Recommendations for the Standardization and Interpretation of the Electrocardiogram

Selection of number of clusters and warping penalty in clustering functional electrocardiogram

Clustering functional data aims to identify unique functional patterns in the entire domain, but this can be challenging due to phase variability that distorts the observed patterns. Curve registration can be used to remove this variability, but determining the appropriate level of warping flexibility can be complicated. Curve registration also requires a target to which a functional object is aligned, typically the cross-sectional mean of functional objects within the same cluster. However, this mean is unknown prior to clustering. Furthermore, there is a trade-off between flexible warping and the number of resulting clusters. Removing more phase variability through curve registration can lead to fewer remaining variations in the functional data, resulting in a smaller number of clusters. Thus, the optimal number of clusters and warping flexibility cannot be uniquely identified. We propose to use external information to solve the identification issue. We define a cross validated Kullback-Leibler information criterion to select the number of clusters and the warping penalty. The criterion is derived from the predictive classification likelihood considering the joint distribution of both the functional data and external variable and penalizes the uncertainty in the cluster membership. We evaluate our method through simulation and apply it to electrocardiographic data collected in the Chronic Renal Insufficiency Cohort study. We identify two distinct clusters of electrocardiogram (ECG) profiles, with the second cluster exhibiting ST segment depression, an indication of cardiac ischemia, compared to the normal ECG profiles in the first cluster.

Prospective Multicenter Screening With High-Sensitivity Cardiac Troponin T for Wild-Type Transthyretin Cardiac Amyloidosis in Outpatient and Community-Based Settings

BACKGROUND

High-sensitivity cardiac troponin T (hs-cTnT) was proposed as a simple and useful diagnostic tool for cardiac amyloidosis (CA). We performed exploratory systemic screening using hs-cTnT to detect wild-type transthyretin CA (ATTRwt-CA) in outpatient and community-based settings.

METHODS AND RESULTS

This study was a prospective multicenter study including 8 internal medicine clinics in Kochi Prefecture, Japan. Consecutive individuals aged ≥70 years who visited those clinics as outpatients were enrolled. Patients with a prior diagnosis of CA or a history of heart failure hospitalization were excluded. We measured hs-cTnT levels in the enrolled individuals at each clinic, and those with elevated hs-cTnT levels (≥0.03ng/mL) received further detailed examination, including remeasurement of hs-cTnT. The diagnosis of ATTRwt-CA was confirmed by biopsy-proven transthyretin. Of 1,141 individuals enrolled in the study, 55 (4.8%) had elevated hs-cTnT levels. Of the 33 patients who underwent further examination, 22 had elevated hs-cTnT levels at remeasurement. Finally, 2 men were diagnosed with ATTRwt-CA. The prevalence of ATTRwt-CA was 9.1% (2/22) among patients with elevated hs-cTnT levels at two examinations, and at least 0.18% (2/1,141) in the whole study population.

CONCLUSIONS

Measurement of hs-cTnT will help to screen for patients with undiagnosed ATTRwt-CA in primary care practice.

An approach to the detection of pain from autonomic and cortical correlates

OBJECTIVE

To assess the value of combining brain and autonomic measures to discriminate the subjective perception of pain from other sensory-cognitive activations.

METHODS

20 healthy individuals received 2 types of tonic painful stimulation delivered to the hand: electrical stimuli and immersion in 10 Celsius degree (°C) water, which were contrasted with non-painful immersion in 15 °C water, and stressful cognitive testing. High-density electroencephalography (EEG) and autonomic measures (pupillary, electrodermal and cardiovascular) were continuously recorded, and the accuracy of pain detection based on combinations of electrophysiological features was assessed using machine learning procedures.

RESULTS

Painful stimuli induced a significant decrease in contralateral EEG alpha power. Cardiac, electrodermal and pupillary reactivities occurred in both painful and stressful conditions. Classification models, trained on leave-one-out cross-validation folds, showed low accuracy (61-73%) of cortical and autonomic features taken independently, while their combination significantly improved accuracy to 93% in individual reports.

CONCLUSIONS

Changes in cortical oscillations reflecting somatosensory salience and autonomic changes reflecting arousal can be triggered by many activating signals other than pain; conversely, the simultaneous occurrence of somatosensory activation plus strong autonomic arousal has great probability of reflecting pain uniquely.

SIGNIFICANCE

Combining changes in cortical and autonomic reactivities appears critical to derive accurate indexes of acute pain perception.

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.

Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

Evaluating the impacts of digital ECG denoising on the interpretive capabilities of healthcare professionals

Aims

Electrocardiogram (ECG) interpretation is an essential skill across multiple medical disciplines; yet, studies have consistently identified deficiencies in the interpretive performance of healthcare professionals linked to a variety of educational and technological factors. Despite the established correlation between noise interference and erroneous diagnoses, research evaluating the impacts of digital denoising software on clinical ECG interpretation proficiency is lacking.

Methods and results

Forty-eight participants from a variety of medical professions and experience levels were prospectively recruited for this study. Participants' capabilities in classifying common cardiac rhythms were evaluated using a sequential blinded and semi-blinded interpretation protocol on a challenging set of single-lead ECG signals (42 × 10 s) pre- and post-denoising with robust, cloud-based ECG processing software. Participants' ECG rhythm interpretation performance was greatest when raw and denoised signals were viewed in a combined format that enabled comparative evaluation. The combined view resulted in a 4.9% increase in mean rhythm classification accuracy (raw: 75.7% ± 14.5% vs. combined: 80.6% ± 12.5%, P = 0.0087), a 6.2% improvement in mean five-point graded confidence score (raw: 4.05 ± 0.58 vs. combined: 4.30 ± 0.48, P < 0.001), and 9.7% reduction in the mean proportion of undiagnosable data (raw: 14.2% ± 8.2% vs. combined: 4.5% ± 2.4%, P < 0.001), relative to raw signals alone. Participants also had a predominantly positive perception of denoising as it related to revealing previously unseen pathologies, improving ECG readability, and reducing time to diagnosis.

Conclusion

Our findings have demonstrated that digital denoising software improves the efficacy of rhythm interpretation on single-lead ECGs, particularly when raw and denoised signals are provided in a combined viewing format, warranting further investigation into the impact of such technology on clinical decision-making and patient outcomes.

Risk profiles for ventricular arrhythmias in hypertrophic cardiomyopathy through clustering analysis including left ventricular strain

AIMS

The prediction of ventricular arrhythmia (VA) in hypertrophic cardiomyopathy (HCM) remains challenging. We sought to characterize the VA risk profile in HCM patients through clustering analysis combining clinical and conventional imaging parameters with information derived from left ventricular longitudinal strain analysis (LV-LS).

METHODS

A total of 434 HCM patients (65% men, mean age 56 years) were included from two referral centers and followed longitudinally (mean duration 6 years). Mechanical and temporal parameters were automatically extracted from the LV-LS segmental curves of each patient in addition to conventional clinical and imaging data. A total of 287 features were analyzed using a clustering approach (k-means). The principal endpoint was VA.

RESULTS

4 clusters were identified with a higher rhythmic risk for clusters 1 and 4 (VA rates of 26%(28/108), 13%(13/97), 12%(14/120), and 31%(34/109) for cluster 1,2,3 and 4 respectively). These 4 clusters differed mainly by LV-mechanics with a severe and homogeneous decrease of myocardial deformation for cluster 4, a small decrease for clusters 2 and 3 and a marked deformation delay and temporal dispersion for cluster 1 associated with a moderate decrease of the GLS (p < 0.0001 for GLS comparison between clusters). Patients from cluster 4 had the most severe phenotype (mean LV mass index 123 vs. 112 g/m2; p = 0.0003) with LV and left atrium (LA) remodeling (LA-volume index (LAVI) 46.6 vs. 41.5 ml/m2, p = 0.04 and LVEF 59.7 vs. 66.3%, p < 0.001) and impaired exercise capacity (% predicted peak VO2 58.6 vs. 69.5%; p = 0.025).

CONCLUSION

Processing LV-LS parameters in HCM patients 4 clusters with specific LV-strain patterns and different rhythmic risk levels are identified. Automatic extraction and analysis of LV strain parameters improves the risk stratification for VA in HCM patients.

Tensor cardiography: A novel ECG analysis of deviations in collective myocardial action potential transitions based on point processes and cumulative distribution functions

To improve clinical diagnoses, assessments of potential cardiac disease risk, and predictions of lethal arrhythmias, the analysis of electrocardiograms (ECGs) requires a more accurate method of weighting waveforms to efficiently detect abnormalities that appear as minute strains in the waveforms. In addition, the inverse problem of estimating the myocardial action potential from the ECG has been a longstanding challenge. To analyze the variance of the ECG waveforms and to estimate collective myocardial action potentials (APs) from the ECG, we designed a model equation incorporating the probability densities of Gaussian functions of time-series point processes in the cardiac cycle and dipoles of the collective APs in the myocardium. The equation, which involves taking the difference between the cumulative distribution functions (CDFs) that represent positive endocardial and negative epicardial potentials, fits both R and T waves. The mean, standard deviation, weights, and level of each cumulative distribution function (CDF) are metrics for the variance of the transition state of the collective myocardial AP. Clinical ECGs of myocardial ischemia during coronary intervention show abnormalities in the aforementioned specific elements of the tensor associated with repolarization transition variance earlier than in conventional indicators of ischemia. The tensor can be used to evaluate the beat-to-beat dynamic repolarization changes between the ventricular epi and endocardium in terms of the Mahalanobis distance (MD). This tensor-based cardiography that uses the differences between CDFs to show changes in collective myocardial APs has the potential to be a new analysis tool for ECGs.

Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder-decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm's measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (-2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (-2.4 ± 5.4 ms), and QT-interval (-0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance.

Tighter is better: Can a simple and cost-free parameter predict response to cardiac synchronization therapy?

BACKGROUND

Several studies have evaluated the role of QRS duration (QRSd) or QRS narrowing as a predictor of response to cardiac resynchronization therapy (CRT) to reduce nonresponders.

AIM

Our study aimed to determine the correlation between the relative change in QRS index (QI) compared to clinical outcome and prognosis in patients who underwent CRT implantation.

METHODS

A three-centers study involving 398 patients with a CRT device was conducted. Clinical, echocardiographic and pharmacological variables, QRSd before and after CRT implantation and QI were measured.

RESULTS

In a 6-month follow-up, a significant improvement in left ventricular ejection fraction (LVEF), left ventricular end-diastolic and systolic volumes (LVEDV and LVESV) were observed. QI was related to reverse remodeling (multiple r-squared: 0.48, adjusted r-squared: 0.43, p = .001), and the cut-off value that best predicted LV reverse remodeling after 6 months of CRT was 12.25% (AUC 0.7, p = .001). At 24 months, a statistically significant difference was found between patients with a QI ≤ 12.25% and those with a QI > 12.25% regarding NYHA class worsening (p = .04). The mean of the QI of patients who died from cardiovascular causes was lower than patients who died of other causes (p = .0179). A correlation between pre-CRT QRSd/LVEDV and QI was observed (r = + 0.20; p = .0003). A higher QRSd/LVEDV ratio was associated with an improved LVEF, LVEDV, and LVESV (p < .0001) at follow-up.

CONCLUSIONS

QI narrowing after CRT was related to greater echocardiographic reverse remodeling and a lower rate of adverse events (death or cardiovascular hospitalizations). The QI can improve the prediction of adverse events in a population with CRT regardless of comorbidities according to the Charlson Comorbidity Index. QI could be used to predict CRT response.

Predictor of A4 amplitude using preprocedural electrocardiography in patients with leadless pacemakers

BACKGROUND

Based on historical studies of leadless pacemakers (LPs), high atrioventricular synchrony (AVS) with mechanical sensing-based VDD pacing is largely influenced by A4 amplitude. A limited study investigated the predictors of A4 amplitude using clinical and echocardiographic parameters.

OBJECTIVE

The purpose of this study was to investigate the predictors of A4 amplitude preoperatively to select patients who could benefit the most from AVS among patients with VDD LPs (Micra-AV, Medtronic).

METHODS

Data from patients who received Micra-AV implantations from November 2021 to August 2023 at Tottori University Hospital were analyzed. Twelve-lead electrocardiography and transthoracic echocardiography were performed before the Micra-AV implantations. To assess the electrical indices associated with the A4 signal, electrocardiographic morphologic P-wave parameters were analyzed, including P-wave duration, P-wave amplitude, maximum deflection index (MDI), and P-wave dispersion.

RESULTS

A total of 50 patients who underwent Micra-AV implantations (median age 84 years; 64% male) were included and divided into 2 groups based on the median value of A4 amplitude, the high-A4 group (A4 amplitude >2.5 m/s2; n = 26), and low-A4 group (A4 amplitude ≤2.5 m/s2; n = 24). There was a significant difference between the high-A4 and low-A4 groups with regard to left ventricular ejection fraction (P = .01), P-wave dispersion (P = .01), and MDI (P <.001). Multivariate logistic analysis revealed that lower MDI was an independent predictor of high A4-amplitude (odds ratio 0.78; 95% confidence interval 0.67-0.92; P = 0.003).

CONCLUSION

Preoperative electrocardiographic evaluations of P-wave morphology may be useful for predicting A4 amplitude. MDI was the only independent A4 amplitude predictor that seemed promising for selecting Micra-AV patients.

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.

RECENT FINDINGS

AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.

Empagliflozin's role in reducing ventricular repolarization heterogeneity: insights into cardiovascular mortality decline from the EMPATHY-HEART trial

BACKGROUND

The incidence of myocardial infarction (MI) and sudden cardiac death (SCD) is significantly higher in individuals with Type 2 Diabetes Mellitus (T2DM) than in the general population. Strategies for the prevention of fatal arrhythmias are often insufficient, highlighting the need for additional non-invasive diagnostic tools. The T-wave heterogeneity (TWH) index measures variations in ventricular repolarization and has emerged as a promising predictor for severe ventricular arrhythmias. Although the EMPA-REG trial reported reduced cardiovascular mortality with empagliflozin, the underlying mechanisms remain unclear. This study investigates the potential of empagliflozin in mitigating cardiac electrical instability in patients with T2DM and coronary heart disease (CHD) by examining changes in TWH.

METHODS

Participants were adult outpatients with T2DM and CHD who exhibited TWH > 80 µV at baseline. They received a 25 mg daily dose of empagliflozin and were evaluated clinically including electrocardiogram (ECG) measurements at baseline and after 4 weeks. TWH was computed from leads V4, V5, and V6 using a validated technique. The primary study outcome was a significant (p < 0.05) change in TWH following empagliflozin administration.

RESULTS

An initial review of 6,000 medical records pinpointed 800 patients for TWH evaluation. Of these, 412 exhibited TWH above 80 µV, with 97 completing clinical assessments and 90 meeting the criteria for high cardiovascular risk enrollment. Empagliflozin adherence exceeded 80%, resulting in notable reductions in blood pressure without affecting heart rate. Side effects were generally mild, with 13.3% experiencing Level 1 hypoglycemia, alongside infrequent urinary and genital infections. The treatment consistently reduced mean TWH from 116 to 103 µV (p = 0.01).

CONCLUSIONS

The EMPATHY-HEART trial preliminarily suggests that empagliflozin decreases heterogeneity in ventricular repolarization among patients with T2DM and CHD. This reduction in TWH may provide insight into the mechanism behind the decreased cardiovascular mortality observed in previous trials, potentially offering a therapeutic pathway to mitigate the risk of severe arrhythmias in this population.

TRIAL REGISTRATION

NCT: 04117763.

A graph-based cardiac arrhythmia classification methodology using one-lead ECG recordings

In this study, we present a novel graph-based methodology for an accurate classification of cardiac arrhythmia diseases using a single-lead electrocardiogram (ECG). The proposed approach employs the visibility graph technique to generate graphs from time signals. Subsequently, informative features are extracted from each graph and then fed into classifiers to match the input ECG signal with the appropriate target arrhythmia class. The six target classes in this study are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), atrial premature contraction (A), and fusion (F) beats. Three classification models were explored, including graph convolutional neural network (GCN), multi-layer perceptron (MLP), and random forest (RF). ECG recordings from the MIT-BIH arrhythmia database were utilized to train and evaluate these classifiers. The results indicate that the multi-layer perceptron model attains the highest performance, showcasing an average accuracy of 99.02%. Following closely, the random forest achieves a strong performance as well, with an accuracy of 98.94% while providing critical intuitions.

Histological assessment of cardiac amyloidosis in patients undergoing transcatheter aortic valve replacement

AIMS

Studies have reported a strongly varying co-prevalence of aortic stenosis (AS) and cardiac amyloidosis (CA). We sought to histologically determine the co-prevalence of AS and CA in patients undergoing transcatheter aortic valve replacement (TAVR). Consequently, we aimed to derive an algorithm to identify cases in which to suspect the co-prevalence of AS and CA.

METHODS AND RESULTS

In this prospective, monocentric study, endomyocardial biopsies of 162 patients undergoing TAVR between January 2017 and March 2021 at the University Medical Centre Göttingen were analysed by one pathologist blinded to clinical data using haematoxylin-eosin staining, Elastica van Gieson staining, and Congo red staining of endomyocardial biopsies. CA was identified in only eight patients (4.9%). CA patients had significantly higher N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (4356.20 vs. 1938.00 ng/L, P = 0.034), a lower voltage-to-mass ratio (0.73 vs. 1.46 × 10-2 mVm2/g, P = 0.022), and lower transaortic gradients (Pmean 17.5 vs. 38.0 mmHg, P = 0.004) than AS patients. Concomitant CA was associated with a higher prevalence of post-procedural acute kidney injury (50.0% vs. 13.1%, P = 0.018) and sudden cardiac death [SCD; P (log-rank test) = 0.017]. Following propensity score matching, 184 proteins were analysed to identify serum biomarkers of concomitant CA. CA patients expressed lower levels of chymotrypsin (P = 0.018) and carboxypeptidase 1 (P = 0.027). We propose an algorithm using commonly documented parameters-stroke volume index, ejection fraction, NT-proBNP levels, posterior wall thickness, and QRS voltage-to-mass ratio-to screen for CA in AS patients, reaching a sensitivity of 66.6% with a specificity of 98.1%.

CONCLUSIONS

The co-prevalence of AS and CA was lower than expected, at 4.9%. Despite excellent 1 year mortality, AS + CA patients died significantly more often from SCD. We propose a multimodal algorithm to facilitate more effective screening for CA containing parameters commonly documented during clinical routine. Proteomic biomarkers may yield additional information in the future.

Prevalence of Cardiovascular Disease and Risk Factors Among Former National Football League Players

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death worldwide, but prevalence estimates in former professional athletes are limited.

OBJECTIVES

HUDDLE (Heart Health: Understanding and Diagnosing Disease by Leveraging Echocardiograms) aimed to raise awareness and estimate the prevalence of CVD and associated risk factors among members of the National Football League (NFL) Alumni Association and their families through education and screening events.

METHODS

HUDDLE was a multicity, cross-sectional study of NFL alumni and family members aged 50 years and older. Subjects reported their health history and participated in CVD education and screening (blood pressure, electrocardiogram, and transthoracic echocardiogram [TTE] assessments). Phone follow-up by investigators occurred 30 days postscreening to review results and recommendations. This analysis focuses on former NFL athletes.

RESULTS

Of 498 participants screened, 57.2% (N = 285) were former NFL players, the majority of whom were African American (67.6%). The prevalence of hypertension among NFL alumni was estimated to be 89.8%, though only 37.5% reported a history of hypertension. Of 285 evaluable participants, 61.8% had structural cardiac abnormalities by TTE. Multivariable analysis showed that hypertension was a significant predictor of clinically relevant structural abnormalities on TTE.

CONCLUSIONS

HUDDLE identified a large discrepancy between participant self-awareness and actual prevalence of CVD and risk factors, highlighting a significant opportunity for population health interventions. Structural cardiac abnormalities were observed in most participants and were independently predicted by hypertension, affirming the role of TTE for CVD screening in this population aged older than 50 years. (Heart Health: Understanding and Diagnosing Disease by Leveraging Echocardiograms [HUDDLE]; NCT05009589).

Poor evidence for poor R wave progression in coronary disease: A scoping review

BACKGROUND

Poor R wave progression (PRWP) and reversed R wave progression (RRWP) have long been noted in electrocardiograms as potential indicators of anterior wall fibrosis or chronic coronary artery disease; however, the quantity and quality of evidence supporting these associations warrants closer examination.

OBJECTIVE

The aim of this scoping review is to assess the breadth of evidence regarding the diagnostic significance of PRWP and RRWP, explore the extent of research, study populations and methodologies, and the presence of gaps in knowledge regarding these electrocardiographic phenomena and their association with coronary diseases.

DESIGN

We conducted a comprehensive search across PubMed, Web of Science, and Scopus, covering literature on PRWP or RRWP in the context of myocardial infarction, ischemia, or fibrosis from any time period and in any language.

RESULTS

A total of 20 studies were included in this review, highlighting the severe paucity of data. No high-quality accuracy studies have been identified, and existing research suffers from methodological issues, in particular selection bias. Prevalence and prognostic studies showed significant heterogeneity in terms of definitions and outcomes, which contributes to an alarming risk of bias.

CONCLUSIONS

The lack of solid evidence for PRWP and RRWP as diagnostic markers for acute and chronic coronary artery disease necessitates caution in clinical interpretation. Future research should focus on well-designed case-control studies to clarify the diagnostic accuracy of these markers. Until robust evidence is available, the reliance on PRWP/RRWP for diagnosing anterior infarction should be discouraged, reflecting a gap between clinical practice and evidence-based medicine.

Electrocardiographic approach strategies in patients with Parkinson disease treated with deep brain stimulation

Deep brain stimulation (DBS) is an interdisciplinary and reversible therapy that uses high-frequency electrical stimulation to correct aberrant neural pathways in motor and cognitive neurological disorders. However, the high frequency of the waves used in DBS can interfere with electrical recording devices (e.g., electrocardiogram, electroencephalogram, cardiac monitor), creating artifacts that hinder their interpretation. The compatibility of DBS with these devices varies and depends on factors such as the underlying disease and the configuration of the neurostimulator. In emergencies where obtaining an electrocardiogram is crucial, the need for more consensus on reducing electrical artifacts in patients with DBS becomes a significant challenge. Various strategies have been proposed to attenuate the artifact generated by DBS, such as changing the DBS configuration from monopolar to bipolar, temporarily deactivating DBS during electrocardiographic recording, applying frequency filters both lower and higher than those used by DBS, and using non-standard leads. However, the inexperience of medical personnel, variability in DBS models, or the lack of a controller at the time of approach limit the application of these strategies. Current evidence on their reproducibility and efficacy is limited. Due to the growing elderly population and the rising utilization of DBS, it is imperative to create electrocardiographic methods that are easily accessible and reproducible for general physicians and emergency services.

ECGMiner: A flexible software for accurately digitizing ECG

BACKGROUND AND OBJECTIVE

The electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but store the numerical data in a proprietary format that is not interpretable and is not therefore useful for automatic diagnosis. There have been many efforts to digitize paper-based ECGs. The main limitations of previous works in ECG digitization are that they require manual selection of the regions of interest, only partly provide signal digitization, and offer limited accuracy.

METHODS

We have developed the ECGMiner, an open-source software to digitize ECG images. It is precise, fast, and simple to use. This software digitizes ECGs in four steps: 1) recognizing the image composition; 2) removing the gridline; 3) extracting the signals; 4) post-processing and storing the data.

RESULTS

We have evaluated the ECGMiner digitization capabilities using the Pearson Correlation Coefficient (PCC) and the Root Mean Square Error (RMSE) measures, and we consider ECG from two large, public, and widely used databases, LUDB and PTB-XL. The actual and digitized values of signals in both databases have been compared. The software's ability to correctly identify the location of characteristic waves has also been validated. Specifically, the PCC values are between 0.971 and 0.995, and the RMSE values are between 0.011 and 0.031 mV.

CONCLUSIONS

The ECGMiner software presented in this paper is open access, easy to install, easy to use, and capable of precisely recovering the paper-based/digital ECG signal data, regardless of the input format and signal complexity. ECGMiner outperforms existing digitization algorithms in terms of PCC and RMSE values.

A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals

Goal: Clinical interpretation of an electrocardiogram (ECG) can be detrimentally affected by noise. Removal of the electromyographic (EMG) noise is particularly challenging due to its spectral overlap with the QRS complex. The existing EMG-denoising algorithms often distort signal morphology, thus obscuring diagnostically relevant information. Methods: Here, a new iterative regeneration method (IRM) for efficient EMG-noise suppression is proposed. The main hypothesis is that the temporary removal of the dominant ECG components enables extraction of the noise with the minimum alteration to the signal. The method is validated on SimEMG database of simultaneously recorded reference and noisy signals, MIT-BIH arrhythmia database and synthesized ECG signals, both with the noise from MIT Noise Stress Test Database. Results: IRM denoising and morphology-preserving performance is superior to the wavelet- and FIR-based benchmark methods. Conclusions: IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices.

The Danish Nationwide Electrocardiogram (ECG) Cohort

The electrocardiogram (ECG) is a non-invasive diagnostic tool holding significant clinical importance in the diagnosis and risk stratification of cardiac disease. However, access to large-scale, population-based digital ECG data for research purposes remains limited and challenging. Consequently, we established the Danish Nationwide ECG Cohort to provide data from standard 12-lead digital ECGs in both pre- and in-hospital settings, which can be linked to comprehensive Danish nationwide administrative registers on health and social data with long-term follow-up. The Danish Nationwide ECG Cohort is an open real-world cohort including all patients with at least one digital pre- or in-hospital ECG in Denmark from January 01, 2000, to December 31, 2021. The cohort includes data on standardized and uniform ECG diagnostic statements and ECG measurements including global parameters as well as lead-specific measures of waveform amplitudes, durations, and intervals. Currently, the cohort comprises 2,485,987 unique patients with a median age at the first ECG of 57 years (25th-75th percentiles, 40-71 years; males, 48%), resulting in a total of 11,952,430 ECGs. In conclusion, the Danish Nationwide ECG Cohort represents a novel and extensive population-based digital ECG dataset for cardiovascular research, encompassing both pre- and in-hospital settings. The cohort contains ECG diagnostic statements and ECG measurements that can be linked to various nationwide health and social registers without loss to follow-up.

Comprehensive analysis of right fascicular and right bundle branch blocks: A multi-center study

Electrocardiographic patterns of right bundle branch and fascicular blocks were comprehensively analyzed in a two-phase study. The research aimed to address the scarcity of literature and the absence of standardized diagnostic criteria for these conditions. It revealed a weak correlation between the cardiac axis and age and highlighted the high misdiagnosis rate of these blocks. Furthermore, it discussed the challenges in fulfilling existing diagnostic criteria. The study emphasizes the need for a more precise understanding of right ventricular conduction disorders and the importance of developing robust diagnostic criteria.

Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis

BACKGROUND

cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people.

METHODS

the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis.

RESULTS

the Accuracy discrimination results ranged between 73% and 100%, the Recall between 68% and 100%, and the AUC between 0.42 and 1.

CONCLUSIONS

the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.

Representative QRS loop of the VCG record evaluation

Introduction: This study proposes an algorithm for preprocessing VCG records to obtain a representative QRS loop. Methods: The proposed algorithm uses the following methods: Digital filtering to remove noise from the signal, wavelet-based detection of ECG fiducial points and isoelectric PQ intervals, spatial alignment of QRS loops, QRS time synchronization using root mean square error minimization and ectopic QRS elimination. The representative QRS loop is calculated as the average of all QRS loops in the VCG record. The algorithm is evaluated on 161 VCG records from a database of 58 healthy control subjects, 69 patients with myocardial infarction, and 34 patients with bundle branch block. The morphologic intra-individual beat-to-beat variability rate is calculated for each VCG record. Results and Discussion: The maximum relative deviation is 12.2% for healthy control subjects, 19.3% for patients with myocardial infarction, and 17.2% for patients with bundle branch block. The performance of the algorithm is assessed by measuring the morphologic variability before and after QRS time synchronization and ectopic QRS elimination. The variability is reduced by a factor of 0.36 for healthy control subjects, 0.38 for patients with myocardial infarction, and 0.41 for patients with bundle branch block. The proposed algorithm can be used to generate a representative QRS loop for each VCG record. This representative QRS loop can be used to visualize, compare, and further process VCG records for automatic VCG record classification.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Prevalence of Brugada electrocardiographic pattern in adult population - A community-based study from Kerala, South India

There is no data for Brugada electrocardiographic pattern (BrEP) from India. In a cross-sectional study of men and women between the ages 20-79 years, electrocardiograms were analyzed following the 2002 consensus. The overall prevalence of BrEP was 1.06 % (95 % CI 0.76, 1.35). There were two cases type I (0.04 %; 95 % CI 0.01, 0.06) and forty-seven type II/III (1.01 %; 95 % CI 1.02, 1.35); the pattern was markedly higher in men. In this study, BrEP was slightly less prevalent compared to South Asia but more than in the west.

Post-COVID-19 cardio-pulmonary manifestations after 1-year of SARS-CoV-2 infection among Indian population: A single centre, case-control study (OneCoV2 study)

BACKGROUND

The evolving challenge of persistent symptoms post-Coronavirus disease-2019 (COVID-19), particularly debilitating cardio-pulmonary manifestations, necessitates further exploration. Our study aimed to assess the cardio-pulmonary complications in patients a year after hospital discharge from severe COVID-19, contrasting these with findings from a non-COVID group.

METHODS

The OneCoV2 study, a prospective, case-control study, was conducted at a tertiary care teaching hospital in northern India. We enrolled 43 subjects, with a mean age of 25.57 ± 7.94 years (COVID group) and 27.30 ± 8.17 years (non-COVID group). Comprehensive tests included pulmonary function tests, cardiac function tests, 6-min walk tests, and laboratory investigations.

RESULTS

Significant differences were found in the pulmonary function [forced vital capacity (FVC) (p = 0.037), forced expiratory flow (FEF) 25-75 % (p = 0.013)], and cardiac function [left ventricular ejection fraction (LVEF) (p = 0.032), heart rate (HR) (p = 0.047)], along with the six-minute walk test results between the two groups. In the COVID group, Pearson's correlation showed a negative correlation between FVC and C-reactive protein (CRP) [r = -0.488, p = 0.007] and a positive correlation between the six-minute walk test [r = 0.431, p = 0.003] and HR [r = 0.503, p = 0.013].

CONCLUSIONS

Our data suggest that pulmonary abnormalities are prevalent in COVID patients even after 1-year of hospital discharge. Cardiac biomarkers also show an inclination towards the COVID group. While we found significant correlations involving some parameters like FVC, CRP, HR, and results from the six-minute walk test, we did not find any significant correlations with the other tested parameters in our study.

Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study

BACKGROUND

The accuracy of electrocardiogram (ECG) interpretation by doctors are affected by the available clinical information. However, having a complete set of clinical details before making a diagnosis is very difficult in the clinical setting especially in the early stages of the admission process. Therefore, we developed an artificial intelligence-assisted ECG diagnostic system (AI-ECG) using natural language processing to provide screened key clinical information during ECG interpretation.

METHODS

Doctors with varying levels of training were asked to make diagnoses from 50 ECGs using a common ECG diagnosis system that does not contain clinical information. After a two-week-blanking period, the same set of ECGs was reinterpreted by the same doctors with AI-ECG containing clinical information. Two cardiologists independently provided diagnostic criteria for 50 ECGs, and discrepancies were resolved by consensus or, if necessary, by a third cardiologist. The accuracy of ECG interpretation was assessed, with each response scored as correct/partially correct = 1 or incorrect = 0.

RESULTS

The mean accuracy of ECG interpretation was 30.2% and 36.2% with the common ECG system and AI-ECG system, respectively. Compared to the unaided ECG system, the accuracy of interpretation was significantly improved with the AI-ECG system (P for paired t-test = 0.002). For senior doctors, no improvement was found in ECG interpretation accuracy, while an AI-ECG system was associated with 27% higher mean scores (24.3 ± 9.4% vs. 30.9 ± 10.6%, P = 0.005) for junior doctors.

CONCLUSION

Intelligently screened key clinical information could improve the accuracy of ECG interpretation by doctors, especially for junior doctors.

Incidence and determinants of atrial fibrillation in patients with wild-type transthyretin cardiac amyloidosis

BACKGROUND

Data on the incidence and factors associated with de novo atrial fibrillation (AF) in patients with wild-type transthyretin cardiac amyloidosis (ATTRwt-CA) is limited. We described the incidence and factors associated with de novo AF in patients diagnosed with ATTRwt-CA to drive tailored arrhythmia screening.

METHODS

Multicenter, retrospective, observational cohort study performed in six referral centers for CA. All consecutive patients diagnosed with ATTRwt-CA between 2004 and 2020 with >6-month follow up (FU) were enrolled and divided into three groups according to presence of AF: (1)patients with 'known AF'; (2)patients in 'sinus rhythm' and (3)patients developing 'de novo AF' during FU. Incidence and factors associated with AF in patients with ATTRwt were the primary outcomes.

RESULTS

Overall, 266 patients were followed for a median of 19 [11-33] months: 148 (56%) with known AF, 84 (31.6%) with sinus rhythm, and 34 (12.8%) with de novo AF. At Fine-Gray competing risk analysis to account for mortality, PR (sub-distribution hazard ratio [SHR] per Δms: 1.008, 95% C.I. 1.001-1.013, p = 0.008), QRS (SHR per Δms: 1.012, 95% C.I. 1.001-1.022, p = 0.046) and left atrial diameter ≥ 50 mm (SHR: 2.815,95% C.I. 1.483-5.342, p = 0.002) were associated with de novo AF. Patients with at least two risk factors (PR ≥ 200 ms, QRS ≥ 120 ms or LAD≥50 mm) had a higher risk of developing de novo AF compared to patients with no risk factors (HR 14.918 95% C.I. 3.242-31.646, p = 0.008).

CONCLUSIONS

At the end of the study almost 70% patients had AF. Longer PR and QRS duration and left atrial dilation are associated with arrhythmia onset.

BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

BACKGROUND AND OBJECTIVES

Electrocardiographic (ECG) and vectorcardiographic (VCG) analyses are used to diagnose current cardiovascular disease and for risk stratification for future adverse cardiovascular events. With increasing use of digital ECGs, research into novel ECG/VCG parameters has increased, but widespread computer-based ECG/VCG analysis is limited because there are no currently available, open-source, and easily customizable software packages designed for automated and reproducible analysis.

METHODS AND RESULTS

We present BRAVEHEART, an open-source, modular, customizable, and easy to use software package implemented in the MATLAB programming language, for scientific analysis of standard 12-lead ECGs acquired in a digital format. BRAVEHEART accepts a wide variety of digital ECG formats and provides complete and automatic ECG/VCG processing with signal denoising to remove high- and low-frequency artifact, non-dominant beat identification and removal, accurate fiducial point annotation, VCG construction, median beat construction, customizable measurements on median beats, and output of measurements and results in numeric and graphical formats.

CONCLUSIONS

The BRAVEHEART software package provides easily customizable scientific analysis of ECGs and VCGs. We hope that making BRAVEHART available will allow other researchers to further the field of ECG/VCG analysis without having to spend significant time and resources developing their own ECG/VCG analysis software and will improve the reproducibility of future studies. Source code, compiled executables, and a detailed user guide can be found at http://github.com/BIVectors/BRAVEHEART. The source code is distributed under the GNU General Public License version 3.

Multiscale Cumulative Residual Dispersion Entropy with Applications to Cardiovascular Signals

Heart rate variability (HRV) is used as an index reflecting the adaptability of the autonomic nervous system to external stimuli and can be used to detect various heart diseases. Since HRVs are the time series signal with nonlinear property, entropy has been an attractive analysis method. Among the various entropy methods, dispersion entropy (DE) has been preferred due to its ability to quantify the time series' underlying complexity with low computational cost. However, the order between patterns is not considered in the probability distribution of dispersion patterns for computing the DE value. Here, a multiscale cumulative residual dispersion entropy (MCRDE), which employs a cumulative residual entropy and DE estimation in multiple temporal scales, is presented. Thus, a generalized and fast estimation of complexity in temporal structures is inherited in the proposed MCRDE. To verify the performance of the proposed MCRDE, the complexity of inter-beat interval obtained from ECG signals of congestive heart failure (CHF), atrial fibrillation (AF), and the healthy group was compared. The experimental results show that MCRDE is more capable of quantifying physiological conditions than preceding multiscale entropy methods in that MCRDE achieves more statistically significant cases in terms of p-value from the Mann-Whitney test.

Right fascicular blocks: A case series and a comprehensive electrocardiographic analysis

The main trunk of the right bundle branch divides into an anterior, middle (lateral) and posterior fascicle. This article describes the right anterior and posterior fascicular block. They present a diagnostic challenge and are often overlooked during diagnostic processes. The studied patients were young adults whose electrocardiographic tracings were registered at the Mexican Faculty of Medicine of La Salle University. The presence of delayed R-peak time in aVR and V1, along with the described ventricular complex morphologies, and a right or left deviation of the cardiac axis exceeding +60°, may be suggestive of right fascicular blocks.

Right and left ventricular mass development in early infancy: Correlation of electrocardiographic changes with echocardiographic measurements

BACKGROUND

Right ventricular mass indexed to body surface area (RVMI) decreases and left ventricular mass index (LVMI) increases rapidly and substantially during early infancy. The relationship between these sizeable mass transformations and simultaneous electrocardiographic changes have not been previously delineated.

METHODS

Normal term infants (#45 initially enrolled) were prospectively evaluated at 2 days and at 2-week, 2-month, and 4-month clinic visits. Ventricular masses were estimated with 2D echocardiographic methods. QRS voltages were measured in leads V1, V6, I and aVF.

RESULTS

Mean QRS axis shifted from 135 (95%CI 124, 146) to 65 degrees (95%CI 49, 81) and correlated with both RVMI decrease and LVMI increase (R = 0.46⁎ vs. 0.25†, respectively. *p < 0.01, †p < 0.05). As RVMI decreased from mean 28.1 (95%CI 27.1, 29.1) to 23.3 g/m2 (95%CI 21.4, 25.2) so did V1R and V6S voltages. RVMI changes correlated with V1R, V6S, and V1R + V6S voltages (R = 0.29*, 0.23† and 0.35*, respectively. *p < 0.01, †p < 0.05) but not with V1R/S ratio. As LVMI increased from 44.6 (95%CI 42.9, 46.3) to 55.4 g/m2 (95%CI 52.3, 58.5) V6R and V6Q increased but V1S voltage did not. LVMI changes correlated with V6R, V6R-S, and V6(Q + R)-S voltages (R = 0.31*, 0.34*, and 0.38* respectively. *p < 0.01) but not with V1S or V6R/S (R = 0.01 and 0.18 respectively, p = NS).

CONCLUSIONS

During early infancy the RVMI decrease correlates best with the QRS axis shift and V1R + V6S voltage, and the LVMI increase correlates best with V6R-S and V6(Q + R)-S voltages.

Cardiac Autonomic Dysfunction and Risk of Silent Myocardial Infarction Among Adults With Type 2 Diabetes

Background There is a paucity of large-scale epidemiological studies on the link between cardiac autonomic neuropathy (CAN) and the risk of silent myocardial infarction (SMI) in type 2 diabetes. We evaluated the association between CAN and the risk of SMI in a large sample of adults with type 2 diabetes. Methods and Results Participants with type 2 diabetes from the ACCORD (Action to Control Cardiovascular Risk in Diabetes) study without atherosclerotic cardiovascular disease at baseline were included. CAN was ascertained using heart rate variability indices calculated from 10-s resting electrocardiograms. The heart rate variability indices included standard deviation of all normal-to-normal R-R intervals and root mean square of successive differences between normal-to-normal R-R intervals. CAN was defined as both the standard deviation of all normal-to-normal R-R intervals and root mean square of successive differences between normal-to-normal R-R intervals less than the fifth percentile of the general population. We used Cox proportional hazards regression to generate hazard ratios (HRs) for incident SMI in relation to CAN measures. Among 4842 participants (mean age, 62.5 years; 46.6% women; 60.2% White), there were 73 incident SMI cases over a median follow-up of 4.9 years (incidence rate 3.1 out of 1000 person-years [95% CI, 2.5-3.9]). After adjusting for confounders, low heart rate variability was associated with a higher risk of SMI (HR, 1.67 [95% CI, 1.02-2.72] and HR, 1.56 [95% CI, 0.94-2.58] for low standard deviation of all normal-to-normal R-R intervals and root mean square of successive differences between normal-to-normal R-R intervals, respectively). Participants with CAN had a 1.9-fold greater risk of SMI (HR, 1.91 [95% CI, 1.14-3.20]). Conclusions In a large cohort of adults with type 2 diabetes, CAN was significantly associated with an increased risk of incident SMI.

Low-Power Long-Term Ambulatory Electrocardiography Monitor of Three Leads with Beat-to-Beat Heart Rate Measurement in Real Time

A low-power long-term ambulatory ECG monitor was developed for the acquisition, storage and processing of three simultaneous leads DI, aVF and V2 with a beat-to-beat heart rate measurement in real time. It provides long-term continuous ECG recordings until 84 h. The monitor uses a QRS complex detection algorithm based on the continuous wavelet transform with splines, which automatically selects the scale for the analysis of ECG records with different sampling frequencies. It includes a lead-off detection to continuously monitor the electrode connections and a real-time system of visual and acoustic alarms to alert users of abnormal conditions in its operation. The monitor presented is based in an ADS1294 analogue front end with four channels, 24-bit analog-to-digital converters and programmable gain amplifiers, a low-power dual-core ESP32 microcontroller, a microSD memory for data storage in a range of 4 GB to 32 GB and a 1.4 in thin-film transistor liquid crystal display (LCD) variant with a resolution of 128 × 128 pixels. It has programmable sampling rates of 250, 500 and 1000 Hz; a bandwidth of 0 Hz to 50% of the selected sampling rate; a CMRR of -105 dB; an input margin of ±2.4 V; a resolution of 286 nV; and a current consumption of 50 mA for an average battery life of 84 h. The ambulatory ECG monitor was evaluated with the commercial data-acquisition system BIOPAC MP36 and its module for ECG LABEL SS2LB, simultaneously comparing the morphologies of two ECG records and obtaining a correlation of 91.78%. For the QRS detection in real time, the implemented algorithm had an error less than 5%. The developed ambulatory ECG monitor can be used for the analysis of the dynamics of the heart rate variability in long-term ECG records and for the development of one's own databases of ECG recordings of normal subjects and patients with cardiovascular and noncardiovascular diseases.

Residential greenness and cardiac conduction abnormalities: epidemiological evidence and an explainable machine learning modeling study

BACKGROUND

Previous studies indicated the beneficial influence of residential greenness on cardiovascular disease (CVD), however, the association of residential greenness with cardiac conduction performance remains unclear. This study aims to examine the epidemiological associations between residential greenness and cardiac conduction abnormalities in rural residents, simultaneously exploring the role of residential greenness for cardiac health in an explainable machine learning modeling study.

METHODS

A total of 27,294 participants were derived from the Henan Rural Cohort. Two satellite-based indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. Independent and combined associations of residential greenness indices and physical activities with electrocardiogram (ECG) parameter abnormalities were evaluated using the logistic regression model and generalized linear model. The Gradient Boosting Machine (GBM) and the SHapely Additive exPlanations (SHAP) were employed in the modeling study.

RESULTS

The odds ratios (OR) and 95% confidence interval (CI) for QRS interval, heart rate (HR), QTc interval, and PR interval abnormalities with per interquartile range in NDVI were 0.896 (0.873-0.920), 0.955 (0.926-0.986), 1.015 (0.984-1.047), and 0.986 (0.929-1.045), respectively. Furthermore, the participants with higher physical activities plus residential greenness (assessed by EVI) were related to a 1.049-fold (1.017-1.081) and 1.298-fold (1.245-1.354) decreased risk for abnormal QRS interval and HR. Similar results were also observed in the sensitivity analysis. The NDVI ranked fifth (SHAP mean value 0.116) in the analysis for QRS interval abnormality risk in the modeling study.

CONCLUSION

A higher level of residential greenness was significantly associated with cardiac conduction abnormalities. This effect might be strengthened in residents with more physical activities. This study indicated the cruciality of environmental greenness to cardiac functions and also contributed to refining preventive medicine and greenness design strategies.

Electrocardiogram-based biometrics for user identification - Using your heartbeat as a digital key

External biometrics such as thumbprint and facial recognition have become standard tools for securing our digital devices and protecting our data. These systems, however, are potentially prone to copying and cybercrime access. Researchers have therefore explored internal biometrics, such as the electrical patterns within an electrocardiogram (ECG). The heart's electrical signals carry sufficient distinctiveness to allow the ECG to be used as an internal biometric for user authentication and identification. Using the ECG in this way has many potential advantages and limitations. This article reviews the history of ECG biometrics and explores some of the technical and security considerations. It also explores current and future uses of the ECG as an internal biometric.

Compressed sensing framework for BCG signals based on the optical fiber sensor

A compressed sensing (CS) framework is built for ballistocardiography (BCG) signals, which contains two parts of an optical fiber sensor-based heart monitoring system with a CS module and an end-to-end deep learning-based reconstruction algorithm. The heart monitoring system collects BCG data, and then compresses and transmits the data through the CS module at the sensing end. The deep learning-based algorithm reconstructs compressed data at the received end. To evaluate results, three traditional CS reconstruction algorithms and a deep learning method are adopted as references to reconstruct the compressed BCG data with different compression ratios (CRs). Results show that our framework can reconstruct signals successfully when the CR grows from 50% to 95% and outperforms other methods at high CRs. The mean absolute error (MAE) of the estimated heartbeat rate (HR) is lower than 1 bpm when the CR is below 95%. The proposed CS framework for BCG signals can be integrated into the IoMT system, which has great potential in health care for both medical and home use.

Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity

Introduction

Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting cardiac abnormalities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early improves the quality and efficiency of medical care.

Methods

The paper presents various modern approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincaré representation of ECG signal and deep-learning-based image classifiers. Additionally, the raw signals were processed with the one-dimensional convolutional model while the XGBoost model was facilitated to predict based on the time-series features.

Results

The Poincaré-based methods showed decent performance in predicting AF (atrial fibrillation) but not other types of arrhythmia. XGBoost model gave an acceptable performance in long-term data but had a long inference time due to highly-consuming calculations within the pre-processing phase. Finally, the 1D convolutional model, specifically the 1D ResNet, showed the best results in both studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and 71%, respectively, and they were superior to the first-ranking solution of each challenge. The 1D models also presented high specificity. Additionally, our paper investigated efficiency metrics including power consumption and equivalent CO2 emissions, with one-dimensional models like 1D CNN and 1D ResNet being the most energy efficient. Model interpretation analysis showed that the DenseNet detected AF using heart rate variability while the 1D ResNet assessed the AF patterns in raw ECG signals.

Discussion

Despite the under-performed results, the Poincaré diagrams are still worth studying further because of the accessibility and inexpensive procedure. In the 1D convolutional models, the residual connections are useful to keep the model simple but not decrease the performance. Our approach in power measurement and model interpretation helped understand the numerical complexity and mechanism behind the model decision.

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review

BACKGROUND

Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments.

OBJECTIVE

This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence.

METHODS

We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases.

RESULTS

From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation.

CONCLUSIONS

ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.

Age and gender differences of basic electrocardiographic values and abnormalities in the general adult population; Tehran Cohort Study

BACKGROUND

Although several studies are available regarding baseline Electrocardiographic (ECG) parameters and major and minor ECG abnormalities, there is considerable controversy regarding their age and gender differences in the literature.

METHODS

Data from 7630 adults aged ≥ 35 from the Tehran Cohort Study registered between March 2016 and March 2019 were collected. Basic ECG parameters values and abnormalities related to arrhythmia, defined according to the American Heart Association definitions, were analyzed and compared between genders and four distinct age groups. The odds ratio of having any major ECG abnormality between men and women, stratified by age, was calculated.

RESULTS

The average age was 53.6 (± 12.66), and women made up 54.2% (n = 4132) of subjects. The average heart rate (HR) was higher among women(p < 0.0001), while the average values of QRS duration, P wave duration, and RR intervals were higher among men(p < 0.0001). Major ECG abnormalities were observed in 2.9% of the study population (right bundle branch block, left bundle branch block, and Atrial Fibrillation were the most common) and were more prevalent among men compared to women but without statistical significance (3.1% vs. 2.7% p = 0.188). Moreover, minor abnormalities were observed in 25.9% of the study population and again were more prevalent among men (36.4% vs. 17% p < 0.001). The prevalence of major ECG abnormalities was significantly higher in participants older than 65.

CONCLUSION

Major and minor ECG abnormalities were roughly more prevalent in male subjects. In both genders, the odds of having major ECG abnormalities surge with an increase in age.

Evaluating heart rate variability with 10 second multichannel electrocardiograms in a large population-based sample

Introduction

Heart rate variability (HRV), defined as the variability of consecutive heart beats, is an important biomarker for dysregulations of the autonomic nervous system (ANS) and is associated with the development, course, and outcome of a variety of mental and physical health problems. While guidelines recommend using 5 min electrocardiograms (ECG), recent studies showed that 10 s might be sufficient for deriving vagal-mediated HRV. However, the validity and applicability of this approach for risk prediction in epidemiological studies is currently unclear to be used.

Methods

This study evaluates vagal-mediated HRV with ultra-short HRV (usHRV) based on 10 s multichannel ECG recordings of N = 4,245 and N = 2,392 participants of the Study of Health in Pomerania (SHIP) from two waves of the SHIP-TREND cohort, additionally divided into a healthy and health-impaired subgroup. Association of usHRV with HRV derived from long-term ECG recordings (polysomnography: 5 min before falling asleep [N = 1,041]; orthostatic testing: 5 min of rest before probing an orthostatic reaction [N = 1,676]) and their validity with respect to demographic variables and depressive symptoms were investigated.

Results

High correlations (r = .52-.75) were revealed between usHRV and HRV. While controlling for covariates, usHRV was the strongest predictor for HRV. Furthermore, the associations of usHRV and HRV with age, sex, obesity, and depressive symptoms were similar.

Conclusion

This study provides evidence that usHRV derived from 10 s ECG might function as a proxy of vagal-mediated HRV with similar characteristics. This allows the investigation of ANS dysregulation with ECGs that are routinely performed in epidemiological studies to identify protective and risk factors for various mental and physical health problems.

Association between Cardiovascular Response and Inflammatory Cytokines in Non-Small Cell Lung Cancer Patients

Cardiovascular disease is an essential comorbidity in patients with non-small cell lung cancer (NSCLC) and represents an independent risk factor for increased mortality. Therefore, careful monitoring of cardiovascular disease is crucial in the healthcare of NSCLC patients. Inflammatory factors have previously been associated with myocardial damage in NSCLC patients, but it remains unclear whether serum inflammatory factors can be utilized to assess the cardiovascular health status in NSCLC patients. A total of 118 NSCLC patients were enrolled in this cross-sectional study, and their baseline data were collected through a hospital electronic medical record system. Enzyme-linked immunosorbent assay (ELISA) was used to measure the serum levels of leukemia inhibitory factor (LIF), interleukin (IL)-18, IL-1β, transforming growth factor-β1 (TGF-β1), and connective tissue growth factor (CTGF). Statistical analysis was performed using the SPSS software. Multivariate and ordinal logistic regression models were constructed. The data revealed an increased serum level of LIF in the group using tyrosine kinase inhibitor (TKI)-targeted drugs compared to non-users (p < 0.001). Furthermore, serum TGF-β1 (area under the curve, AUC: 0.616) and cardiac troponin T (cTnT) (AUC: 0.720) levels were clinically evaluated and found to be correlated with pre-clinical cardiovascular injury in NSCLC patients. Notably, the serum levels of cTnT and TGF-β1 were found to indicate the extent of pre-clinical cardiovascular injury in NSCLC patients. In conclusion, the results suggest that serum LIF, as well as TGFβ1 together with cTnT, are potential serum biomarkers for the assessment of cardiovascular status in NSCLC patients. These findings offer novel insights into the assessment of cardiovascular health and underscore the importance of monitoring cardiovascular health in the management of NSCLC patients.

Serum Interleukin-34 in Psoriatic arthritis patients and its correlation with disease 1 activity, and subclinical atherosclerosis

Background

Psoriatic arthritis (PsA) is a chronic multi-domains autoimmune inflammatory disorder. Patients with PsA have a significant prevalence of cardiovascular affection. Upregulated Interleukin-34 (IL-34) has been seen in many autoimmune disorders, and also in atherosclerotic plaques. The aim of this observational case–control study was to evaluate the serum levels of il-34 in PsA patients and correlate between its level and disease activity, and subclinical cardiovascular affection.

Results

In this study, there were 70 PsA patients and 70 healthy volunteers, 43 patients were on Methotrexate, 6 on sulfasalazine, while 40 patients were on biological therapy either monotherapy or in combination with DMARDs. There were significant differences between PsA patients and controls in ESR, high sensitivity-CRP, total lipid profile, and IL-34 levels (p < 0.05) while there were no significant differences regarding Echo and ECG results. Also, we found that there was significant elevation in DAPSA score, hs-CRP, IL-34, and cIMT in the active patients when we compared them with inactive patients. IL-34 had significant positive correlations with DAPSA score, hs-CRP, and cIMT (r = 0.654, 0.579, and 0.658 respectively).

Conclusion

Serum interleukin-34 is an important marker in PsA as its levels were elevated in PsA patients and were correlated with disease activity and subclinical cardiovascular affection.

Planning, conducting, and analyzing a psychophysiological experiment on challenge and threat: A comprehensive tutorial

The biopsychosocial model of challenge and threat (BPS-CT) is a powerful framework linking psychological processes to reliable patterns of cardiovascular responses during motivated performance situations. Specifically, the BPS-CT poses challenge and threat as two motivational states that can emerge in response to a demanding, self-relevant task, where greater challenge arises when perceived resources are higher than demands, and greater threat arises when perceived resources are lower than demands. By identifying unique patterns of physiological responses associated with challenge and threat, respectively, the BPS-CT affords insight into subjective appraisals of resources and demands, and their determinants, during motivated performance situations. Despite its broad utility, lack of familiarity with physiological concepts and difficulty with identifying clear guidelines in the literature are barriers to wider uptake of this approach by behavioral researchers. Our goal is to remove these barriers by providing a comprehensive, step-by-step tutorial on conducting an experiment using the challenge and threat model, offering concrete recommendations for those who are new to the method, and serving as a centralized collection of resources for those looking to deepen their understanding. The tutorial spans five parts, covering theoretical introduction, lab setup, data collection, data analysis, and appendices offering additional details about data analysis and equipment. With this, we aim to make challenge and threat research, and the insights it offers, more accessible to researchers throughout the behavioral sciences.

Prevalence and diagnostic significance of de-novo 12-lead ECG changes after COVID-19 infection in elite soccer players

BACKGROUND AND AIM

The efficacy of pre-COVID-19 and post-COVID-19 infection 12-lead ECGs for identifying athletes with myopericarditis has never been reported. We aimed to assess the prevalence and significance of de-novo ECG changes following COVID-19 infection.

METHODS

In this multicentre observational study, between March 2020 and May 2022, we evaluated consecutive athletes with COVID-19 infection. Athletes exhibiting de-novo ECG changes underwent cardiovascular magnetic resonance (CMR) scans. One club mandated CMR scans for all players (n=30) following COVID-19 infection, despite the absence of cardiac symptoms or de-novo ECG changes.

RESULTS

511 soccer players (median age 21 years, IQR 18-26 years) were included. 17 (3%) athletes demonstrated de-novo ECG changes, which included reduction in T-wave amplitude in the inferior and lateral leads (n=5), inferior leads (n=4) and lateral leads (n=4); inferior T-wave inversion (n=7); and ST-segment depression (n=2). 15 (88%) athletes with de-novo ECG changes revealed evidence of inflammatory cardiac sequelae. All 30 athletes who underwent a mandatory CMR scan had normal findings. Athletes revealing de-novo ECG changes had a higher prevalence of cardiac symptoms (71% vs 12%, p<0.0001) and longer median symptom duration (5 days, IQR 3-10) compared with athletes without de-novo ECG changes (2 days, IQR 1-3, p<0.001). Among athletes without cardiac symptoms, the additional yield of de-novo ECG changes to detect cardiac inflammation was 20%.

CONCLUSIONS

3% of athletes demonstrated de-novo ECG changes post COVID-19 infection, of which 88% were diagnosed with cardiac inflammation. Most affected athletes exhibited cardiac symptoms; however, de-novo ECG changes contributed to a diagnosis of cardiac inflammation in 20% of athletes without cardiac symptoms.

Generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across geography, ages and sexes

To drive health innovation that meets the needs of all and democratize healthcare, there is a need to assess the generalization performance of deep learning (DL) algorithms across various distribution shifts to ensure that these algorithms are robust. This retrospective study is, to the best of our knowledge, an original attempt to develop and assess the generalization performance of a DL model for AF events detection from long term beat-to-beat intervals across geography, ages and sexes. The new recurrent DL model, denoted ArNet2, is developed on a large retrospective dataset of 2,147 patients totaling 51,386 h obtained from continuous electrocardiogram (ECG). The model's generalization is evaluated on manually annotated test sets from four centers (USA, Israel, Japan and China) totaling 402 patients. The model is further validated on a retrospective dataset of 1,825 consecutives Holter recordings from Israel. The model outperforms benchmark state-of-the-art models and generalized well across geography, ages and sexes. For the task of event detection ArNet2 performance was higher for female than male, higher for young adults (less than 61 years old) than other age groups and across geography. Finally, ArNet2 shows better performance for the test sets from the USA and China. The main finding explaining these variations is an impairment in performance in groups with a higher prevalence of atrial flutter (AFL). Our findings on the relative performance of ArNet2 across groups may have clinical implications on the choice of the preferred AF examination method to use relative to the group of interest.

A novel algorithm to assess the quality of 12-lead ECG recordings: validation in a real telecardiology application

Objective. Automatic detection of Electrocardiograms (ECG) quality is fundamental to minimize costs and risks related to delayed diagnosis due to low ECG quality. Most algorithms to assess ECG quality include non-intuitive parameters. Also, they were developed using data non-representative of a real-world scenario, in terms of pathological ECGs and overrepresentation of low-quality ECG. Therefore, we introduce an algorithm to assess 12-lead ECG quality, Noise Automatic Classification Algorithm (NACA) developed in Telehealth Network of Minas Gerais (TNMG).Approach. NACA estimates a signal-to-noise ratio (SNR) for each ECG lead, where 'signal' is an estimated heartbeat template, and 'noise' is the discrepancy between the template and the ECG heartbeat. Then, clinically-inspired rules based on SNR are used to classify the ECG as acceptable or unacceptable. NACA was compared with Quality Measurement Algorithm (QMA), the winner of Computing in Cardiology Challenge 2011 (ChallengeCinC) by using five metrics: sensitivity (Se), specificity (Sp), positive predictive value (PPV),F₂, and cost reduction resulting from adoption of the algorithm. Two datasets were used for validation: TestTNMG, consisting of 34 310 ECGs received by TNMG (1% unacceptable and 50% pathological); ChallengeCinC, consisting of 1000 ECGs (23% unacceptable, higher than real-world scenario).Main results. Both algorithms reached a similar performance on ChallengeCinC, although NACA performed considerably better than QMA in TestTNMG (Se = 0.89 versus 0.21; Sp = 0.99 versus 0.98; PPV = 0.59 versus 0.08;F₂= 0.76 versus 0.16 and cost reduction 2.3 ± 1.8% versus 0.3 ± 0.3%, respectively).Significance. Implementing of NACA in a telecardiology service results in evident health and financial benefits for the patients and the healthcare system.