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Search Results (447)

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Keywords = electrocardiogram (ECG) monitoring

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19 pages, 487 KiB  
Review
Smart Clothing and Medical Imaging Innovations for Real-Time Monitoring and Early Detection of Stroke: Bridging Technology and Patient Care
by David Sipos, Kata Vészi, Bence Bogár, Dániel Pető, Gábor Füredi, József Betlehem and Attila András Pandur
Diagnostics 2025, 15(15), 1970; https://doi.org/10.3390/diagnostics15151970 - 6 Aug 2025
Abstract
Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and [...] Read more.
Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach. Full article
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35 pages, 6415 KiB  
Review
Recent Advances in Conductive Hydrogels for Electronic Skin and Healthcare Monitoring
by Yan Zhu, Baojin Chen, Yiming Liu, Tiantian Tan, Bowen Gao, Lijun Lu, Pengcheng Zhu and Yanchao Mao
Biosensors 2025, 15(7), 463; https://doi.org/10.3390/bios15070463 - 18 Jul 2025
Viewed by 370
Abstract
In recent decades, flexible electronics have witnessed remarkable advancements in multiple fields, encompassing wearable electronics, human–machine interfaces (HMI), clinical diagnosis, and treatment, etc. Nevertheless, conventional rigid electronic devices are fundamentally constrained by their inherent non-stretchability and poor conformability, limitations that substantially impede their [...] Read more.
In recent decades, flexible electronics have witnessed remarkable advancements in multiple fields, encompassing wearable electronics, human–machine interfaces (HMI), clinical diagnosis, and treatment, etc. Nevertheless, conventional rigid electronic devices are fundamentally constrained by their inherent non-stretchability and poor conformability, limitations that substantially impede their practical applications. In contrast, conductive hydrogels (CHs) for electronic skin (E-skin) and healthcare monitoring have attracted substantial interest owing to outstanding features, including adjustable mechanical properties, intrinsic flexibility, stretchability, transparency, and diverse functional and structural designs. Considerable efforts focus on developing CHs incorporating various conductive materials to enable multifunctional wearable sensors and flexible electrodes, such as metals, carbon, ionic liquids (ILs), MXene, etc. This review presents a comprehensive summary of the recent advancements in CHs, focusing on their classifications and practical applications. Firstly, CHs are categorized into five groups based on the nature of the conductive materials employed. These categories include polymer-based, carbon-based, metal-based, MXene-based, and ionic CHs. Secondly, the promising applications of CHs for electrophysiological signals and healthcare monitoring are discussed in detail, including electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), respiratory monitoring, and motion monitoring. Finally, this review concludes with a comprehensive summary of current research progress and prospects regarding CHs in the fields of electronic skin and health monitoring applications. Full article
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14 pages, 971 KiB  
Article
High Voltage and Train-Surfing Injuries: A 30-Year Retrospective Analysis of High-Voltage Trauma and Its Impact on Cardiac Biomarkers
by Viktoria Koenig, Maximilian Monai, Alexandra Christ, Marita Windpassinger, Gerald C. Ihra, Alexandra Fochtmann-Frana and Julian Joestl
J. Clin. Med. 2025, 14(14), 4969; https://doi.org/10.3390/jcm14144969 - 14 Jul 2025
Viewed by 290
Abstract
Background: High-voltage electrical injuries (HVEIs) represent a complex and life-threatening entity, frequently involving multi-organ damage. While traditionally linked to occupational hazards, train surfing—riding on moving trains—and train climbing—scaling stationary carriages—have emerged as increasingly common causes among adolescents. Popularized via social media, these [...] Read more.
Background: High-voltage electrical injuries (HVEIs) represent a complex and life-threatening entity, frequently involving multi-organ damage. While traditionally linked to occupational hazards, train surfing—riding on moving trains—and train climbing—scaling stationary carriages—have emerged as increasingly common causes among adolescents. Popularized via social media, these behaviors expose individuals to the invisible danger of electric arcs from 15,000-volt railway lines, often resulting in extensive burns, cardiac complications, and severe trauma. This study presents a 30-year retrospective analysis comparing cardiac biomarkers and clinical outcomes in train-surfing injuries versus work-related HVEIs. Methods: All patients with confirmed high-voltage injury (≥1000 volts) admitted to a Level 1 burn center between 1994 and 2024 were retrospectively analyzed. Exclusion criteria comprised low-voltage trauma, suicide, incomplete records, and external treatment. Clinical and laboratory parameters—including total body surface area (TBSA), Abbreviated Burn Severity Index (ABSI), electrocardiogram (ECG) findings, intensive care unit (ICU) and hospital stay, mortality, and cardiac biomarkers (creatine kinase [CK], CK-MB, lactate dehydrogenase [LDH], aspartate transaminase [AST], troponin, and myoglobin)—were compared between the two cohorts. Results: Of 81 patients, 24 sustained train-surfing injuries and 57 were injured in occupational settings. Train surfers were significantly younger (mean 16.7 vs. 35.2 years, p = 0.008), presented with greater TBSA (49.9% vs. 17.9%, p = 0.008), higher ABSI scores (7.3 vs. 5.1, p = 0.008), longer ICU stays (53 vs. 17 days, p = 0.008), and higher mortality (20.8% vs. 3.5%). ECG abnormalities were observed in 51% of all cases, without significant group differences. However, all cardiac biomarkers were significantly elevated in train-surfing injuries at both 72 h and 10 days post-injury (p < 0.05), suggesting more pronounced cardiac and muscular damage. Conclusions: Train-surfing-related high-voltage injuries are associated with markedly more severe systemic and cardiac complications than occupational HVEIs. The significant biomarker elevation and critical care demands highlight the urgent need for targeted prevention, public awareness, and early cardiac monitoring in this high-risk adolescent population. Full article
(This article belongs to the Section Cardiovascular Medicine)
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17 pages, 1952 KiB  
Article
Feasibility and Safety of Early Cardiac Rehabilitation Using Remote Electrocardiogram Monitoring in Patients with Cardiac Surgery: A Pilot Study
by Yeon Mi Kim, Bo Ryun Kim, Sung Bom Pyun, Jae Seung Jung, Hee Jung Kim and Ho Sung Son
J. Clin. Med. 2025, 14(14), 4887; https://doi.org/10.3390/jcm14144887 - 10 Jul 2025
Viewed by 415
Abstract
Purpose: We aimed to evaluate the safety and feasibility of a remote electrocardiogram (ECG) monitoring-based cardiac rehabilitation (CR) program during an early postoperative period in patients who underwent cardiac surgery. Methods: Five days after cardiac surgery, patients were referred to a [...] Read more.
Purpose: We aimed to evaluate the safety and feasibility of a remote electrocardiogram (ECG) monitoring-based cardiac rehabilitation (CR) program during an early postoperative period in patients who underwent cardiac surgery. Methods: Five days after cardiac surgery, patients were referred to a CR department and participated in a low-intensity inpatient CR program while wearing an ECG monitoring device. Prior to discharge, the patients underwent a cardiopulmonary exercise test (CPET) and squat endurance test to determine the suitable intensity and target heart rate (HR) for home-based CR (HBCR). During 2 weeks of the HBCR period after discharge, patients participated in aerobic and resistance exercises. Electrocardiogram data were transmitted to a cloud, where researchers closely monitored them through a website and provided feedback to the patients via telephone calls. Grip strength (GS), 6 min walk distance (6 MWD), EuroQol-5 dimension (EQ-5D), short-form 36-item health survey (SF-36), and Korean Activity Scale/Index (KASI) were measured at three different time points: 5 d post-surgery (T1), pre-discharge (T2), and 2 weeks after discharge (T3). Squat endurance tests and CPET were performed only at T2 and T3. Result: Sixteen patients completed the study, seven (44%) of whom underwent coronary artery bypass graft surgery (CABG). During the study period between T2 and T3, peak VO2 improved from 12.39 ± 0.57 to 17.93 ± 1.25 mL/kg/min (p < 0.01). The squat endurance test improved from 16.69 ± 2.31 to 21.81 ± 2.31 (p < 0.01). In a comparison of values of time points between T1 and T3, the GS improved from 28.30 ± 1.66 to 30.40 ± 1.70 kg (p = 0.02) and 6 MWD increased from 249.33 ± 20.92 to 387.02 ± 22.77 m (p < 0.01). The EQ-5D and SF-36 improved from 0.59 ± 0.03 to 0.82 ± 0.03 (p < 0.01) and from 83.99 ± 3.40 to 122.82 ± 6.06 (p < 0.01), and KASI improved from 5.44 ± 0.58 to 26.11 ± 2.70 (p < 0.01). In a subgroup analysis, the CABG group demonstrated a greater increase in 6 MWD (102.29 m, p < 0.01) than the non-CABG group. At the end of the study, 75% of the patients expressed satisfaction with the early CR program guided by remote ECG monitoring. Conclusions: Our findings suggest that early remote ECG monitoring-based CR programs are safe and feasible for patients who have undergone cardiac surgery. Additionally, the program improved aerobic capacity, functional status, and quality of life. Full article
(This article belongs to the Section Cardiology)
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18 pages, 8992 KiB  
Article
Flexible Bioelectrodes-Integrated Miniaturized System for Unconstrained ECG Monitoring
by Yaoliang Zhan, Xue Wang and Jin Yang
Sensors 2025, 25(13), 4213; https://doi.org/10.3390/s25134213 - 6 Jul 2025
Viewed by 431
Abstract
The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users’ daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. [...] Read more.
The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users’ daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. Therefore, utilizing reusable and cost-effective flexible bioelectrodes (with a signal-to-noise ratio of 33 dB), a miniaturized wearable system (MWS) is proposed for unconstrained ECG monitoring, which holds a size of 65 × 52 × 12 mm3 and a weight of 69 g. Given these compelling features, this system enables reliable and high-quality ECG signal monitoring in individuals’ daily activities, including sitting, walking, and cycling, with the acquired signals exhibiting distinguishable QRS characteristics. Furthermore, an exercise intensity classification model was developed based on ECG characteristics and a fully connected neural network (FCNN) algorithm, with an evaluation accuracy of 98%. These results exhibit the promising potential of the MWS in tracking individuals’ physiological signals and assessing exercise intensity. Full article
(This article belongs to the Special Issue Feature Papers in Electronic Sensors 2025)
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23 pages, 2320 KiB  
Article
Visualizing Relaxation in Wearables: Multi-Domain Feature Fusion of HRV Using Fuzzy Recurrence Plots
by Puneet Arya, Mandeep Singh and Mandeep Singh
Sensors 2025, 25(13), 4210; https://doi.org/10.3390/s25134210 - 6 Jul 2025
Viewed by 443
Abstract
Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a [...] Read more.
Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article
(This article belongs to the Special Issue Sensors for Heart Rate Monitoring and Cardiovascular Disease)
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21 pages, 8180 KiB  
Article
Resource-Constrained On-Chip AI Classifier for Beat-by-Beat Real-Time Arrhythmia Detection with an ECG Wearable System
by Mahfuzur Rahman and Bashir I. Morshed
Electronics 2025, 14(13), 2654; https://doi.org/10.3390/electronics14132654 - 30 Jun 2025
Viewed by 407
Abstract
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 [...] Read more.
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
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24 pages, 1766 KiB  
Article
An Analysis of Arterial Pulse Wave Time Features and Pulse Wave Velocity Calculations Based on Radial Electrical Bioimpedance Waveforms in Patients Scheduled for Coronary Catheterization
by Kristina Lotamõis, Tiina Uuetoa, Andrei Krivošei, Paul Annus, Margus Metshein, Marek Rist, Sulev Margus, Mart Min and Gert Tamberg
J. Cardiovasc. Dev. Dis. 2025, 12(7), 237; https://doi.org/10.3390/jcdd12070237 - 20 Jun 2025
Viewed by 388
Abstract
The monitoring of peripheral electrical bioimpedance (EBI) variations is a promising method that has the potential to replace invasive or burdensome techniques for cardiovascular measurements. Segmental or continuous recording of peripheral pulse waves can serve as a basis for calculating prognostic markers like [...] Read more.
The monitoring of peripheral electrical bioimpedance (EBI) variations is a promising method that has the potential to replace invasive or burdensome techniques for cardiovascular measurements. Segmental or continuous recording of peripheral pulse waves can serve as a basis for calculating prognostic markers like pulse wave velocity (PWV) or include parameters such as pulse transit time (PTT) or pulse arrival time (PAT) for noninvasive blood pressure (BP) estimation, as well as potentially novel cardiovascular risk indicators. However, several technical, analytical, and interpretative aspects need to be resolved before the EBI method can be adopted in clinical practice. Our goal was to investigate and improve the application of EBI, executing its comparison with other cardiovascular assessment methods in patients hospitalized for coronary catheterization procedures. Methods: We analyzed data from 44 non-acute patients aged 45–74 years who were hospitalized for coronary catheterization at East Tallinn Central Hospital between 2020 and 2021. The radial EBI and electrocardiogram (ECG) were measured simultaneously with central and contralateral pressure curves. The Savitzky–Golay filter was used for signal smoothing. The Hankel matrix decomposer was applied for the extraction of cardiac waveforms from multi-component signals. After extracting the cardiac component, a period detection algorithm was applied to EBI and blood pressure curves. Results: Seven points of interest were detected on the pressure and EBI curves, and four with good representativeness were selected for further analysis. The Spearman correlation coefficient was low for all but the central and distal pressure curve systolic upstroke time points. A high positive correlation was found between PWV measured both invasively and with EBI. The median value of complimentary pulse wave velocity (CPWV), a parameter proposed in the paper, was significantly lower in patients with normal coronaries compared to patients with any stage of coronary disease. Conclusions: With regard to wearable devices, the EBI-derived PAT can serve as a substrate for PWV calculations and cardiovascular risk assessment, although these data require further confirmation. Full article
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33 pages, 15086 KiB  
Review
Broad Electrocardiogram Syndromes Spectrum: From Common Emergencies to Particular Electrical Heart Disorders—Part II
by Alexandr Ceasovschih, Anastasia Balta, Victorița Șorodoc, Krishnaraj Rathod, Ahmed El Gohary, Serghei Covantsev, Richárd Masszi, Yusuf Ziya Şener, Alexandru Corlăteanu, Syed Haseeb Raza Naqvi, Alexandra Grejdieru, Nicholas G. Kounis and Laurențiu Șorodoc
Diagnostics 2025, 15(12), 1568; https://doi.org/10.3390/diagnostics15121568 - 19 Jun 2025
Viewed by 2634
Abstract
The electrocardiogram (ECG) remains a cornerstone of modern cardiology, providing rapid, non-invasive, and widely accessible diagnostic insights. While ECG interpretation is an essential skill for clinicians, certain patterns can be subtle or atypical, posing diagnostic challenges. In our previous review (doi.org/10.3390/jpm12111754), we explored [...] Read more.
The electrocardiogram (ECG) remains a cornerstone of modern cardiology, providing rapid, non-invasive, and widely accessible diagnostic insights. While ECG interpretation is an essential skill for clinicians, certain patterns can be subtle or atypical, posing diagnostic challenges. In our previous review (doi.org/10.3390/jpm12111754), we explored several uncommon ECG syndromes with significant clinical implications. However, the spectrum of electrocardiographic abnormalities extends far beyond those initially discussed. In this second installment, we expand our discussion of rare and underrecognized ECG syndromes, including Long QT, Jervell and Lange-Nielsen, Romano–Ward, Andersen–Tawil, Timothy, Short QT, and Twiddler’s syndromes, as well as Noonan, Barlow’s, Bundgaard, BRASH, Carvajal, Naxos, and Danon disease. We highlight their clinical context, characteristic findings, and implications for diagnosis and management. These conditions range from acute, life-threatening emergencies requiring immediate intervention to chronic electrical disorders necessitating long-term monitoring and risk stratification. By broadening our focus, we aim to enhance awareness and recognition of these entities, ultimately improving patient outcomes through timely and accurate diagnosis. Full article
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26 pages, 4662 KiB  
Article
Cognitive Cardiac Assessment Using Low-Cost Electrocardiogram Acquisition System
by Milan Milivojević and Ana Gavrovska
Electronics 2025, 14(12), 2468; https://doi.org/10.3390/electronics14122468 - 18 Jun 2025
Viewed by 526
Abstract
Information and communication technologies are revolutionizing cardiac monitoring. Particularly, different Internet of Things (IoT) devices are gaining popularity, although basic cognitive tools that rely on electrocardiograms (ECGs) are still uncommon. Here, an ECG acquisition system for cognitive load analysis has been developed based [...] Read more.
Information and communication technologies are revolutionizing cardiac monitoring. Particularly, different Internet of Things (IoT) devices are gaining popularity, although basic cognitive tools that rely on electrocardiograms (ECGs) are still uncommon. Here, an ECG acquisition system for cognitive load analysis has been developed based on an Arduino-based, low-cost device for signal processing, recording, analysis, and classification. The system used network components such a cloud server for storage and related functions. By comparing the recorded signals to the reference professional medical device, the quality of the signals was confirmed. The Stroop test was used in the experiment to measure cognitive load in healthy subjects. The cognitive test caused, in most cases, characteristic changes in the structure of a large deviation multifractal spectrum. Thus, a new classification model based on multifractal total variations was presented for cognitive load assessment based on an ECG. The proposed cosine kNN (k nearest neighbors) approach yielded high accuracy results of above 90% using five-fold cross-validation, which were compared to other methods. It applied a relatively small number of features, including the Shannon entropy and the total variations. Full article
(This article belongs to the Special Issue Emerging Biomedical Electronics)
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29 pages, 3354 KiB  
Article
Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI
by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad and Hussain Dawood
Algorithms 2025, 18(6), 333; https://doi.org/10.3390/a18060333 - 2 Jun 2025
Viewed by 1033
Abstract
Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the [...] Read more.
Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. This lack of interpretability limits their clinical trust and adoption. The proposed approach addresses this limitation by integrating predictive modeling with Explainable AI (XAI) to ensure both accuracy and transparency in clinical decision-making. The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. Due to class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance the representation of the minority class. After preprocessing, various ML algorithms were employed, among which Artificial Neural Networks (ANN) achieved the highest performance with 96.1% accuracy, 95.7% recall, and 95.7% F1-score. To enhance the interpretability of ANN, two XAI techniques, specifically SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were utilized. This study incrementally benchmarks SMOTE, ANN, and XAI techniques such as SHAP and LIME on standardized cardiac datasets, emphasizing clinical interpretability and providing a reproducible framework for practical healthcare implementation. These techniques enable healthcare practitioners to understand the model’s decisions, identify key predictive features, and enhance clinical judgment. By bridging the gap between AI-driven performance and practical medical implementation, this work contributes to making heart attack prediction both highly accurate and interpretable, facilitating its adoption in real-world clinical settings. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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11 pages, 610 KiB  
Article
Prediction of Cardiac Arrhythmias in Cancer Patients Treated with Immune Checkpoint Inhibitors Using Electrocardiogram
by Alper Coskun, Ece Celebi Coskun, Ahmet Bilgehan Sahin, Fatih Levent, Eyup Coban, Fatih Koca, Seda Sali, Omer Furkan Demir, Adem Deligonul, Erhan Tenekecioglu, Erdem Cubukcu, Fahriye Vatansever Agca and Turkkan Evrensel
Diagnostics 2025, 15(10), 1235; https://doi.org/10.3390/diagnostics15101235 - 14 May 2025
Cited by 1 | Viewed by 609
Abstract
Background/Objectives: Immune checkpoint inhibitor (ICI)-associated cardiovascular adverse events are relatively uncommon; they can be life-threatening, particularly when involving malignant ventricular arrhythmias. Electrocardiographic markers such as P-wave dispersion (PWD), QT dispersion (QTd), T-peak to T-end (Tp-e) interval, and Tp-e/QT and Tp-e/QTc ratios have [...] Read more.
Background/Objectives: Immune checkpoint inhibitor (ICI)-associated cardiovascular adverse events are relatively uncommon; they can be life-threatening, particularly when involving malignant ventricular arrhythmias. Electrocardiographic markers such as P-wave dispersion (PWD), QT dispersion (QTd), T-peak to T-end (Tp-e) interval, and Tp-e/QT and Tp-e/QTc ratios have been linked to an elevated risk of both atrial and ventricular arrhythmias and sudden cardiac death across various cardiac conditions. Monitoring these parameters may aid in identifying the risk of arrhythmogenic events in cancer patients undergoing ICI therapy. Methods: This retrospective cohort study analyzed 42 patients with cancer who received ICI therapy and had serial 12-lead electrocardiograms (ECGs) performed at baseline and at three-month intervals during the first year of treatment, from May 2022 to November 2023. ECG parameters including PWD, QTd, Tp-e interval, and Tp-e/QT and Tp-e/QTc ratios were measured and compared between baseline and follow-up time points. Results: The median follow-up duration was 5.3 months (range: 0.5–18.9 months). No statistically significant differences were observed in any of the ECG parameters between baseline and subsequent measurements (p > 0.05). One patient developed atrial fibrillation during the third month of treatment. Additionally, one patient exhibited a left anterior fascicular block, and another experienced frequent ventricular extrasystoles. No malignant ventricular arrhythmias were reported throughout the study period. Conclusions: This study found no significant changes in electrocardiographic markers associated with arrhythmia risk during ICI treatment. Larger, multicenter, prospective studies with extended follow-up are warranted to further elucidate the cardiovascular safety profile of ICIs. Full article
(This article belongs to the Section Clinical Diagnosis and Prognosis)
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14 pages, 2495 KiB  
Article
Specific Premature Ventricular Complex Characteristics in Women: Insights from a Patient Cohort
by Ștefan Ailoaei, Laurențiu Șorodoc, Carina Ureche, Nicolae Sîtari, Alexandr Ceasovschih, Mihaela Grecu, Radu Andy Sascău and Cristian Stătescu
J. Cardiovasc. Dev. Dis. 2025, 12(5), 181; https://doi.org/10.3390/jcdd12050181 - 13 May 2025
Viewed by 391
Abstract
Background: Premature ventricular complexes (PVCs) are common arrhythmias that can range from benign to clinically significant. While PVCs have been extensively studied in the general population, gender-specific differences in their characteristics, prevalence, and clinical impact remain underexplored. This study aims to investigate the [...] Read more.
Background: Premature ventricular complexes (PVCs) are common arrhythmias that can range from benign to clinically significant. While PVCs have been extensively studied in the general population, gender-specific differences in their characteristics, prevalence, and clinical impact remain underexplored. This study aims to investigate the unique features of PVCs in women and their potential implications for diagnosis and management. Methods: We analyzed a cohort of female patients diagnosed with PVCs, assessing their electrocardiographic patterns, symptomatology, and clinical outcomes. Data were collected from medical records, including Holter monitoring, electrocardiograms (ECGs), and echocardiographic findings. The study also evaluated the association between PVC burden and underlying cardiac conditions. Results: This study analyzed 161 patients (59 females, 91 males) with PVCs, revealing significant sex-based differences. Males were older, had higher BMI, and smoked more, while females experienced more presyncope. ECGs showed greater QRS fragmentation in males. TTE and CMR found males had larger ventricles, lower EF, and more myocardial fibrosis (LGE: 59.34% vs. 37.93%). Patients with LGE were older and had worse clinical outcomes, including higher ICD implantation and hospitalization rates. Despite these structural differences, treatment efficacy was similar across groups. Conclusion: This study highlights key differences in PVC characteristics among women, underscoring the need for gender-specific approaches in clinical evaluation and management. Recognizing these distinctions may aid in early diagnosis, reduce unnecessary interventions, and improve patient outcomes. Further research is warranted to explore the long-term implications of PVCs in women and optimize therapeutic strategies. Full article
(This article belongs to the Special Issue Modern Approach to Complex Arrhythmias, 2nd Edition)
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17 pages, 3331 KiB  
Article
Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities
by Alessandra Papetti, Marianna Ciccarelli, Andrea Manni, Andrea Caroppo and Gabriele Rescio
Sensors 2025, 25(10), 3015; https://doi.org/10.3390/s25103015 - 10 May 2025
Cited by 1 | Viewed by 579
Abstract
To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physiological measures for monitoring work-related stress, electrooculography (EOG) remains underexplored in this [...] Read more.
To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physiological measures for monitoring work-related stress, electrooculography (EOG) remains underexplored in this context. Although less extensively studied, EOG shows significant promise for comparable applications. Furthermore, the realm of human factors and ergonomics lacks sufficient research on the integration of wearable sensors, particularly in the evaluation of human work. This article aims to bridge these gaps by examining the potential of EOG signals, captured through smart eyewear, as indicators of stress. The study involved twelve subjects in a controlled environment, engaging in four stress-inducing tasks interspersed with two-minute relaxation intervals. Emotional responses were categorized both into two classes (relaxed and stressed) and three classes (relaxed, slightly stressed, and stressed). Employing supervised machine learning (ML) algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN)—the analysis revealed accuracy rates exceeding 80%, with RF leading at 85.8% and 82.4% for two classes and three classes, respectively. The proposed wearable system shows promise in monitoring workers’ well-being, especially during visual activities. Full article
(This article belongs to the Special Issue Sensing Human Cognitive Factors)
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23 pages, 5770 KiB  
Review
Are Wearable ECG Devices Ready for Hospital at Home Application?
by Jorge Medina-Avelino, Ricardo Silva-Bustillos and Juan A. Holgado-Terriza
Sensors 2025, 25(10), 2982; https://doi.org/10.3390/s25102982 - 9 May 2025
Viewed by 2221
Abstract
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) [...] Read more.
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
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