Recommendations for the Standardization and Interpretation of the Electrocardiogram

Soothing touch matters: Patterns of everyday mother-infant physical contact and their real-time physiological implications

Physical contact between infants and caregivers is crucial for attachment development. Previous research shows that skin-to-skin contact after birth and frequent baby wearing in the first year predict secure attachment at 12-months. This relationship is thought to be mediated by the activation of infants' parasympathetic nervous system through caregiver touch. However, little is known about everyday touch behaviors and their impact on infants' real-time parasympathetic activity. Laboratory observations may not accurately represent real-world interactions, highlighting the need for ecologically valid studies. To address this, we examined everyday dyadic touch behaviors and their real-time effects on infant parasympathetic activation. We video recorded N = 28 infants (1-10 months old) and their mothers at home for behavioral analyses. All infants wore wireless ECG sensors (1024 Hz) during video recordings, and n = 21 infants had high-quality ECG data that could be used for Respiratory Sinus Arrhythmia analyses. We used a dynamic measure of RSA (updated every 200 ms) as an index for real-time parasympathetic activation. We found that dyads touch interactions at home involve short, though highly variable bouts of physical contact, that change with infant age. Younger infants spent more time remaining stationary during contact and receiving more soothing touch compared to older infants. Only soothing touch - i.e., rocking, patting, bouncing, or stroking- led to immediate, significant increases in parasympathetic activity (RSA), and this effect was driven by younger infants. This study provides new insights into the ecological patterns of touch in early development and the biobehavioral mechanisms promoting secure attachment.

Wearable Electrocardiogram Technology: Help or Hindrance to the Modern Doctor?

Unlabelled

Electrocardiography is an essential tool in the arsenal of medical professionals, Traditionally, patients have been required to meet health care practitioners in person to have an electrocardiogram (ECG) recorded and interpreted. This may result in paroxysmal arrhythmias being missed, as well as decreased patient convenience, and thus reduced uptake. The advent of wearable ECG devices built into consumer smartwatches has allowed unparalleled access to ECG monitoring for patients. Not only are these modern devices more portable than traditional Holter monitors, but with the addition of artificial intelligence (AI)-led rhythm interpretation, diagnostic accuracy is improved greatly when compared with conventional ECG-machine interpretation. The improved wearability may also translate into increased rates of detected arrhythmias. Despite the many positives, wearable ECG technology brings with it its own challenges. Diagnostic accuracy, managing patient expectations and limitations, and incorporating home ECG monitoring into clinical guidelines have all arisen as challenges for the modern clinician. Decentralized monitoring and patient alerts to supposed arrhythmias have the potential to increase patient anxiety and health care visitations (and therefore costs). To better obtain meaningful data from these devices, provide optimal patient care, and provide meaningful explanations to patients, providers need to understand the basic sciences underpinning these devices, how these relate to the surface ECG, and the implications in diagnostic accuracy. This review article examines the underlying physiological principles of electrocardiography, as well as examines how wearable ECGs have changed the clinical landscape today, where their limitations lie, and what clinicians can expect in the future with their increasing use.

Functional feature extraction and validation from twelve-lead electrocardiograms to identify atrial fibrillation

BACKGROUND

Deep learning methods on standard, 12-lead electrocardiograms (ECG) have resulted in the ability to identify individuals at high-risk for the development of atrial fibrillation. However, the process remains a "black box" and does not help clinicians in understanding the electrocardiographic changes at an individual level. we propose a nonparametric feature extraction approach to identify features that are associated with the development of atrial fibrillation (AF).

METHODS

We apply functional principal component analysis to the raw ECG tracings collected in the Chronic Renal Insufficiency Cohort (CRIC) study. We define and select the features using ECGs from participants enrolled in Phase I (2003-2008) of the study. Cox proportional hazards models are used to evaluate the association of selected ECG features and their changes with the incident risk of AF during study follow-up. The findings are then validated in ECGs from participants enrolled in Phase III (2013-2015).

RESULTS

We identify four features that are related to the P-wave amplitude, QRS complex and ST segment. Both their initial measurement and 3-year changes are associated with the development of AF. In particular, one standard deviation in the 3-year decline of the P-wave amplitude is independently associated with a 29% increased risk of incident AF in the multivariable model (HR: 1.29, 95% CI: [1.16, 1.43]).

CONCLUSIONS

Compared with deep learning methods, our features are intuitive and can provide insights into the longitudinal ECG changes at an individual level that precede the development of AF.

Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution

Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional Autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed; this is followed by in-depth analysis of the enhanced signals. The autoencoder receives a signal window (of 5 s) sampled at 50 Hz (low resolution) as input and reconstructs a denoised super-resolution signal at 500 Hz. The proposed autoencoder is applied to publicly available datasets, demonstrating optimal performance in reconstructing high-resolution signals from very low-resolution inputs sampled at 50 Hz. The results were then compared with current state-of-the-art for electrocardiogram super-resolution, demonstrating the effectiveness of the proposed method. The method achieves a signal-to-noise ratio of 12.20 dB, a mean squared error of 0.0044, and a root mean squared error of 4.86%, which significantly outperforms current state-of-the-art alternatives. This framework can effectively enhance hidden information within signals, aiding in the detection of heart-related diseases.

Factors associated with heart failure events in patients with new-onset persistent left bundle branch block at discharge after transcatheter aortic valve replacement

BACKGROUND

New-onset persistent left bundle branch block (NOP-LBBB) at discharge after transcatheter aortic valve replacement (TAVR) is frequent, but its association with death and hospitalization for heart failure (HHF) remains unclear.

OBJECTIVE

We aimed to assess the association of LBBB persistence or resolution after discharge and of permanent pacemaker (PPM) implantation before discharge with these outcomes.

METHODS

We analyzed consecutive patients undergoing TAVR at Nancy University Hospital from 2009 to 2021 with NOP-LBBB at discharge and available 1-year follow-up. We assessed the association of LBBB persistence (LBBB+) or resolution (LBBB-) at 3 months and in-hospital PPM implantation (PPM+) or absence (PPM-) with the 1-year risk of the composite outcome of mortality or HHF.

RESULTS

Of 1646 TAVR patients, 287 (17.4%) had NOP-LBBB, with complete follow-up data available for 234 patients. Of them, 73 patients (31.2%) required in-hospital PPM implantation, 142 patients (60.7%) experienced LBBB persistence at 3-month follow-up, and 45 (19.2%) had both. The 1-year mortality or HHF rate was 6.3% (PPM-/LBBB-), 10.7% (PPM+/LBBB-), 20.6% (PPM-/LBBB+), and 22.2% (PPM+/LBBB+). LBBB persistence was significantly associated with the composite outcome irrespective of PPM implantation (adjusted hazard ratio [aHR] compared with PPM-/LBBB-: aHR for PPM-/LBBB+, 4.91 [1.64-14.64; P = .004]; aHR for PPM+/LBBB+, 4.58 [1.43-14.68; P = .010]), whereas PPM implantation with LBBB- was not (P = .29). This association was mainly driven by HHF (aHR for PPM-/LBBB+, 8.36 [1.90-36.83; P = .005]; aHR for PPM+/LBBB+, 8.36 [1.80-38.89; P = .007]).

CONCLUSION

The persistence of LBBB beyond discharge, rather than in-hospital PPM implantation, was associated with a higher risk of 1-year mortality or HHF in patients with NOP-LBBB at discharge after TAVR. Assessing postdischarge LBBB persistence may improve prognostic accuracy.

The UNICA sleep HRV analysis tool: an integrated open-source tool for heart rate variability analysis during sleep

Heart rate variability (HRV) analysis during sleep plays a key role for understanding autonomic nervous system function and assessing cardiovascular health. The UNICA Sleep HRV analysis (UNICA-HRV) tool is a novel, open-source MATLAB tool designed to fill the gap in current HRV analysis tools. In particular, the integration of ECG and HRV data with hypnogram information, which illustrates the progression through the different sleep stages, eases the computation of HRV metrics in polysomnographic recordings. This integration is crucial for accurate phase-specific analysis, as autonomic regulation changes markedly across different sleep stages. The tool supports single- and multiple-subject analyses and is tailored to enhance usability and accessibility for researchers and clinicians without requiring extensive technical expertise. It implements and supports a variety of data inputs and configurations, allowing for flexible, detailed HRV analyses across sleep stages, employing classical and advanced metrics, such as time-domain, frequency-domain, non-linear, complexity, and Poincaré plot indexes. Validation of the tool against established tools like Kubios and PhysioZoo indicates its robustness and precision in generating reliable HRV metrics, that are essential not only for sleep research, but also for clinical diagnostics. The introduction of UNICA-HRV represents a significant simplification for sleep studies, and its open-source nature (licensed under a Creative Commons Attribution 4.0 International License) allows to easily extend the functionality to other needs.

Arterial Blood Gas Analysis and Clinical Decision-Making in Emergency and Intensive Care Unit Nurses: A Performance Evaluation

Background: This study aimed to evaluate Emergency Department and Intensive Care Unit nurses' skills in interpreting blood gas analysis results and to use those interpretations in clinical decision-making. Methods: In this prospective, multicenter, simulation-based study, nurses from the Emergency Department (ED) of Merano Hospital and the Intensive Care Unit (ICU) of Bolzano Hospital, Italy, were presented with 16 clinical vignettes based on real patient cases. These vignettes were designed to evaluate the nurses' ability to identify patients with time-dependent conditions and recommend appropriate therapeutic interventions. Outcomes measured included sensitivity, specificity, and agreement with physician-assigned urgency levels and therapy recommendations. Results: Among the 43 participants (26 ICU and 17 ED nurses), specificity in excluding patients without time-dependent conditions or organ replacement needs was high. However, sensitivity in identifying time-dependent conditions was less than 50%. Agreement with physician-assigned urgency levels was low, with Cohen's kappa values of 0.139 for ICU nurses and 0.218 for ED nurses. Nurses with lower self-confidence in interpreting BGA results made more errors, while other personal or professional factors did not significantly impact performance. Conclusions: Although critical care nurses can effectively rule out patients without time-dependent conditions, their ability to identify such conditions requires improvement. These findings underscore the need for targeted training programs to enhance nurses' BGA interpretation skills and clinical decision-making in high-pressure, time-sensitive situations.

A Brief History of Clinical Electrocardiography: A Century After Einthoven's Nobel Prize

The invention of the string galvanometer by Willem Einthoven in 1901 ushered in a new era of clinical investigation. Previous instruments were capable only of rudimentary measurements that were of limited utility. Advances in physiology and engineering allowed Einthoven to construct a device that was uniquely capable of recording the minute electrical currents of the heart. Early string galvanometers were massive, immovable machines. Over time, the apparatus became smaller and portable, allowing examinations at the bedside. In the decades that followed, clinicians used the now ubiquitous instrument to evaluate cardiac arrhythmias, coronary artery disease, and conduction abnormalities. The remainder of the century saw the evolution of the contemporary 12-lead electrocardiogram, with standards established regarding technique and nomenclature. Awarded the Nobel Prize in 1924 for "his discovery of the mechanism of the electrocardiogram," Einthoven's legacy endures in modern clinical medicine.

The feasibility of a serial 12‑lead ECG wireless patch in the hospital setting

BACKGROUND

Chest pain is the second most common reason to present to the emergency department in the United States, and the ECG is a first-line diagnostic tool for myocardial ischemia assessment. For patients with ongoing symptoms or unclear initial ECGs, guidelines recommend performing multiple standard ECGs at 15-30-min intervals during the first 1-2 h, which improves acute coronary syndrome (ACS) detection by 15 % and accelerates triage of high-risk ACS patients. However, obtaining serial ECG is not consistently practiced due to overcrowding and the limited technical abilities of current 12‑lead ECG machines. This study aimed to evaluate an FDA-approved wireless 12‑lead ECG patch for serial cardiac monitoring in the hospital setting.

METHODS

Prospectively, ECG patch was applied in the Mason-Likar electrode configuration after obtaining consent. The patch remained in place for at least one hour. Clinical Utility of the ECGs was categorized from 1 to 3: 1 = uninterpretable, 2 = borderline, and 3 = interpretable.

RESULTS

Among hospitalized cardiac patients, 28 consented to wear the ECG patch for at least one hour and patients were free to ambulate during the study. Most (70 %) patients were in sinus rhythm, and an episode of asymptomatic TMI was captured. The clinical utility of the ECGs (n = 364) was mostly interpretable, 64 % (n = 231), while 15 % (n = 55) were uninterpretable and 18 % (n = 65) were borderline. Most (69 %) preferred the patch, while 12 % preferred telemetry. The hospitalized cardiac patients reported significantly better ability to ambulate with the ECG patch (Z = -3.607, p < 0.001).

CONCLUSION

Thus, this experiment demonstrated that the ECG patch provides quality serial ECG monitoring and captures TMI of hospitalized cardiac patients without increasing burden.

Association of Cardiovascular Risk Factors With Major and Minor Electrocardiographic Abnormalities: A Report From the Cross-Sectional Phase of Tehran Cohort Study

Background and Aims

In the current study, we aimed to identify the association between major and minor electrocardiographic abnormalities and cardiovascular risk factors.

Methods

We used the Tehran cohort study baseline data, an ongoing multidisciplinary, longitudinal study designed to identify cardiovascular disease risk factors in the adult population of Tehran. The electrocardiograms (ECGs) of 7630 Iranian adults aged 35 years and above were analyzed. ECG abnormalities were categorized into major or minor groups based on their clinical importance. Results were obtained by multivariable logistic regression and are expressed as odds ratios (ORs).

Results

A total of 756 (9.9%) participants had major ECG abnormalities, while minor abnormalities were detected in 2526 (33.1%). Males comprised 45.8% of the total population, and 41.8% of them had minor abnormalities. Individuals with older age, diabetes (OR = 1.35; 95% CI: 1.11-1.64), and hypertension (OR = 2.21; 95% CI: 1.82-2.68) had an increased risk of major ECG abnormalities. In contrast, intermediate (OR = 0.69; 95% CI: 0.57-0.84) and high physical activity levels (OR = 0.66; 95% CI: 0.51-0.86) were associated with a lower prevalence of major abnormalities. Male sex, older age, hypertension, and current smoking were also associated with an increased prevalence of ECG abnormalities combined (major or minor).

Conclusion

Major and minor ECG abnormalities are linked with important cardiovascular risk factors such as diabetes and hypertension. Since these abnormalities have been associated with poor outcomes, screening patients with cardiovascular risk factors with an ECG may distinguish high-risk individuals who require appropriate care and follow-up.

Efficacy of Various Dry Electrode-Based ECG Sensors: A Review

Long-term electrocardiogram (ECG) monitoring is crucial for detecting and diagnosing cardiovascular diseases (CVDs). Monitoring cardiac health and activities using efficient, noninvasive, and cost-effective techniques such as ECG can be vital for the early detection of different CVDs. Wet electrode-based traditional ECG techniques come with unavoidable limitations of the altered quality of ECG signals caused by gel volatilization and unwanted noise followed by dermatitis. The limitation related to the wet electrodes for long-term ECG monitoring in static and dynamic postures reminds us of the urgency of a suitable substitute. Dry electrodes promise long-term ECG monitoring with the potential for significant noise reduction. This review discusses traditional and alternative techniques to record ECG in terms of meeting the efficient detection of CVDs by conducting a detailed analysis of different types of dry electrodes along with materials (substrate, support, matrix, and conductive part) used for fabrication, followed by the number of human subjects they have been used for validation. The degradation of these electrodes has also been discussed briefly. This review finds a need for more validation on a sufficient number of subjects and the issue of cost and noise hindering the commercialization of these dry electrodes.

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.

Progression and prognostic significance of electrocardiographic findings in patients with cardiac amyloidosis

AIMS

This study aimed to evaluate the change of the main electrocardiographic (ECG) characteristics and their prognostic role across the main subtypes of cardiac amyloidosis [light-chain amyloidosis (AL) and hereditary (ATTRv) and wild-type transthyretin amyloidosis (ATTRwt)].

METHODS AND RESULTS

This multicentre, retrospective study was performed in six referral centres for cardiac amyloidosis. Clinical and ECG data were collected at the first and last evaluations. Three hundred fifty-six patients were included (AL, n = 105; ATTRv, n = 50; ATTRwt, n = 201). The median age was 76 (67-81) years, and 271 (74%) were men. At baseline, patients with ATTRwt showed a higher prevalence of conduction abnormalities compared with those with AL [first-degree atrioventricular block, n = 51 (40%) vs. n = 13 (34%), P < 0.01; left bundle branch block, n = 23 (11%) vs. n = 2 (2%), P < 0.01], and patients with AL more often had low QRS voltage [n = 58 (55%); in ATTRv, n = 17 (34%); in ATTRwt, n = 67 (33%), P value < 0.01] and T wave inversion compared with those with ATTR [n = 39 (37%); in ATTRv, n = 9 (18%); in ATTRwt, n = 37 (18%)]. After a median follow-up of 15 (8-26) months, the adjusted differences in mean PR, QRS interval, total, peripheral, and precordial QRS scores were similar across subtypes of amyloidosis (P value for linear regression > 0.05). The adjusted odds ratios for the development of right bundle branch block were higher in AL compared with ATTRwt [odds ratio 4.7 (95% confidence interval 1.5-15), P < 0.05]. QRS duration at baseline remained independently associated with patient survival in the overall population even after adjustment for relevant clinical variables [hazard ratio 1.78 (95% confidence interval 1.13-2.8), P < 0.01].

CONCLUSIONS

The progression of the ECG abnormalities seems similar across amyloidosis subtypes. QRS duration could be a marker of more advanced disease.

Early Pacemaker Dependency After Heart Transplantation Is Associated with Permanent Pacemaker Implantation, Graft Failure and Mortality

AIMS

Patients after heart transplantation (HTX) often experience post-transplant bradycardia, but little is known about the outcomes of early pacemaker dependency after HTX. We compared post-transplant mortality, graft failure, and the requirement for the permanent pacemaker implantation of patients with and without early pacemaker dependency after HTX.

METHODS

We screened all adult patients for early pacemaker dependency after HTX (defined as immediately after surgery) who underwent HTX at Heidelberg Heart Center between 1989 and 2022. Patients were stratified by diagnosis and type of early pacemaker dependency after HTX (sinoatrial or atrioventricular conduction disturbance).

RESULTS

A total of 127 of 699 HTX recipients (18.2%) had early pacemaker dependency after HTX, including 52 patients with sinoatrial conduction disturbances (40.9%) and 75 patients with atrioventricular conduction disturbances (59.1%). Patients with early pacemaker dependency after HTX showed both increased 1-year overall mortality after HTX (55.9% vs. 15.2%, p < 0.001) and higher mortality due to graft failure (25.2% vs. 4.2%, p < 0.001). Multivariate analysis revealed early pacemaker dependency after HTX (HR: 5.226, 95% CI: 3.738-7.304, p < 0.001) as an independent risk factor for 1-year mortality after HTX. Patients with early pacemaker dependency after HTX had a higher rate of 30-day (7.1% vs. 0.4%, p < 0.001) and 1-year (11.8% vs. 0.5%, p < 0.001) permanent pacemaker implantation after HTX compared to patients without early pacemaker dependency after HTX.

CONCLUSIONS

Patients with early pacemaker dependency after HTX had a significantly higher rate of post-transplant mortality, graft failure, and the requirement for permanent pacemaker implantation.

Amplitude spectrum area is dependent on the electrocardiogram magnitude: evaluation of different normalization approaches

Objective.Amplitude Spectrum Area (AMSA) of the electrocardiogram (ECG) waveform during ventricular fibrillation (VF) has shown promise as a predictor of defibrillation success during cardiopulmonary resuscitation (CPR). However, AMSA relies on the magnitude of the ECG waveform, raising concerns about reproducibility across different settings that may introduce magnitude bias. This study aimed to evaluate different AMSA normalization approaches and their impact on removing bias while preserving predictive value.Approach.ECG were recorded in 118 piglets (1-2 months old) during a model of asphyxia-associated VF cardiac arrest and CPR. An initial subset (91/118) was recorded using one device (Device 1), and the remaining piglets were recorded in the second device (Device 2). Raw AMSA and three ECG magnitude metrics were estimated to assess magnitude-related bias between devices. Five AMSA normalization approaches were assessed for their ability to remove detected bias and to classify defibrillation success.Main results.Device 2 showed significantly lower ECG magnitude and raw AMSA compared to Device 1. CPR-based AMSA normalization approaches mitigated device-associated bias. Raw AMSA normalized by the average AMSA in the 1st minute of CPR (AMSA1m-cpr) exhibited the best sensitivity and specificity for classification of successful and unsuccessful defibrillation. While the optimal AMSA1m-cprthresholds for balanced sensitivity and specificity were consistent across both devices, the optimal raw AMSA thresholds varied between the two devices. The area under the receiver operating characteristic curve for AMSA1m-cprdid not significantly differ from raw AMSA for both devices (Device 1: 0.74 vs. 0.88,P= 0.14; Device 2: 0.56 vs. 0.59,P= 0.81).Significance.Unlike raw AMSA, AMSA1m-cprdemonstrated consistent results across different devices while maintaining predictive value for defibrillation success. This consistency has important implications for the widespread use of AMSA and the development of future guidelines on optimal AMSA thresholds for successful defibrillation.

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.

PATCH (Preferred Attachment Strategy for Optimal Electrocardiograms)-1 Study

12-Lead electrocardiography (ECG) is among the most frequently performed tests in medical practice. Despite its pivotal role in diagnostic and treatment decisions, baseline artifacts and errors in lead placement are common. The PATCH (Preferred Attachment Strategy for Optimal Electrocardiograms)-1 study enrolled patients with stable cardiovascular disease and a clinical indication for an ECG. Each participant underwent both a standard (S) 12-lead ECG and a patch (P) ECG (EKG-Patch™) during one routine ambulatory clinic visit. The P-ECG has an all-in-one design with built-in lead wires attached to pre-positioned electrodes. An experienced clinical research coordinator performed all ECGs. Each was interpreted by an experienced cardiologist blinded to the method of ECG. A total of 200 participants (67.4 ± 14.9 years; range: 21-95 years) (women 44%) had P- and S-ECGs. Common clinical indications included coronary artery disease (40.5%), essential hypertension (14.0%), heart failure (10.5%), atrial fibrillation (10.0%) and valvular heart disease (6.5%). Many participants had more than one indication. The P-ECG provided a tracing in 1.4 ± 0.5 min compared to 2.4 ± 0.5 min with the S-ECG (p < 0.001). Most participants either preferred the P-ECG (47%) or did not have a preference (52%). Baseline artifacts that impacted interpretability were detected in 13 (6.5%) P-ECGs and 30 (15.0%) S-ECGs (p = 0.006). Heart rhythm, rate, conduction, axis, intervals (PR, QRS, QT, and QTc) and ST-T wave findings did not differ between P-and S-ECGs. In conclusion, the P-ECG was preferred among participants, had fewer baseline artifacts than the S-ECG, and provided a rapid and reproducible ECG in patients with stable cardiovascular disease in an ambulatory clinic setting.

Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review

This review systematically analyzes the latest advancements in preprocessing techniques for Electrocardiography (ECG) and Magnetocardiography (MCG) signals over the past decade. ECG and MCG play crucial roles in cardiovascular disease (CVD) detection, but both are susceptible to noise interference. This paper categorizes and compares different ECG denoising methods based on noise types, such as baseline wander (BW), electromyographic noise (EMG), power line interference (PLI), and composite noise. It also examines the complexity of MCG signal denoising, highlighting the challenges posed by environmental and instrumental interference. This review is the first to systematically compare the characteristics of ECG and MCG signals, emphasizing their complementary nature. MCG holds significant potential for improving the precision of CVD clinical diagnosis. Additionally, it evaluates the limitations of current denoising methods in clinical applications and outlines future directions, including the potential of explainable neural networks, multi-task neural networks, and the combination of deep learning with traditional methods to enhance denoising performance and diagnostic accuracy. In summary, while traditional filtering techniques remain relevant, hybrid strategies combining machine learning offer substantial potential for advancing signal processing and clinical diagnostics. This review contributes to the field by providing a comprehensive framework for selecting and improving denoising techniques, better facilitating signal quality enhancement and the accuracy of CVD diagnostics.

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.

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.

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.