Search Results (1,805)

Background/Objective: Atrial fibrillation [AF] is the most common arrhythmia encountered in clinical practice and significantly increases the risk of stroke, peripheral embolism, and mortality. With the rapid advancement in artificial intelligence [AI] technologies, there is growing potential to enhance the tools used in AF detection and diagnosis. This scoping review aimed to synthesize the current knowledge on the application of AI, particularly machine learning [ML], in identifying and diagnosing AF in clinical settings. Methods: Following the PRISMA ScR guidelines, a comprehensive search was conducted using the MEDLINE, PubMed, SCOPUS, and EMBASE databases, targeting studies involving AI, cardiology, and diagnostic tools. Precisely 2635 articles were initially identified. After duplicate removal and detailed evaluation of titles, abstracts, and full texts, 30 studies were selected for review. Additional relevant studies were included to enrich the analysis. Results: AI models, especially ML-based models, are increasingly used to optimize AF diagnosis. Deep learning, a subset of ML, has demonstrated superior performance by automatically extracting features from large datasets without manual intervention. Self-learning algorithms have been trained using diverse data, such as signals from 12-lead and single-lead electrocardiograms, and photoplethysmography, providing accurate AF detection across various modalities. Conclusions: AI-based models, particularly those utilizing deep learning, offer faster and more accurate diagnostic capabilities than traditional methods with equal or superior reliability. Ongoing research is further enhancing these algorithms using larger datasets to improve AF detection and management in clinical practice. These advancements hold promise for significantly improving the early diagnosis and treatment of AF. Full article

Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings. Two devices with PPG and one device with an ECG sensor were worn by 25 healthy volunteers for 10 days. PPG-derived HR and HRV showed reasonable accuracy and reliability, particularly during sleep, with mean absolute error < 1 beat for HR and 6–15 ms for HRV. The relative error of HRV estimated from PPG varied with activity type and was higher than during the resting state by 14–51%. The accuracy of HR/HRV was impacted by the proportion of usable data, body posture, and epoch length. The multi-scale peak and trough detection algorithm demonstrated superior performance in detecting beats from PPG signals, with an F1 score of 89% during sleep. The study demonstrates the trade-offs of utilizing PPG measurements for remote monitoring in daily life and identifies optimal use conditions by recommending enhancements. Full article

Background: A reversal of time difference between the onset of early diastolic velocity (e’) during tissue Doppler imaging and the onset of mitral inflow (E) has been observed in cases of elevated left atrial pressure. Whether this interval (T_e’-E) may be useful to assess right atrial pressure has never been investigated, neither in healthy subjects nor in pulmonary hypertension patients. Methods: Right ventricular T_e’-E was assessed in patients with pre-capillary pulmonary hypertension and compared with healthy volunteers who underwent comprehensive echocardiography examination. T_e’-E is the difference between the interval from R wave at the superimposed electrocardiogram to the e’ wave during right ventricular tissue Doppler imaging and the interval from the R wave to transtricuspid E wave during pulsed wave Doppler imaging. Right atrial pressure was invasively measured in pulmonary hypertension patients. Results: Fifty-six patients were enrolled. T_e’-E was prolonged in pulmonary hypertension subjects compared with healthy subjects (p < 0.001). Amongst the pulmonary hypertension patients, strong correlations were found between T_e’-E and right atrial pressure (r = −0.885, p < 0.001), systolic pulmonary pressure (r = −0.85, p < 0.001) and the duration of tricuspid regurgitation (r = 0.72, p < 0.001). The area under the receiver operating characteristic curve of T_e’-E in identifying right atrial pressure higher than 15 mm of mercury was 0.992 (sensitivity 100%, specificity 83%). Conclusions: In contrast to the left ventricle, there is a delay in the proto-diastolic filling in pulmonary hypertension patients, which correlates with the increase in systolic pulmonary arterial pressure, right atrial pressure, tricuspid regurgitation duration and restrictive diastolic pattern. Full article

Background: Cardiac resynchronization therapy is an essential treatment for heart failure patients. Candidates typically have cardiomyopathy accompanied by delayed electrical activation in the left ventricular lateral wall, causing uncoordinated contractions and worsening heart failure. Heart failure severity can be assessed with functional tests: the cardiopulmonary test, which is a maximal exercise test, remains the gold standard, but the 6 min walk test has emerged as an easier, faster, and more comfortable alternative to be used by clinicians to adjust treatment protocols for cardiovascular and pulmonary conditions. Methods: This is a prospective observational study that included 69 patients from a single healthcare facility, and the purpose was to determine if the 6 min walk test results could be associated with changes in various electrocardiographic, clinical, functional, and demographic parameters. All the parameters and the 6 min walk distance were recorded at four key time moments: before the procedure and after 6, 9, and 12 months. The electrocardiographic parameters were obtained from the patients’ electrocardiograms recorded in the four key moments and included variables such as QRS area, duration, percentage of biventricular pacing, and many others, while the functional variables included the monitored intraprocedural systolic blood pressure and the end-systolic left ventricular volume. We also aimed to check if clinical conditions such as diabetes and chronic kidney disease and demographic variables such as age or sex have any impact. Results and Conclusions: All this research was performed in order to identify which parameters hold a predictive value and can serve as future criteria for better patient selection and for defining a proper resynchronization outcome. The study shows that parameters such as diabetes and QRS duration have an impact over the 6 min walk distance. Also, newer variables such as the QRS area and the R/S ratio may represent a direction worth studying in order to predict the outcomes of cardiac resynchronization therapy. Full article

Premature ventricular contractions (PVCs) are a common arrhythmia characterized by ectopic excitations within the ventricles. Accurately estimating the ablation site using an electrocardiogram (ECG) is crucial for the initial classification of PVC origins, typically focusing on the right and left ventricular outflow tracts. However, finer classification, specifically identifying the left cusp (LC), anterior cusp (AC), and right cusp (RC), is essential for detailed preoperative planning. This study aims to improve the accuracy of cardiac waveform source estimation and classification in 27 patients with PVCs originating from the pulmonary valve. We utilized an anatomical human model and electromagnetic simulations to estimate wave source positions from 12-lead ECG data. Time-series source points were identified for each measured ECG waveform, focusing on the moment when the distance between the estimated wave source and the pulmonary valve was minimal. Computational analysis revealed that the distance between the estimated wave source and the pulmonary valve was reduced to less than 1 cm, with LC localization achieving errors under 5 mm. Additionally, 74.1% of the subjects were accurately classified into the correct origin (LC, AC, or RC), with each origin demonstrating the highest percentage of subjects corresponding to the targeted excitation origin. Our findings underscore the novel potential of this source localization method as a valuable complement to traditional waveform classification, offering enhanced diagnostic precision and improved preoperative planning for PVC ablation procedures. Full article

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts. Full article

Changes in cardiac function and morphology are reflected in variations in the electrocardiogram (ECG) and, in turn, in the cardiac risk indices derived from it. These variations have led to the introduction of normalization as a step to compensate for possible biasing factors responsible for inter- and intra-subject differences, which can affect the accuracy of ECG-derived risk indices in assessing cardiac risk. The aim of this work is to perform a scoping review to provide a comprehensive collection of open-access published research that examines normalized ECG-derived parameters used as markers of cardiac anomalies or instabilities. The literature search was conducted from February to July 2024 in the major global electronic bibliographic repositories. Overall, 39 studies were selected. Results suggest extensive use of normalization on heart rate variability-related indices (49% of included studies), QT-related indices (18% of included studies), and T-wave alternans (5% of included studies), underscoring their recognized importance and suggesting that normalization may enhance their role as clinically useful risk markers. However, the primary objective of the included studies was not to evaluate the effect of normalization itself; thus, further research is needed to definitively assess the impact and advantages of normalization across various ECG-derived parameters. Full article

Background: Over the last five decades, a fair number of echocardiographic studies have evaluated the prevalence of mitral valve prolapse (MVP) in various cohorts of individuals, including heterogeneous study populations. The present systematic review has been primarily designed to summarize the main findings of these studies and to estimate the overall MVP prevalence in the general community. Methods: All echocardiographic studies assessing the MVP prevalence in various cohorts of individuals, selected from PubMed and EMBASE databases, were included. There was no limitation of time period. The risk of bias was assessed by using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: The full texts of 21 studies with 1354 MVP individuals out of 63,723 participants were analyzed. The overall pooled prevalence of MVP was 4.9% (range of 0.6–21%). When dividing the studies in two groups according to the echocardiographic criteria used for MVP diagnosis (less specific old criteria or more specific new criteria, respectively), the estimated pooled prevalence of MVP was 7.8% (range of 2–21%) for the older studies (performed between 1976 and 1998) and 2.2% (range of 0.6–4.2%) for the more recent ones (conducted between 1999 and 2021). Potential selection bias, hospital- or referral-based series, and the use of less specific echocardiographic criteria for MVP diagnosis have been indicated as the main reasons for the higher MVP prevalence detected by the older studies. MVP was commonly associated with a narrow antero-posterior thoracic diameter, isolated ventricular premature beats and nonspecific ST-T-wave abnormalities on a resting electrocardiogram, mild-to-moderate mitral regurgitation (MR), the reduced probability of obstructive coronary artery disease, and a low frequency of serious complications, such as severe MR, infective endocarditis, heart failure, stroke, and atrial fibrillation. Conclusions: MVP has a low prevalence in the general population, regardless of age, gender, and ethnicity, and is associated with a good outcome. Full article

Ventricular pre-excitation (VP) is a cardiac disorder characterized by the presence of an accessory pathway (AP) that bypasses the atrioventricular node (AVN), which, although often asymptomatic, exposes individuals to an increased risk of re-entrant supraventricular tachycardias and sudden cardiac death (SCD) due to rapid atrial fibrillation (AF) conduction. This condition is particularly significant in sports cardiology, where preparticipation ECG screening is routinely performed on athletes. Professional athletes, given their elevated risk of developing malignant arrhythmias, require careful assessment. Early identification of VP and proper risk stratification are crucial for determining the most appropriate management strategy and ensuring the safety of these individuals during competitive sports. Non-invasive tools, such as resting electrocardiograms (ECGs), ambulatory ECG monitoring, and exercise stress tests, are commonly employed, although their interpretation can sometimes be challenging. This review aims to provide practical tips and electrocardiographic clues for detecting VP beyond the classical triad (short PR interval, delta wave, and prolonged QRS interval) and offers guidance on non-invasive risk stratification. Although the diagnostic gold standard remains invasive electrophysiological study, appropriate interpretation of the ECG can help limit unnecessary referrals for young, often asymptomatic, athletes. Full article

Background: Licorice (glycyrrhiza glabra) cough syrup intoxication is manifested with refractory hypokalemia, hypertension, and metabolic alkalosis. The transformation of glycyrrhiza glabra metabolic into glycyrrhetic acid after ingestion further inhibits the 11-β-hydroxysteroid dehydrogenase-2 enzyme, impeding the conversion of cortisol into cortisone. The accumulation of cortisol can also stimulate mineralocorticoid receptors, which leads to a pseudo-hyperaldosteronism-like effect. Case Presentation: We report a 60-year-old male patient with licorice intoxication due to the chronic consumption of licorice cough syrup. He exhibited a transient seizure lasting approximately one minute. Initially, hypokalemia (potassium level was 2.0 mmol/L), metabolic alkalosis, and QT interval prolongation with premature ventricular complexes were demonstrated on his electrocardiogram. Despite the administration of both intravenous and oral potassium supplements over two days, there was no significant improvement in hypokalemia. Spironolactone, an aldosterone receptor antagonist, was administered in addition to ongoing potassium supplementation from the 3rd day. This intervention led to a rapid normalization of hypokalemia in one day. The patient was ultimately discharged on the 6th day without any subsequent complications. Conclusions: The licorice-induced chronic intoxication, which led to pseudo-hyperaldosteronism and refractory hypokalemia, was successfully managed with aggressive potassium supplementation and spironolactone treatment. Full article

Background/Objectives: In cases of myocarditis, electrocardiograms (ECGs) may suggest a pattern of ST-segment elevation myocardial infarction (STEMI) or non-ST-segment elevation myocardial infarction (NSTEMI). NSTEMI patterns are less frequent in myocarditis cases, but it remains unclear if the presence of ST-segment elevation in myocarditis cases is related to a more severe condition and more damage in the myocardium. Methods: This is a retrospective study involving 38 patients admitted to hospital with myocarditis. Patients were divided into two groups: patients with ST-segment elevation (STE) patterns in the ECG (25), and patients without ST-segment elevation (non-STE) patterns (13). The data compared included results from epidemiological, laboratory, and instrumental tests. Data were analysed using IBM SPSS Statistics v26.0. A p value of <0.05 was established as the threshold for statistical significance. Results: C-reactive protein (CRP) levels were higher in the STE group (103.40 ± 82.04 mg/L vs. 43.54 ± 61.93 mg/L, p = 0.017). The left ventricle ejection fraction (LVEF) was significantly higher in the non-STE pattern group (49.71 ± 4.14 vs. 56.58 ± 3.99, p < 0.001). A lower LVEF correlates with higher TnI levels (r= −0.353, p = 0.032) and higher CRP levels (r = −0.554, p < 0.001). Lower left ventricle (LV) strain correlates with higher levels of Troponin I (TnI) (r = −0.641, p = 0.013). Conclusions: LVEFs in the STE group were lower compared to those in the non-STE pattern group. STE pattern was associated with higher CRP levels. Higher TnI levels in cases of myocarditis were associated with lower LV strain and lower LVEF; higher CRP levels also correlated with lower LVEF. Based on a 6-month echocardiographic follow-up, the prognosis of myocarditis was favourable. Full article

Although mesenchymal stem cells (MSCs) insertion has gained recent attention as a joint-preserving procedure, no study has conducted direct intralesional implantation of human umbilical cord-derived MSCs (hUCMSCs) in patients with ONFH. This is a protocol for a phase 1 clinical trial designed to assess the safety and exploratory efficacy of human umbilical cord-derived osteoblasts (hUC-Os), osteogenic differentiation-induced cells from hUCMSCs, in patients with early-stage ONFH. Nine patients with Association Research Circulation Osseous (ARCO) stage 1 or 2 will be assigned to a low-dose (1 × 10⁷ hUC-O cells, n = 3), medium-dose (2 × 10⁷ cells, n = 3), and high-dose group (4 × 10⁷ cells, n = 3) in the order of their arrival at the facility, and, depending on the occurrence of dose-limiting toxicity, up to 18 patients can be enrolled by applying the 3 + 3 escalation method. We will perform hUC-O (CF-M801) transplantation combined with core decompression and follow-up for 12 weeks according to the study protocol. Safety will be determined through adverse event assessment, laboratory tests including a panel reactive antibody test, vital sign assessment, physical examination, and electrocardiogram. Efficacy will be explored through the change in pain visual analog scale, Harris hip score, Western Ontario and McMaster Universities Osteoarthritis Index, ARCO stage, and also size and location of necrotic lesion according to Japanese Investigation Committee classification before and after the procedure. Joint preservation is important, particularly in younger, active patients with ONFH. Confirmation of the safety and efficacy of hUC-Os will lead to a further strategy to preserve joints for those suffering from ONFH and improve our current knowledge of cell therapy. Full article

Echocardiography (ECG) is a noninvasive technology that is widely used for recording heartbeats and diagnosing cardiac arrhythmias. However, interpreting ECG signals is challenging and may require substantial time from medical specialists. The evolution of technology and artificial intelligence has led to advances in the study and development of automatic arrhythmia classification systems to aid in medical diagnoses. Within this context, this paper introduces a framework for classifying cardiac arrhythmias on the basis of a multimodal convolutional neural network (CNN) with an adaptive attention mechanism. ECG signal segments are transformed into images via the Hilbert space-filling curve (HSFC) and recurrence plot (RP) techniques. The framework is developed and evaluated using the MIT-BIH public database in alignment with AAMI guidelines (ANSI/AAMI EC57). The evaluations accounted for interpatient and intrapatient paradigms, considering variations in the input structure related to the number of ECG leads (lead MLII and V1 + MLII). The results indicate that the framework is competitive with those in state-of-the-art studies, particularly for two ECG leads. The accuracy, precision, sensitivity, specificity and F1 score are 98.48%, 94.15%, 80.23%, 96.34% and 81.91%, respectively, for the interpatient paradigm and 99.70%, 98.01%, 97.26%, 99.28% and 97.64%, respectively, for the intrapatient paradigm. Full article

The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM), yet their treatments and prognoses are quite different. Early differentiation between these conditions yields positive outcomes, but the gold standard (coronary angiography) is invasive. The potential use of ECG signals based on variational mode decomposition (VMD) as an alternative remains underexplored. An ECG dataset containing 87 subjects (44 DCM, 43 ICM) is pre-processed for denoising and heartbeat division. Firstly, the ECG signal is processed by empirical mode decomposition (EMD) and VMD. And then, five modes are determined by correlation analysis. Secondly, bispectral analysis is conducted on these modes, extracting corresponding bispectral and nonlinear features. Finally, the features are processed using five machine learning classification models, and a comparative assessment of their classification efficacy is facilitated. The results show that the technique proposed provides a better categorization for DCM and ICM using ECG signals compared to previous approaches, with a highest classification accuracy of 98.30%. Moreover, VMD consistently outperforms EMD under diverse conditions such as different modes, leads, and classifiers. The superiority of VMD on ECG analysis is verified. Full article

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications. Full article