Search Results (1,554)

Electrocardiogram (ECG)-gated contrast-enhanced computed tomography angiography (CTA) is the reference method for follow-up of ascending aortic aneurysms but delivers substantially higher radiation doses than ECG-gated non-contrast CT (NCCT). NCCT can be acquired at a lower dose while enabling measurements of the aortic outer diameter. This study aimed to quantify the radiation dose of both techniques and determine whether a significant difference exists in ascending thoracic aorta diameter measurements between NCCT and CTA. Eighty patients who underwent ECG-gated cardiac CT for suspected coronary artery disease were retrospectively analyzed. Three observers measured the ascending aortic diameter at the level of the pulmonary artery in a plane perpendicular to the aorta on both NCCT and CTA images. Inter-rater reliability was assessed using intraclass correlation coefficients, and paired samples t-tests were used to evaluate measurement differences. Dose-length products (DLP) were collected. Median DLP values were 16.1 mGy·cm (interquartile range 11.8–25.1) for NCCT and 190.3 mGy·cm (interquartile range 120.5–298.9) for CTA. NCCT measurements were consistently larger than CTA measurements, with mean differences of 2.1 ± 0.8 mm, 2.6 ± 0.96 mm, and 2.9 ± 1.09 mm for the senior radiologist, junior radiologist, and resident, respectively (all p < 0.001). Inter-observer agreement was excellent (ICC = 0.99, p < 0.001). NCCT delivered an 11.8-fold lower radiation dose than CTA. NCCT may replace CTA for ascending aortic diameter follow-up if measurements are adjusted by approximately 2–3 mm relative to CTA-derived inner-diameter thresholds. Full article

Introduction: Palpitations are one of the most common cardiovascular complaints, affecting approximately 6% to 11% of the general population. Since palpitations often occur sporadically and resolve before medical evaluation, diagnosing the underlying rhythm disturbance requires documentation via an electrocardiogram (ECG) recorded during the symptomatic episode. The standard tool for this purpose has long been the 24-h Holter monitor, which has significant limitations, with diagnostic yields as low as 10% to 15%. Objective: This study aims to evaluate the feasibility and diagnostic yield of single-lead ECG recordings from smartwatches in patients presenting with palpitations. Methods: From 1 May 2023 to 1 May 2025, we conducted a prospective, real-world cohort study among consecutive adults referred to the University Hospital of Ferrara-based arrhythmia outpatient clinics for evaluation of palpitations. Eligibility required patients to be ≥21 years of age, report palpitations for which ambulatory documentation was clinically indicated, and already own a compatible smartwatch capable of single-lead ECG. Participants were trained to record a 30-s single-lead ECG at the onset of symptoms. Tracings were transmitted securely and independently reviewed by two blinded electrophysiologists. Results: Fifty-nine patients were enrolled (mean age 52 years, 64% male). Thirty-one patients (52%) transmitted at least one smartwatch-derived electrocardiographic tracing. Seventy-seven smartwatch tracings were received. Of these, 73 (95%) were interpretable; 57 (78%) showed an arrhythmia, whereas 16 (22%) demonstrated normal sinus rhythm. Four recordings (5%) were non-interpretable. From the 57 arrhythmic tracings, 44 distinct arrhythmic diagnoses were identified. Paroxysmal atrial fibrillation (AF) accounted for 16 episodes. Other diagnosed arrhythmias included atrial flutter (n = 6), paroxysmal supraventricular tachycardia (PSVT) (n = 4), premature atrial complexes (PAC) (n = 6), premature ventricular complexes (PVC) (n = 9), inappropriate sinus tachycardia (n = 12), and second-degree atrioventricular (AV) block type I (n = 4). Conclusions: Smartwatch-based ECG monitoring in symptomatic patients is feasible and provides a high diagnostic yield for a broad spectrum of arrhythmias. Unlike large-scale population screening approaches, which generate vast datasets with limited clinical benefit, a symptom-driven strategy applied to carefully selected, educated, and motivated patients proves both clinically valuable and organizationally sustainable. Indeed, the mean number of tracings transmitted per patient was low (1.3), confirming the clinical and operational sustainability of this patient-triggered, real-world approach. Full article

Timely classification of cardiovascular diseases is crucial to improve medical outcomes. Emerging remote patient monitoring systems help achieve this by enabling continuous monitoring of electrocardiogram signals in home environments. However, these systems struggle with unique challenges like missing genuine medical emergencies, rising energy demands, scalability challenges, handling vast medical databases, data processing delays, and safeguarding patient records. To overcome these challenges, we propose a single framework with three main phases: (a) an embedded hardware-driven K-Nearest Neighbor (KNN)-assisted real-time ECG monitoring and classification method; (b) a differentiated communication strategy (DCS) formed with a priority-based ECG data packaging framework and multi-layered security protocols; and (c) a multi-level blockchain network (MLBN) architecture armed with adaptive security mechanisms and real-time cross-chain medical data communication bridges. Simulations are conducted using the ECG signals (1000 fragments) dataset and the Ganache Ethereum development framework. The classification accuracies obtained for patient urgent categories U1 to U5 are 91.43%, 95.71%, 94.23%, 90.00%, and 91.43%, respectively. The performance evaluation results of the KNN-guided classification method, along with DCS and MLBN simulation results obtained from average gas consumption analysis, confirms reliability and viability of our framework, while also revolutionizing remote patient monitoring technology and addressing critical challenges in existing systems. Full article

Cardiac arrest is a way of demise of patients who are affected by heart failure (HF), being more frequent in those with HF with a reduced left ventricular ejection fraction (HFrEF), and is, as such, responsible for 30–50% of cardiac death. Specific data on the risk of sudden cardiac death (SCD) related to HF with a preserved ejection fraction (HFpEF) and HF with a mildly reduced ejection fraction (HFmrEF) are lacking, as well as data regarding ventricular arrhythmias in this population. Considering the 0.3% person/year incidence rate of investigator-reported ventricular tachycardia (VT) and ventricular fibrillation (VF), the rate of SCD in the analyzed population seems to be 1.3% per year. Age, gender, history of diabetes and myocardial infarction, left bundle branch block (LBBB) on electrocardiogram (ECG), and a natural logarithm of N-terminal pro B-type natriuretic peptide (NT-proBNP), identified a subgroup of HFpEF patients with a higher risk (5-year cumulative incidence of 11%) of sudden death (SD). In HFpEF patients, both glifozins and finerenone did not demonstrate a beneficial effect on SCD incidence in comparison to placebo. A significantly lower rate of SCD emerged in patients who were treated with dapaglifozin (10 vs. 26 pts) among patients with HF with an improved ejection fraction (HFimpEF), who were defined as patients with a previous left ventricular ejection fraction (LVEF) < 40%. Promising methods discussed include cardiac magnetic resonance, myocardial scintigraphy, genetic assessment, and electrophysiologic studies for predicting SCD in those patients. In conclusion, arrhythmic SCD in HFpEF patients should not be considered merely as an effect of VT/VF; bradyarrhythmia is probably more frequent and dangerous. The effects of drugs in preventing SCD in HFpEF have not been demonstrated yet. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

(1) Background: Heartbeat detection from electrocardiogram (ECG) and photoplethysmograph (PPG) signals is widely used in wearable devices for health monitoring, fitness tracking, and stress assessment. While numerous methods have been proposed, their practical suitability depends not only on accuracy but also on computational and memory constraints inherent to resource-limited systems. (2) Methods: A scoping review of 52 studies published between 2017 and 2024 was conducted, covering time-domain, frequency-domain, matrix-based, and machine learning approaches. The methods were evaluated according to estimation accuracy, computational complexity, memory footprint, and suitability for on-device implementation. (3) Results: Time-domain peak detection methods consistently provide high accuracy (minimum of $79.25$%, maximum of $99.96$%, and median $\ge 99.69$%) for ECG and reliable heart rate estimation for PPG with linear computational complexity, low memory requirements and low energy consumption. Frequency-domain approaches are suitable for average heart rate estimation from PPG but do not preserve inter-beat intervals (error range of [1.07, 6.4] beats per minute (BPM)). Matrix-based and machine learning methods often entail higher computational cost without proportional performance gains in wearable contexts (error range of [1.07, 6.4] BPM for PPG signals; accuracy in range of $[95.4, 99.96]$% for ECG). (4) Conclusions: Lightweight signal-processing techniques offer the most favorable trade-off between accuracy and efficiency for wearable implementations, whereas computationally intensive approaches are better suited for edge- or cloud-based processing. Full article

Background: Metabolic syndrome (MetS) leads to alterations in cardiac autonomic control that can be detected from electrocardiogram (ECG)-derived markers, particularly when the cardiovascular system is challenged during an oral glucose tolerance test (OGTT). Methods: In this paper, we present an automated framework for MetS identification using RR intervals and heart rate variability (HRV) features extracted from 12-lead ECG recordings acquired during the five OGTT stages in 40 male participants (15 with MetS, 10 controls, and 15 endurance-trained marathon runners). RR intervals were first derived using a multilead Pan-Tompkins approach with fusion-based validation. From these RR series, HRV descriptors were computed from time-domain statistics (RR mean, SDNN, rMSSD, pNN50), spectral indices (VLF, LF, HF, LF/HF), and nonlinear measures (SD1, SD2, SampEn, DFA-${\alpha }_1$). Conventional HRV analysis revealed pronounced physiological differences between groups: MetS subjects exhibited reduced parasympathetic activity, reflected by lower rMSSD and SD1, lower HF power, and higher LF/HF ratios, whereas marathoners showed greater vagal modulation, higher HF power, and increased signal complexity. Healthy controls showed an intermediate autonomic profile. Using RR sequences and HRV descriptors (256 samples per stage), we trained three multimodal classifiers: a CNN-MLP model with a softmax output, a CNN-MLP model with an SVM head, and a CNN + LSTM-MLP + SVM architecture. Results: All models achieved strong discriminative performance, with accuracies ranging from 0.92 to 0.95, F1-macro values from 0.92 to 0.95, and macro-AUC values from 0.96 to 0.97. The CNN-MLP model achieved the best overall performance, whereas the CNN + LSTM-MLP + SVM model showed strong class discrimination, particularly for endurance athletes, while maintaining competitive recall for MetS. Conclusions: These findings support the feasibility of ECG-based autonomic assessment as a complementary non-invasive approach for early metabolic risk detection in clinical and preventive cardiometabolic screening settings. Full article

Introduction: Prolongation of the QT interval and QRS duration, which are markers of ventricular repolarization and depolarization, has been reported in patients receiving flecainide therapy. However, the effects of flecainide on the QRS–T angle—a recognized indicator of transmural dispersion of repolarization—remain unclear. The aim of our study was to investigate the impact of flecainide therapy on the QRS–T angle. Method: In this study, 200 patients who were prescribed flecainide therapy due to atrial or ventricular arrhythmias were included. Prior to the initiation of flecainide treatment, all patients underwent a 12-lead electrocardiogram (ECG) in which heart rate (HR), PR and QRS durations, QT, QTc, JT, Tp–Te intervals and the frontal plane QRS–T angle were measured. At the 1-month follow-up, patients underwent repeat ECG recording and were evaluated for both cardiac and non-cardiac side effects of flecainide. The same ECG parameters were measured again using the follow-up recordings. Changes in ECG parameters between the baseline and 1-month post-treatment were analyzed. Results: Following flecainide administration, the drug was discontinued in 18 patients (9%) due to adverse effects (11 cases of cardiac and seven cases of non-cardiac). HR significantly decreased (78 ± 22 bpm to 74 ± 15 bpm and p < 0.05). PR interval and QRS duration significantly increased (148 ± 23 ms to 156 ± 9 ms and 89 ± 17 ms to 99 ± 19 ms, respectively p < 0.05 for each). Additionally, JT interval (326 ± 27 ms vs. 334 ± 6 ms), QT interval (416 ± 24 ms vs. 434 ± 24 ms), QTc interval (431 ± 24 vs. 447 ± 25 ms) and Tp–Te interval (84 ± 17 vs. 87 ± 18 ms) all showed statistically significant increases after flecainide treatment (p < 0.05 for-each). However, no significant change was observed in the frontal QRS–T angle. Discussion: In patients receiving flecainide therapy for atrial and ventricular arrhythmias, prolongation was observed in atrioventricular conduction, ventricular depolarization and repolarization parameters as measured by ECG. However, no significant change was detected in the frontal QRS–T angle. Full article

Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, N = 763) or AI output disclosure days (AI-days, N = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive-related operational impacts. Full article

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots’ flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the “OODA loop” theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots’ flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities. Full article

Thedeep score-based diffusion (DeScoD) model performs well in electrocardiogram (ECG) denoising tasks. However, due to the theoretical error lower bound in approximating functions with linear transformations, it often lacks flexibility when fitting non-stationary noise, baseline wander, or morphologically variable features such as QRS complexes in ECG signals. In this paper, we propose a Kolmogorov–Arnold network enhanced deep score-based diffusion (KAN-DeScoD) model, which is the first to integrate Kolmogorov–Arnold network (KAN) layers into an ECG denoising diffusion model. By leveraging KAN’s adaptive activation functions, which more finely capture the complex structures within ECG signals, the model’s robustness in high-noise environments, as well as the accuracy and stability of signal reconstruction, are improved. We validate the effectiveness of the proposed method on the QT Database and the MIT-BIH Noise Stress Test Database (NSTDB). Experimental results show that under different shots and noise intensities, ours outperforms the DeScoD model across multiple metrics. The research results demonstrate the effectiveness of introducing KAN, which improves the model’s robustness in high-noise environments and the accuracy of signal reconstruction. Full article

(1) Background: Early treatment of sleep-disordered breathing (SDB) with adaptive servo-ventilation (ASV) after acute myocardial infarction (AMI) has been shown to improve myocardial salvage. This analysis evaluates nocturnal electrocardiogram (ECG) Holter data, derived from polygraphy in a randomised clinical trial (NCT02093377), to assess the occurrence of nocturnal cardiac arrhythmias in patients with SDB and to explore the effect of ASV therapy. (2) Methods: In the TEAM-ASV I trial, patients were stratified by the presence/absence of SDB, defined by an apnoea–hypopnoea index (AHI) ≥15 events/h assessed with polygraphy. Those with SDB were subsequently randomised to receive ASV in addition to standard AMI care. Guideline-conforming semi-automated ECG analysis of nocturnal cardiac arrhythmias was conducted via Holter–ECG software (custo diagnostic, version 5.4). (3) Results: Patients with SDB had an increased incidence of non-sustained ventricular tachycardia (NSVT) (SDB: n = 8 (16%) vs. no SDB: n = 1 (2%); p = 0.024) and premature atrial contractions (PAC) (SDB: 1.2/h [0.3, 3.4] vs. no SDB: 0.3/h [0.1, 1.2]; p = 0.017). In patients with SDB who were randomised to ASV treatment early after AMI, we found no reduction in cardiac arrhythmias when ASV was added to standard care. (4) Conclusions: After AMI, SDB was linked to increased NSVT and PAC. ASV treatment demonstrated neither a harmful nor a beneficial effect on the occurrence of nocturnal cardiac arrhythmias. Further trials are warranted to confirm these findings. Full article

Electrocardiogram (ECG)-based biometrics have emerged as a promising solution for continuous and intrinsic human identification; nevertheless, the robustness of these systems under realistic noise conditions remains a critical challenge for practical deployment. This work presents a theoretical and experimental analysis of how different noise types and levels affect ECG biometric recognition by comparing three methodological families: fiducial-based approaches using morphological features with traditional classifiers such as SVM and k-NN, non-fiducial methods based on signal compression and global descriptors, and Deep Learning models. Controlled distortions and additive noise injection into public ECG databases enable systematic quantification of feature degradation. Experimental validation is performed using the CardioWheel system, a real-world in-vehicle ECG acquisition platform, to evaluate performance under realistic motion and noise conditions. The methodological framework proposed for robustness evaluation and noise-aware training is inherently generic and can be extended to other biometric tasks subject to noise. Results show that different algorithmic families exhibit distinct resilience profiles under noise contamination and reveal a practical signal quality boundary for reliable ECG biometric recognition, with performance deteriorating under severe noise conditions. Noise-aware training improves robustness, particularly for Deep Learning and SVM-based classifiers, highlighting the trade-off between interpretability and robustness. By bridging theoretical analysis and applied experimentation, this work provides practical signal quality guidelines for real-world ECG biometric systems. Full article

Environmental noise may interfere with the reading experience by increasing cognitive load and psychophysiological arousal, yet these effects are difficult to perceive and communicate in real time. This study presents Reading Noise, an interactive installation that combines physiological sensing and sound-driven visualization to externalize perceived noise-related disturbance and psychophysiological strain during reading. In a controlled experiment, 46 participants completed reading tasks under four levels of background conversational noise (0–30, 31–60, 61–90, and >90 dB) while ambient sound level, electrodermal activity (EDA), and electrocardiogram (ECG) were recorded in real time. Following data quality screening, inferential statistical analyses were performed on the analyzable physiological subset (n = 16). Based on these data, a hybrid mapping strategy combining rule-based assignment and LMM-informed exploratory calibration was developed to map acoustic and physiological changes onto dynamic text-based visual parameters, including deformation intensity, jitter, and motion instability, for real-time feedback. Within the analyzable subset, noise level was associated with significant changes in the recorded physiological indicators (all p < 0.05): skin conductance level (SCL) and skin conductance responses per minute (SCRs/min) increased (4.69 ± 2.13 to 5.93 ± 2.19 μS; 1.49 ± 1.59 to 2.51 ± 2.13), whereas the percentage of successive RR intervals differing by more than 50 ms (pNN50) and the root mean square of successive differences (RMSSD) decreased (15.84 ± 16.52% to 10.57 ± 11.35%; 36.63 ± 17.62 to 29.67 ± 16.66 ms). Subjective cognitive load also increased significantly (2.06 ± 0.29 to 6.38 ± 0.31). A follow-up installation study with 24 cross-disciplinary participants, with reported group interaction observations drawn from a 12-participant subset, suggested that the installation may facilitate shared interpretation of attention-related disruption and cognitive strain, indicating the potential of physiology-informed visual translation as a boundary object approach for empathetic, sound-mediated communication. Full article

Doxorubicin (DOX) is a widely used anthracycline, but its clinical use is limited by dose-dependent cardiotoxicity. This experimental study evaluated the cardioprotective potential of empagliflozin (EMPA) against DOX-induced cardiotoxicity. Thirty healthy adult rats were randomized into five groups (n = 6): control (group I), EMPA (group II), EMPA + DOX (group III), DOX (group IV), and EMPA-preconditioning + DOX (group V). EMPA was administered orally at 10 mg/kg/day, either concomitantly with DOX or as a 14-day preconditioning course. Cumulative DOX exposure reached 15 mg/kg to establish a reproducible cardiotoxicity model. Serial electrocardiograms (ECGs) were recorded, blood samples were collected, and hearts were harvested for detailed histopathological analysis. Compared with the control group, group IV demonstrated significant QT/QTc prolongation and repolarization abnormalities, marked troponin elevation, and characteristic histological lesions, including cardiomyocyte vacuolization, loss of striations, diffuse inflammation, myocyte atrophy, and increased fibrosis. In groups receiving EMPA with DOX exposure (groups III and V), ECG changes were attenuated, troponin elevation was lower, and structural myocardial damage was substantially reduced, with better preservation of cardiomyocyte architecture and less fibrosis. These results suggest that EMPA provides significant cardioprotection against DOX-induced cardiotoxicity in rats, supporting further investigation of SGLT2 inhibitors in cardio-oncology. Full article