Standard ECG Research Articles

Abstract 16967: Effect of Catheter Ablation of Persistent Atrial Fibrillation on Surface Atrial ECG Dominant Frequency

Background: There are no standard techniques to track effects of catheter ablation (CA) of persistent atrial fibrillation (PeAF). The Atrial ECG (A-ECG) derived from the standard ECG provides the atrial dominant frequency (DF), an estimation of atrial rate. Research Question: Does CA for PeAF diminish A-ECG DF? Aims: To evaluate change in A-ECG peak DF pre- and post-CA. Methods: We obtained 1-minute epochs of surface ECG pre- and post-CA in 32 patients (age 63±9 years; 71% males) undergoing CA for PeAF. A-ECG was derived by QRST subtraction and independent component analysis filtering, and divided into overlapping10-second windows, each subjected to Fast Fourier Transform. DF was calculated for each lead as the average DF for all 10-sec segments. Peak A-ECG DF was assessed by regional lead group and among all leads. 10 patients were excluded for significant artifact. Of the 22 analyzed patients, all had pulmonary vein isolation and 3 had posterior wall isolation. Two-year outcomes were available in 20 patients (13 were free from recurrent AF). Results: The figure shows the change in the time-frequency plot (left panel) of DF for one subject and the shift in the spectra (right panel) with CA. A-ECG peak DF was significantly lower post- versus pre-CA: 5.96±0.63 vs 6.16±0.64 Hz, p=0.0009. By lead group, peak DFs were all lower post CA - Anterior (V1-3): 5.94±0.62 vs 6.13±0.64 Hz, p=0.0015; Inferior (II, III, aVF): 5.82±0.63 vs 6.01±0.66 Hz, p=0.0040; Lateral (I, aVL, V5-6): 5.82±0.64 vs 5.99±0.63 Hz, p=0.0015. In this small cohort, there was no significant difference in change of peak DF in those free from AF versus with recurrent AF (-2.4±4.3% vs -4.6±3.7%, p=0.26). Conclusion: The A-ECG provides physiologic information of activation rate in PeAF, but despite current, advanced signal processing techniques is analyzable in only 69% of patients. CA results in a significant reduction in DF. Clinical implications of these CA induced changes require further study.

Abstract 17557: Single Limb-Lead Electrocardiogram Accuracy in Screening for Ventricular Pre-Excitation

Introduction: Consumer devices capable of recording bipolar ECGs have been investigated and validated for atrial fibrillation detection and QT interval monitoring. Application of the technology to other electrical abnormalities like Wolff-Parkinson-White (WPW) is limited but could enhance the ability to screen for risk of cardiac arrhythmias before they occur. Aims: 1. Determine how frontal lead ECGs perform in identification of ventricular pre-excitation (VPE).2. Determine which of the standard 3 bipolar leads perform best for this detection.3. Quantify the impact of training of the interpreter on identification of VPE with single lead tracings. Methods: Standard 12-lead ECGs were obtained from 20 patients before and after successful radiofrequency ablation of a single manifest accessory pathway. 3-second tracings were abstracted from leads I, II, and III from each ECG and were presented in random order to interpreters of varying experience and specialization who were asked to evaluate whether the tracing showed VPE. Results were tabulated to determine the accuracy of VPE identification. Descriptive statistics of the performance of each lead (alone and in combinations) and interpreter type were calculated. Results: A total of 52 respondents interpreted 5108 ECG tracings. Some respondents did not read all tracings and there were unequal numbers of respondents in the different interpreter categories. The Table below depicts the performance of each lead and respondent subgroups. Conclusions: VPE can be identified with reasonable accuracy using a single limb-lead ECG tracing, with lead I performing better than leads II and III. Addition of lead II or III improved accuracy by 5% only. Most consumer devices record lead I, suggesting that they could be used to screen for WPW. Interpreter specialization correlated more with accuracy than training status.

Abstract 16707: Clinical Validation of a Chest-Worn Personal ECG Device for the Detection of Atrial Fibrillation

Introduction: Novel consumer-focused personal ECG devices have the potential to revolutionise the way that cardiac arrhythmias are diagnosed and managed. These devices can be used remotely to monitor patients for arrhythmias, which could lead to earlier detection and treatment. In this study, we aimed to compare the cardiologist's interpretation of clinical 12-lead ECG recordings to a consumer-focused dry-electrode ECG chest strap device (Fourth Frontier FX+). Methods: We included patients seen in an outpatient cardiology clinic at the Sri Jayadeva Institute of Cardiovascular Science and Research, Bangalore, India. A total of 163 paired ECG recordings were captured from 85 adult patients (50% female, median age 54 years, AF present at the time of recording in 38%). Patients with critical cardiac problems or life support systems were excluded. ECG data from the Fourth Frontier FX+ (Fourth Frontier, Bangalore, India) and a standard 12-lead ECG were obtained simultaneously in the supine position. PDF reports for both devices were then generated, and the cardiologist's interpretation of the Fourth Frontier FX+ ECG was compared to the cardiologist's interpreted 12L ECG. Results: The cardiologist's interpretation of the Fourth Frontier FX+ data matched the cardiologist-interpreted 12L data for all patients. One FX+ ECG was excluded from the analysis due to not enough data being captured by the device. A total of 33 patients were correctly identified as having atrial fibrillation. Conclusion: The results of this study demonstrate that the Fourth Frontier FX+ ECG monitor is comparable to a 12-lead ECG for cardiac rhythm monitoring and that it can be used remotely to monitor patients for cardiac arrhythmias. This has the potential to change the way that arrhythmias are diagnosed and managed and lead to earlier detection and treatment at scale.

Abstract 13850: Cost-Effectiveness Analysis of Universal Screening for Atrial Fibrillation Using Electrocardiographic Artificial Intelligence in a Rural Community in Taiwan

Introduction: Atrial fibrillation (AF) substantially contributes to strokes, and screening for AF is generally recommended. However, implementing universal screening for AF is challenging in rural areas with limited medical resources. This study assessed the cost-effectiveness of using artificial intelligence-enabled 12-lead electrocardiography (AI-ECG) for AF screening in a rural community. Methods: The cost-effectiveness analysis focused on screening individuals aged 65 or older and utilized a lifelong decision analytic Markov model. The AI-ECG for AF was trained at a medical center in Taiwan, achieving a sensitivity of 0.944 (standard error [SE]: 0.015) and specificity of 0.941 (SE: 0.008). Costs and efficacy of anticoagulant treatments, utility of health status, and clinical variables such as stroke and major bleeding risks were estimated based on literature and epidemiological data from Taiwan. Results were presented as US dollars per quality-adjusted life year (QALY). The base-case analysis compared technicians using AI-ECG for AF detection and referral, and physicians' evaluation with standard 12-lead ECGs to no screening. Uncertainty in the model was addressed through probabilistic sensitivity analysis. Results: Both AI-ECG screening and screening by a physician were found to be more costly yet more effective when compared to no screening. While both strategies exhibited similar effectiveness, the cost was lower with AI-ECG than by a physician ($453 versus $490). Based on 10,000 Monte Carlo simulations, if the willingness to pay (WTP) for a QALY exceeds $9,514, screening by a physician is a cost-effective strategy. However, if the WTP per QALY falls between $2,289 and $9,514, AI-ECG screening is more cost-effective than physician-based screening. Conclusion: This economic evaluation demonstrates the cost-effectiveness of AI-ECG screening for AF, making it a practical alternative for AF screening in areas with limited medical resources.

Abstract 18177: Novel Pre-Operative Non-Invasive Computational 12-Lead ECG Mapping to Facilitate Surgical Ablation of Ventricular Arrhythmias

Background: In patients undergoing cardiac surgery who have ventricular arrhythmias (VAs) such as VT, VF, or PVCs, concurrent surgical ablation is an attractive therapeutic strategy. However, electrophysiologic mapping systems are not routinely available in the OR and it may be difficult to localize the VA origin. We developed a workflow incorporating novel 12-lead ECG computational model-based mapping to localize VAs. Hypothesis: We hypothesized that use of pre-operative computational ECG mapping can help guide surgical ablation of VAs in patients undergoing cardiac surgery. Methods: Patients undergoing cardiac surgery with pre-existing VT, VF or PVCs were enrolled with informed consent. A standard 12-lead ECG of the VA was recorded in the clinic or during non-invasive programmed stimulation in the EP lab. ECG mapping localized the VA and visualized on a 3D model. During circulatory arrest, surgical ablation was performed using either cryoablation or irrigated RF ablation probe. Follow-up was performed with event monitors or ICD monitoring. Results: A total of 7 patients (mean age 55±15 years, female 29%, EF 31%±20%) were enrolled (Table). Surgical indications included mitral annuloplasty (Pt 1), pulmonary thromboendoarterectomy + CABG (Pt 2), coronary artery unroofing (Pt 3), left ventricular assist device (Pt 4-6, Fig 1), and CABG (Pt 7). In this cohort, 5 PVCs, 3 monomorphic VT and 1 VF morphologies were localized using ECG mapping and surgically ablated at time of cardiac surgery. There was a 100% (18 to 0) decrease in VT/VF episodes and 97.2 ± 0.03% reduction in PVC burden at median 9.5 month (IQR 3.7-29.5) follow-up. No intra- or post-operative complications occurred. Conclusions: This case series illustrates feasibility and excellent efficacy of a novel preoperative ECG mapping workflow using a forward-solution algorithm to guide successful and safe concomitant ventricular arrhythmia ablation during cardiac surgery.

Abstract 19210: Interpretation of Deep Learning Model That Identifies Regional Myocardial Dysfunction From the Surface Ecg

Introduction: Cardiac wall motion abnormalities (WMA) are key prognostic indicators for major cardiovascular events and mortality, yet they are poorly detected by current ECG screening tools such as Q waves. Deep learning (DL) models have improved analysis of complex data and novel tools may provide interpretation of these models. Hypothesis: ECG features beyond the Q wave may be identified by analyzing an ECG-trained DL model to improve WMA detection over standard ECG measurements. Methods: We developed a DL neural network using 36,964 unique patients (62.9±16.9 years, 47% female, 58% white) labeled by natural language processing of echocardiography reports within 60 days. To understand how the CNN model identifies WMAs from the ECG input, we implemented Shapley Additive exPlanations (SHAP) and DeepExplainer on R-wave aligned beats over 300ms surrounding the QRS complexes to identify hot-spots across the ECG. We then retrained classifiers on 60mg windows of the ECG to identify which timepoints produced the greatest performance (ablation method). Results: Probing the CNN revealed that ECG regions throughout the QRS and T-waves identified WMA. Analysis output shows that such ECG regions arose throughout the QRS and T-waves, and not just at early regions corresponding to Q waves (Panel A) . An aggregate view of the ECG reports summarizes the most discriminatory ECG regions for all patients (Panel B) . Stepwise analysis of limited windows within aligned ECG beats also confirmed this analysis (Panel C) . We found that the highest performance window was 60-120ms (AUC 0.814), containing most of the QRS, with the lowest performance in the window of 0-60 ms (AUC 0.699) and 0.20-0.80ms (AUC 0.707). Conclusions: The DL model show promise in accurately detecting WMAs and provides key insights into critical ECG regions for WMA detection beyond the Q wave. Further analysis of WMA detection with these expanded regions is needed.

Long-term association of ultra-short heart rate variability with cardiovascular events

Heart rate variability (HRV) is a cardiac autonomic marker with predictive value in cardiac patients. Ultra-short HRV (usHRV) can be measured at scale using standard and wearable ECGs, but its association with cardiovascular events in the general population is undetermined. We aimed to validate usHRV measured using ≤ 15-s ECGs (using RMSSD, SDSD and PHF indices) and investigate its association with atrial fibrillation, major adverse cardiac events, stroke and mortality in individuals without cardiovascular disease. In the National Survey for Health and Development (n = 1337 participants), agreement between 15-s and 6-min HRV, assessed with correlation analysis and Bland–Altman plots, was very good for RMSSD and SDSD and good for PHF. In the UK Biobank (n = 51,628 participants, 64% male, median age 58), after a median follow-up of 11.5 (11.4–11.7) years, incidence of outcomes ranged between 1.7% and 4.3%. Non-linear Cox regression analysis showed that reduced usHRV from 15-, 10- and 5-s ECGs was associated with all outcomes. Individuals with low usHRV (< 20th percentile) had hazard ratios for outcomes between 1.16 and 1.29, p < 0.05, with respect to the reference group. In conclusion, usHRV from ≤ 15-s ECGs correlates with standard short-term HRV and predicts increased risk of cardiovascular events in a large population-representative cohort.

Multicentre, national, investigator-initiated, randomised, parallel-group, register-based superiority trial to compare extended ECG monitoring versus standard ECG monitoring in elderly patients with ischaemic stroke or transient ischaemic attack and the effect on stroke, death and intracerebral bleeding: the AF SPICE protocol

IntroductionAtrial fibrillation (AF) is a major risk factor for ischaemic stroke and transient ischaemic attack (TIA), and AF detection can be challenged by asymptomatic and paroxysmal presentation. Long-term ECG monitoring...

Prognostic value of ECG monitor findings in COVID-19

AimsCOVID-19 can cause severe illness and multiorgan dysfunction. Acute myocardial damage has been detected in a significant portion of patients with COVID-19; therefore, several studies have reported that electrocardiographic findings...

Three-dimensional vector ECG analysis in the diagnosis of the left anterior fascicular block

The relevance of the study is due to the complexity of the differential diagnosis of left anterior fascicular block (LAFB) with myocardial infarction and left ventricular hypertrophy based on a standard ECG.Aim. Determination of the criteria for LAFB according to the data of three- dimensional vectorcardiography with an analysis of the rate of formation of the vector loop.Material and research methods. The study included 85 patients who underwent a 12-lead ECG study. 2 groups of patients were selected. The main group of 56 patients with LAFB, mean age was 63.3±16.6 (M±δ) years, 34 men and 22 women. The control group included 29 healthy individuals, the mean age was 21.4±2.5 (M± δ) years, 12 men and 17 women. All patients underwent synchronous recording of 12 standard ECG leads in the IS «Unified Cardiologist of the Republic of Tatarstan» and reconstruction of a three-d imensional vectorcardiogram using the McFee-Parungao system with an analysis of the vector loop formation rate using EasyECG Rest ATES Medica software (Russia).Results and conclusions. It is shown that for LAFB the movement of the vector loop counterclockwise is typical in the frontal plane; the presence of 2 extrema, the second is directed upwards, to the right and back. On the graph of the rate of formation of a spatial vector loop, 2 peaks are recorded with a decrease in the rate in the final part of the QRS loop by 2–2.5 times relative to the first peak, which reflects a slowdown in myocardial excitation in the output section of the left ventricle.

Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram.

The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients. The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in this model was evaluated for various causes of death. Patients who visited the ED and underwent one or more ECG examinations experienced a high incidence of 30-day mortality [18,734 (5.42%)]. The developed CNN model demonstrated high accuracy in predicting acute mortality (hazard ratio 8.50, 95% confidence interval 8.20-8.80) with areas under the receiver operating characteristic (ROC) curve of 0.84 for the 30-day mortality risk prediction models. This CNN model also demonstrated good performance in predicting one-year mortality (hazard ratio 3.34, 95% confidence interval 3.30-3.39). This model exhibited good predictive performance for 30-day mortality not only for cardiovascular diseases but also across various diseases. The machine learning-based ECG model utilizing CNN screens the risks for 30-day mortality. This model can complement traditional early warning scoring indexes as a useful screening tool for mortality prediction.

Heart rate variability and risk of agitation in Alzheimer's disease: the Atherosclerosis Risk in Communities Study.

Agitation in Alzheimer's disease is common and may be related to impaired emotion regulation capacity. Heart rate variability, a proposed index of autonomic and emotion regulation neural network integrity, could be associated with agitation propensity in Alzheimer's disease. We used the Atherosclerosis Risk in Communities Study cohort data, collected over seven visits spanning over two decades, to investigate whether heart rate variability (change) was associated with agitation risk in individuals clinically diagnosed with dementia due to Alzheimer's disease. Agitation (absence/presence) at Visit 5, the primary outcome, was based on the Neuropsychiatric Inventory agitation/aggression subscale, or a composite score comprising the total number of agitation/aggression, irritability, disinhibition and aberrant motor behaviour subscales present. Visit 1-5 heart rate variability measures were the log-transformed root mean square of successive differences in R-R intervals and standard deviation of normal-to-normal R-R intervals obtained from resting, supine, standard 12-lead ECGs. To aid interpretability, heart rate variability data were scaled such that model outputs were expressed for each 0.05 log-unit change in heart rate variability (which approximated to the observed difference in heart rate variability with every 5 years of age). Among 456 participants who had dementia, 120 were clinically classified to have dementia solely attributable to Alzheimer's disease. This group showed a positive relationship between heart rate variability and agitation risk in regression models, which was strongest for measures of (potentially vagally mediated) heart rate variability change over the preceding two decades. Here, a 0.05 log-unit of heart rate variability change was associated with an up to 10-fold increase in the odds of agitation and around a half-unit increase in the composite agitation score. Associations persisted after controlling for participants' cognitive status, heart rate (change), sociodemographic factors, co-morbidities and medications with autonomic effects. Further confirmatory studies, incorporating measures of emotion regulation, are needed to support heart rate variability indices as potential agitation propensity markers in Alzheimer's disease and to explore underlying mechanisms as targets for treatment development.

Action potential morphology affects T-wave symmetry (simulation study)

BackgroundAssessing T-wave symmetry in addition to QT subintervals measurements can provide novel independent data about ventricular repolarization abnormalities linked with arrhythmogenesis. However, the causes of the changes of T-wave symmetry are not completely understood. In silico studies showed that the more symmetrical T-waves were associated with shorter action potential duration (APD) and larger dispersion of ventricular repolarization (DOR). The aim of present simulation was to study the association between T-wave symmetry and action potential (AP) shape. MethodsECGs were simulated using a cellular automata model shaped as a ventricular wall segment, and two biophysically-detailed models of ventricular AP – the rabbit and the human. The symmetry ratio (SR) was calculated as a T-wave onset-peak to peak-end area ratio. The individual and combined effects of APD, DOR and AP shape on SR were simulated. To study the effect of AP shape, different APs from triangulated to rectangular were simulated. ResultsThe simulations showed that AP shape along with APD and DOR contributes much to T-wave symmetry. APs with a flat phase 3 (triangulated) produced asymmetrical T-waves (SR ≥ 1.5) in all simulations, except the shortest APD range. APs with a rapid phase 3 (rectangular) were associated with more symmetrical T-waves (SR ≤ =1) both at the short and the long APDs. ConclusionSR marker in combination with the standard ECG parameters (QT interval, Tpeak-Tend interval) may be useful to identify the proarrhythmic triangulated AP shape.

Effect of the index of cardiac electrophysiological balance on major adverse cardiovascular events in patients with diabetes complicated with coronary heart disease.

To investigate the prognostic value of the index of cardio-electrophysiological balance (ICEB) and its association with major adverse cardiac events (MACE) and cardiovascular death in diabetic patients complicated with coronary heart disease. A total of 920 diabetic patients were enrolled in this longitudinal study. Participants were categorized into three groups based on their ICEB levels: normal ICEB, low ICEB, and high ICEB. The primary outcome was the occurrence of MACE, and secondary outcomes included cardiovascular death, coronary heart disease (CHD), heart failure (HF), and sudden cardiac arrest (SCA). Patients were followed for a median period of 3.26 years, and the associations between ICEB levels and various outcomes were evaluated. Over the follow-up period, 46 (5.0%) MACE were observed in the normal ICEB group, 57 (6.2%) in the low ICEB group, and 62 (6.8%) in the high ICEB group. Elevated ICEB levels were found to be associated with a higher risk of MACE and cardiovascular death. A significant relationship between ICEB levels and the risk of MACE was observed for both genders. The risk of MACE increased with each unit increment in the ICEB index. However, the two-stage linear regression model did not outperform the single-line linear regression models in determining the threshold effect. This study demonstrates the potential utility of ICEB, derived from a standard non-invasive ECG, as a prognostic tool for predicting MACE and cardiovascular death in diabetic patients complicated with CVD. The associations between ICEB levels and the risk of MACE highlight the importance of understanding cardiac electrophysiological imbalances and their implications in CVD.

The CineECG in ischemia localization in ST-elevation (equivalent) acute coronary syndromes

Aim of the studyIn this proof of concept study we aimed to visualize and quantify the injury vectors using the CineECG in representative examples of ST elevation acute myocardial infarction (STEMI) and STEMI-equivalent electrocardiograms (ECG's). For this purpose ECG's were selected with different ST deviation patterns in acute anterior wall, inferior or posterolateral wall infarctions. MethodsThe ST-amplitudes of the individual leads were measured between J-point and 60 ms after the J-point. These data were used to compute the direction and size (i.e. ST-amplitudes) of the injury vectors of the respective STEMI (equivalent)’s and displayed in the frontal, transverse and sagittal view. The relative contribution of the ST vector was computed for each axis (X,Y,Z) and per view using the length of the projected ST vector on the respective plane. ResultsThe injury vectors accurately pointed to the area at risk in either proximal, mid or distal occlusions of the respective coronary arteries. Moreover in LCX occlusions, where no or small ST abnormalities in the standard ECG were present, the CineECG was found to be capable in pointing towards the ischemic area. Especially the visualization of the ST-vector in the sagittal plane was found to be contributing. ConclusionsThese findings suggest that the CineECG could become a useful additional tool to support the clinician. Moreover our preliminary findings suggest that CineECG may be more capable in detecting ischemic changes in situations where the ECG is not supportive, such as in LCX occlusions. Further studies in patient cohorts are needed to confirm these observations.

Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation.

It is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent atrial fibrillation (non-PeAF). There is limited understanding of whether an AI prediction algorithm could predict the occurrence of non-PeAF from the information of normal sinus rhythm (SR) of a 12-lead ECG. This study aimed to derive a precise predictive AI model for screening non-PeAF using SR ECG within 4 weeks. This retrospective cohort study included patients aged 18 to 99 with SR ECG on 12-lead standard ECG (10 seconds) in Ewha Womans University Medical Center for 3 years. Data were preprocessed into three window periods (which are defined with the duration from SR to non-PeAF detection) - 1 week, 2 weeks, and 4 weeks from the AF detection prospectively. For experiments, we adopted a Residual Neural Network model based on 1D-CNN proposed in a previous study. We used 7,595 SR ECGs (extracted from 215,875 ECGs) with window periods of 1 week, 2 weeks, and 4 weeks for analysis. The prediction algorithm showed an AUC of 0.862 and an F1-score of 0.84 in the 1:4 matched group of a 1-week window period. For the 1:4 matched group of a 2-week window period, it showed an AUC of 0.864 and an F1-score of 0.85. Finally, for the 1:4 matched group of a 4-week window period, it showed an AUC of 0.842 and an F1-score of 0.83. The AI prediction algorithm showed the possibility of risk stratification for early detection of non-PeAF. Moreover, this study showed that a short window period is also sufficient to detect non-PeAF.

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

Background and ObjectivesElectrocardiographic (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 ResultsWe 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. ConclusionsThe 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.

Overall good agreement of smartphone-based and standard base-apex electrocardiography in healthy sheep.

To assess and compare the quality of smartphone ECG tracings to standard (base-apex) ECG tracings and assess agreement of ECG parameters between smartphone-based ECG and standard ECG. 25 rams. The rams were consecutively examined with standard ECG and smartphone-based ECG (KardiaMobile; AliveCor Inc) after physical examination. ECGs were compared for quality score, heart rate, and ECG waves, complexes, and intervals. Quality scores were based on the presence or absence of baseline undulation and tremor artifacts using a 3-point scoring system (lowest possible = 0; highest possible = 3). A lower score was indicative of a better-quality ECG. Smartphone-based ECGs were interpretable in 65% of cases, while 100% of standard ECGs were interpretable. Standard ECG quality was superior to smartphone-based ECG quality, with no agreement in the quality between devices (κ coefficient, -0.0062). There was good agreement for heart rate with mean difference 2.86 beats/min (CI, -3.44 to 9.16) between the standard and smartphone ECGs. Good agreement was observed for P wave amplitude with mean difference 0.02 mV (CI, -0.01 to 0.05), QRS duration with mean difference -10.5 ms (CI, -20.96 to -0.04), QT interval with mean difference -27.14 ms (CI, -59.36 to 5.08), T wave duration with mean difference -30.00 ms (CI, -66.727 to 6.727), and T wave amplitude with mean difference -0.07 mV (CI, -0.22 to 0.08) between the 2 devices. Our findings indicate good agreement between standard and smartphone ECG for most parameters, although 35% of smartphone ECGs were uninterpretable.

Abstract P365: Left Ventricular Hypertrophy In Hypertension-correlation Of Electrocardiography Criteria, Echocardiography And Myocardial Strain Imaging

Background: LVH on ECG has been defined by 37 different criteria with modest degree of differences in accuracy amongst them leading to a lack of consensus. Objectives: Study aims to correlate sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) of selected 17 standard ECG criteria in the diagnosis of LVH, correlation with echocardiography and myocardial global longitudinal strain (GLS). Demographics: Average age of the 104 hypertensive individuals included in the study was 61.86 years with 43.3% women. 73% of subjects had LVH on echocardiography with mean LVMI 115.97g/m2 and mean RWT 0.5. Results: Groningen criteria (24.70%) and Cornell voltage criteria (20.98%) had the best sensitivity for LVH detection. Groningen criteria was defined as QV2+R1+RV5+RV6+SV2+SV4+SV5+SV6 &gt;= 49.5 mm in women and R1+RV5+SII+ SV2+SV6 x QRS duration &gt;= 4500mm msec in men. Sensitivity for other criteria were lesser; Peguero Lo presti(PL) 13.58%, Sokolow Lyon index(SL) 11.11% and Cornell voltage product (CVP) 6.17%. Specificity and Positive predictive value (PPV) with Groningen criteria were 90% and 95.6% and with Cornell voltage criteria 100% and 100% respectively. Best sensitivity results were on combining all 17 ECG LVH criteria (39.5%). Addition of Groningen criteria improved the sensitivity of SL+CV+PL from 28.39% to 38.3%. Combination of the commonly used CV+SL+PL criteria yielded better sensitivity (28.39%) as compared to CV (20.98%) alone. Subjects with mild LVH on echo had an average GLS of -15.46+/-2.4% in 75%, subjects with moderate LVH had an average GLS of -14.23+/-2.8% in 71.4% and subjects with severe LVH had an average GLS of -12.6+/-4.9% in 80%. Conclusion: Irrespective of the voltage criteria used, singular or in combination, sensitivity to diagnose LVH proven on echocardiography is low. Novel Groningen criteria was more sensitive than the previously established standard ECG LVH criteria and had the highest association amongst all in patients with reduction in average GLS. Combination of four criteria SL+CV+PL+Groningen had the best sensitivity (38.3%) which improved only by 1.2% if all 17 criteria were combined.

Electrocardiographic prediction of left ventricular hypertrophy in women and men with left bundle branch block - Comparison of QRS duration, amplitude and voltage-time-integral.

Standard ECG criteria for left ventricular (LV) hypertrophy rely on QRS amplitudes. However, in the setting of left bundle branch block (LBBB), ECG correlates of LV hypertrophy are not well established. We sought to evaluate quantitative ECG predictors of LV hypertrophy in the presence of LBBB. We included adult patients with typical LBBB having ECG and transthoracic echocardiogram performed within 3months of each other in 2010-2020. Orthogonal X, Y, Z leads were reconstructed from digital 12‑lead ECGs using Kors's matrix. In addition to QRS duration, we evaluated QRS amplitudes and voltage-time-integrals (VTIs) from all 12 leads, X, Y, Z leads and 3D (root-mean-squared) ECG. We used age, sex and BSA-adjusted linear regressions to predict echocardiographic LV calculations (mass, end-diastolic and end-systolic volumes, ejection fraction) from ECG, and separately generated ROC curves for predicting echocardiographic abnormalities. We included 413 patients (53% women, age 73±12years). All 4 echocardiographic LV calculations were most strongly correlated with QRS duration (all p<0.00001). In women, QRS duration ≥ 150ms had sensitivity/specificity 56.3%/64.4% for increased LV mass and 62.7%/67.8% for increased LV end-diastolic volume. In men, QRS duration ≥ 160ms had a sensitivity/specificity 63.1%/72.1% for increased LV mass and 58.3%/74.5% for increased LV end-diastolic volume. QRS duration was best able to discriminate eccentric hypertrophy (area under ROC curve 0.701) and increased LV end-diastolic volume (0.681). In patients with LBBB, QRS duration (≥ 150 in women and≥160 in men) is a superior predictor of LV remodeling esp. eccentric hypertrophy and dilation.