experiments further demonstrate that SPFL reduces the number of required rounds by about 30% compared to FedProx and exhibits lower performance degradation under high-noise conditions. These results confirm SPFL’s improvements in fairness and robustness, highlighting its application value in multi-source heterogeneous scenarios such as medical diagnosis, financial risk management, and IoT sensing. Full article

Federated Learning (FL) has emerged as a promising framework for multi-institutional medical artificial intelligence, enabling collaborative model development while preserving data privacy and security. Despite increasing research on federated approaches for cardiovascular disease prediction, previous reviews have largely focused on disease-specific perspectives without systematically comparing data modalities. This study comprehensively examines 28 representative investigations from the past five years, including 17 biosignal-based and 11 electronic health record (EHR)-based applications. Biosignal-based FL emphasizes personalized electrocardiogram (ECG) classification, mitigation of non-independent and identically distributed (Non-IID) data, and Internet of Things (IoT)-based monitoring using methods such as client clustering, asynchronous learning, and Bayesian inference. In contrast, EHR-based studies prioritize large-scale hospital collaboration, adaptive optimization, and secure aggregation through distributed frameworks. By systematically comparing methodological strategies, performance trade-offs, and clinical feasibility, this review highlights the complementary strengths of biosignal- and EHR-based approaches. Biosignal frameworks show strong potential for personalized, low-latency cardiac monitoring, whereas EHR frameworks excel in scalable and privacy-preserving decision support. Building upon the limitations of earlier reviews, this paper introduces data-type-centric design guidelines to enhance the reliability, interpretability, and clinical scalability of FL in cardiovascular diagnosis and prediction. Full article

Metabolic syndrome (MS) components, mainly correlated with insulin resistance and diabetes, constitute physiological disturbances that are objectively detectable based on physiological and anatomical measurements. In particular, the scientific literature indicates clear associations between features extracted from electrocardiograph (ECG) signals and MS. However, there exist few scientific studies related to MS detection by means of ECG signals, specially in automatic computer aided systems. This paper aims at developing and evaluating automatic tools for possible MS detection based on ECG signals. To evaluate how accurately and precisely the developed classifier systems detect MS from ECG signals, we use the following procedures. Initially, we use algorithms that automatically extract Q, R, and S peaks from ECG waveforms. Subsequently, we extract temporal features mainly associated with averages and variances of intervals and ratios between successive Q, R, and S peaks. We also use features describing the cardiac axis. The features are then used for training and testing classifier systems, including Support Vector Machines (SVMs) and RobustBoost classifiers. We also test the use of classifiers operating on raw ECG signals, without preliminary explicit feature extraction. The tested models constitute different configurations of Convolutional Neural Networks (CNNs). Our results indicate that it is possible to classify ECG signals in two different classes, separating people with MS from a control group, with statistically significant results. SVM, RobustBoost, and CNN models obtained average accuracy values equal to 94%, 89%, and 98%, respectively. These results indicate that automatic computer-aided diagnositcs of MS can be added to standard ECG clinical exams. Full article

Background: The reported data on ECG screening are focused on the last two decades. The objectives of this review were bifold: (i) to identify, within a timespan of twenty years, the most recent literature data on cardiac screening in children and adolescents and (ii) to provide data on the procedures used. Methods: Queries were conducted using PubMed, Scopus, and Google Scholar databases for the time window of 2005–2025. The mesh terms used were “ECG”, “Universal Screening”, “Cardiac Pathologies”, “Heart Rate”, and “Sports Pre-participation Evaluation”. Only research articles and review papers were included. The standard English language filter was used. Successively, only research articles were selected. Results: Data from 14 papers were considered, reflecting the lack of information about subjects <16 years of age. Conclusions: The information on objective ECG screening measures is quite scarce, and it is an urgent need to introduce a multidisciplinary approach to differentiate between ECG physiological changes due to growth and ECG pathological changes due to early pathology. Full article

The electrocardiogram (ECG) signal carries information crucial for health assessment, but its analysis can be challenging due to noise and signal variability; therefore, automated processing focused on noise removal and detection of key features is necessary. This paper introduces an ECG signal analysis and abnormality detection application developed to process single-lead ECG signals. In this study, the Lobachevsky University database (LUDB) was used as the source of ECG signals, as it includes annotated recordings using a multi-class, multi-label taxonomy that covers several diagnostic categories, each with specific diagnoses that reflect clinical ECG interpretation practices. The main aim of the paper is to provide a tool that efficiently filters noisy ECG data, accurately detects the QRS complex, PQ and QT intervals, calculates heart rate, and compares these values with normal ranges based on age and gender. Additionally, a multi-class, multi-label SVM-based model was developed and integrated into the application for heart abnormality diagnostics, i.e., assigning one or several diagnoses from various diagnostic categories. The MATLAB-based application is capable of processing raw ECG signals, allowing the use of ECG records not only from LUDB but also from other databases. Full article

This paper investigates Electrocardiogram (ECG) rhythm classification using a progressive deep learning framework that combines time–frequency representations with complementary hand-crafted features. In the first stage, ECG signals from the PhysioNet Challenge 2017 dataset are transformed into scalograms and input to diverse architectures, including Simple Convolutional Neural Network (SimpleCNN), Residual Network with 18 Layers (ResNet-18), Convolutional Neural Network-Transformer (CNNTransformer), and Vision Transformer (ViT). ViT achieved the highest accuracy (0.8590) and F1-score (0.8524), demonstrating the feasibility of pure image-based ECG analysis, although scalograms alone showed variability across folds. In the second stage, scalograms were fused with scattering and statistical features, enhancing robustness and interpretability. FusionViT without dimensionality reduction achieved the best performance (accuracy = 0.8623, F1-score = 0.8528), while Fusion ResNet-18 offered a favorable trade-off between accuracy (0.8321) and inference efficiency (0.016 s per sample). The application of Principal Component Analysis (PCA) reduced the dimensionality of the feature from 509 to 27, reducing the computational cost while maintaining competitive performance (FusionViT precision = 0.8590). The results highlight a trade-off between efficiency and fine-grained temporal resolution. Training-time augmentations mitigated class imbalance, enabling lightweight inference (0.006–0.043 s per sample). For real-world use, the framework can run on wearable ECG devices or mobile health apps. Scalogram transformation and feature extraction occur on-device or at the edge, with efficient models like ResNet-18 enabling near real-time monitoring. Abnormal rhythm alerts can be sent instantly to users or clinicians. By combining visual and statistical signal features, optionally reduced with PCA, the framework achieves high accuracy, robustness, and efficiency for practical deployment. Full article

A core challenge in multimodal emotion recognition lies in the precise capture of the inherent multimodal interactive nature of human emotions. Addressing the limitation of existing methods, which often process visual signals (facial expressions) and physiological signals (EEG, ECG, EOG, and GSR) in isolation and thus fail to exploit their complementary strengths effectively, this paper presents a new multimodal emotion recognition framework called the Gated Biological Visual Network (GBV-Net). This framework enhances emotion recognition accuracy through deep synergistic fusion of facial expressions and physiological signals. GBV-Net integrates three core modules: (1) a facial feature extractor based on a modified ConvNeXt V2 architecture incorporating lightweight Transformers, specifically designed to capture subtle spatio-temporal dynamics in facial expressions; (2) a hybrid physiological feature extractor combining 1D convolutions, Temporal Convolutional Networks (TCNs), and convolutional self-attention mechanisms, adept at modeling local patterns and long-range temporal dependencies in physiological signals; and (3) an enhanced gated attention fusion module capable of adaptively learning inter-modal weights to achieve dynamic, synergistic integration at the feature level. A thorough investigation of the publicly accessible DEAP and MAHNOB-HCI datasets reveals that GBV-Net surpasses contemporary methods. Specifically, on the DEAP dataset, the model attained classification accuracies of 95.10% for Valence and 95.65% for Arousal, with F1-scores of 95.52% and 96.35%, respectively. On MAHNOB-HCI, the accuracies achieved were 97.28% for Valence and 97.73% for Arousal, with F1-scores of 97.50% and 97.74%, respectively. These experimental findings substantiate that GBV-Net effectively captures deep-level interactive information between multimodal signals, thereby improving emotion recognition accuracy. Full article

Accurate diagnosis of myocardial infarction (MI) holds significant clinical importance for public health systems. Deep learning-based ECG, classification and localization methods can automatically extract features, thereby overcoming the dependence on manual feature extraction in traditional methods. However, these methods still face challenges such as insufficient utilization of dynamic information in cardiac cycles, inadequate ability to capture both global and local features, and data imbalance. To address these issues, this paper proposes a ResNet-Transformer cascaded network (RTCN) to process time frequency features of ECG signals generated by the S-transform. First, the S-transform is applied to adaptively extract global time frequency features from the time frequency domain of ECG signals. Its scalable Gaussian window and high phase resolution can effectively capture the dynamic changes in cardiac cycles that traditional methods often fail to extract. Then, we develop an architecture that combines the Transformer attention mechanism with ResNet to extract multi-scale local features and global temporal dependencies collaboratively. This compensates for the existing deep learning models’ insufficient ability to capture both global and local features simultaneously. To address the data imbalance problem, the Denoising Diffusion Probabilistic Model (DDPM) is applied to synthesize high-quality ECG samples for minority classes, increasing the inter-patient accuracy from 61.66% to 68.39%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization confirms that the model’s attention areas are highly consistent with pathological features, verifying its clinical interpretability. Full article

Atrial fibrillation (AF) detection in electrocardiograms (ECG) remains challenging, particularly at the heartbeat level. Traditional deep learning methods typically classify ECG segments as a whole, limiting their ability to detect AF at the granularity of individual heartbeats. This paper presents AF-DETR, a novel transformer-based object detection model for precise AF heartbeat localization and classification. AF-DETR incorporates a CNN backbone and a transformer encoder–decoder architecture, where 2D bounding boxes are used to represent heartbeat positions. Through iterative refinement of these bounding boxes, the model improves both localization and classification accuracy. To further enhance performance, we introduce contrastive denoising training, which accelerates convergence and prevents redundant heartbeat predictions. We evaluate AF-DETR on five publicly available ECG datasets (CPSC2021, AFDB, LTAFDB, MITDB, NSRDB), achieving state-of-the-art performance with F1-scores of 96.77%, 96.20%, 90.55%, and 99.87% for heartbeat-level classification, and segment-level accuracies of 98.27%, 97.55%, 97.30%, and 99.99%, respectively. These results demonstrate the effectiveness of AF-DETR in accurately detecting AF heartbeats and its strong generalization capability across diverse ECG datasets. Full article

Capacitive ECG sensors in automobiles enable unobtrusive heart rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a capacitive sensor with textile electrodes and provides insights into signal quality and RR duration accuracy. Electrodes of various shapes, sizes, and fabrics were integrated at various positions into the seat back of a driving simulator car seat. Seven subjects completed identical driving circuits with their cardiac signals being recorded simultaneously with textile electrodes and reference Ag-AgCl electrodes. Capacitive ECG signals with observable R peaks (after filtering) could be captured with almost all pairs of textile electrodes, independently of design or placement. Signal quality from textile electrodes was consistently lower compared with reference Ag-AgCl electrodes. Proximity to the heart or even contact with the body seems to be key but not enough to improve signal quality. However, accurate measurement of RR durations was mostly independent of signal quality since 90% of all RR durations measured on capacitive ECG signals had a percentage error below 5% compared to reference ECG signals. Accuracy was actually algorithm-dependent, where a classic Pan–Tompkins-based algorithm was interestingly outperformed by an in-house frequency-domain algorithm. Full article

Various types of interfering signals are an integral part of ECGs recorded using wearable electronics, specifically during field monitoring, outside the controlled environment of a medical doctor’s office, or laboratory. The frequency spectrum of several types of interfering signals overlaps significantly with the ECG signal, making effective filtration impossible without losing clinically relevant information. In this article, we proceed from the practical assumption that it is unnecessary to analyze the entire ECG recording in real long-term recordings. Conversely, in the preprocessing phase, it is necessary to detect unreadable segments of the ECG signal. This paper proposes a novel method for automatically detecting unreadable segments distorted by superimposed interference in ECG recordings. The method is based on a convolutional neural network (CNN) and is comparable in quality to annotation performed by a medical expert, but incomparably faster. In a series of controlled experiments, the ECG signal was recorded during physical activities of varying intensities, and individual segments of the recordings were manually annotated based on visual assessment by a medical expert, i.e., divided into four different classes based on the intensity of distortion to the useful ECG signal. A deep convolutional model was designed and evaluated, exhibiting a 87.62% accuracy score and the same F1-score in automatic recognition of segments distorted by superimposed interference. Furthermore, the model exhibits an accuracy and F1-score of 98.70% in correctly identifying segments with visually detectable and non-detectable heart rate. The proposed interference detection procedure appears to be sufficiently effective despite its simplicity. It facilitates subsequent automatic analysis of undisturbed ECG waveform segments, which is crucial in ECG monitoring using wearable electronics. Full article

The primary objective of this paper was to present a complete procedure, including a tester schematic, to test the compliance of any electrocardiographic (ECG) device or its elements, such as the analog front-end (AFE), with the International Electrotechnical Commission (IEC) standards. The paper highlights the importance of designing ECG devices in compliance with the standards, an issue often overlooked in academic research at a lower technology readiness level, and provides detailed guidance on the testing procedure. A measurement system designed to evaluate the performance of ECG devices in accordance with three IEC standards (60601-2-25, 60601-2-27, and 60601-2-47) is presented. A review of standard measurement procedures was conducted using a device equipped with the ADS1298 AFE. The measurements demonstrating the ADS1298’s compliance with the IEC 60601-2-25 ECG standard were performed using the ECG Tester TEST, manufactured by the Łukasiewicz Research Network–Krakow Institute of Technology, Biomedical Engineering Center. Full article