ECG Measurements Research Articles

A DSP-Based EBI, ECG, and PPG Measurement Platform

A DSP-Based EBI, ECG, and PPG Measurement Platform

A Prospective Observational Pilot Study on the Effects of the Activity-Based Stress Release Program on the Mental State and Autonomic Nervous System in Psychiatric Patients.

In our pilot study, we investigated the psychological (well-being and personal coping strategies) and physiological (assessed by heart rate variability (HRV)) effects of the newly developed activity-based stress release (ABSR) program for people with depressive disorders, also in combination with burnout symptoms and/or anxiety disorders. A single-arm prospective observational study was carried out. Twenty participants diagnosed with depression and burnout or anxiety disorders were invited to participate in the 8-week ABSR program. All participants filled in two questionnaires (the multidimensional mental state questionnaire and the Ehrenfeld inventory on coping style) before, directly after and 2 months after the 8-week ABSR program. In addition, all participants were offered a 24-hour Holter ECG measurement before, directly after and 2 months after the 8-week ABSR program to measure HRV. Calmness and serenity showed a statistically significant improvement after the 8-week ABSR program. However, complete questionnaire sets were not available from all of the participants. In the four individuals who had agreed to a 24-hour Holter ECG, the overall HRV and parasympathetic activity increased. The ABSR program is an encouraging novel therapeutic approach: A significant improvement in calmness and serenity was observed after ABSR. A larger and controlled trial of this treatment option in depression and burnout is recommended. It should be further investigated how ABSR affects task-evoked and resting brain activity, and what effects it has on the cardiovascular system in general (better blood flow and normalization of activity are to be expected).

Attentive Bi-LSTM-Based Method for Noise Suppression in Ambulatory ECG Measurements

Attentive Bi-LSTM-Based Method for Noise Suppression in Ambulatory ECG Measurements

Machine learning models of 6-lead ECGs for the interpretation of left ventricular hypertrophy (LVH)

BackgroundLeft Ventricular Hypertrophy (LVH) is closely linked to the cardiovascular disease prognosis, and thus, timely diagnosis improves outcomes. Diagnosis is challenging due to dependency on doctor's visits and a 12‑lead ECG. In addition, the interpretation of LVH from ECGs is challenging due to variability of ECG measurements, body habitus, electrode positioning, several LVH ECG criteria and EP mechanisms. The aims of this study are to evaluate different big data-driven machine learning models for ECG LVH interpretation based on limb leads only, and to compare the performance of an ECG parameter-based statistical model with a deep learning-based model. Methods and dataThe first two models are binary class Random Forest (RF) models, an ensemble learning method which constructs many decision trees at training time and predicts the class chosen by the greatest number of trees at inference time. One random forest is trained using the following five features: lead aVL R-wave amplitude, lead I, II, aVL ST segment amplitude, and QRS duration. The second RF model uses 54 features across all limb leads, including the five features used by the smaller model. The second type of model is a multi-class deep neural network (DNN) which takes median beats of 6 limb leads arranged in Cabrera sequence as input. The signal preprocessing included forming median beats, filtering with a 40-Hz lowpass filter, and down-sampling to 125 Hz. The DNN models consist of 1 lead-formation convolutional layer, 5 downsampling convolutional resnet blocks with skip connections, and 3 fully connected layers. The training dataset consisted of 1 million 10-s 12‑lead ECGs, and an independent test dataset consisted of 250,000 10-s ECGs from the Mayo Clinic. ResultsThe five-parameter RF model has the prediction performance of Area Under the Receiver-Operator Curve (AUC) 0.78, and the larger RF model had AUC of 0.83. The DNN model for ECG LVH detection achieves AUC 0.92 using only the limb leads, compared to an AUC of 0.98 for the full 12‑lead DNN. ConclusionThe study shows that machine learning models trained only on limb leads achieve promising results with potential to add clinical value to early detection mechanisms. We also observe that the RF model splits parameters by thresholds known to be characteristic of LVH, and that the DNN model can automatically detect morphology differences from 6 limb lead ECGs. This will be meaningful for expanding the capabilities of potential electrical LVH detection in mobile 6‑lead ECG devices.

P-wave durations from automated electrocardiogram analysis to predict atrial fibrillation and mortality in heart failure.

P-wave indices have been used to predict incident atrial fibrillation (AF), stroke, and mortality. However, such indices derived from automated ECG measurements have not been explored for their predictive values in heart failure (HF). We investigated whether automated P-wave indices can predict adverse outcomes in HF. This study included consecutive Chinese patients admitted to a single tertiary centre, presenting with HF but without prior AF, and with at least one baseline ECG, between 1 January 2010 and 31 December 2016, with last follow-up of 31 December 2019. A total of 2718 patients were included [median age: 77.4, interquartile range (IQR): (66.9-84.3) years; 47.9 males]. After a median follow-up of 4.8years (IQR: 1.9-9.0years), 1150 patients developed AF (8.8/year), 339 developed stroke (2.6/year), 563 developed cardiovascular mortality (4.3/year), and 1972 had all-cause mortality (15.1/year). Compared with 101-120ms as a reference, maximum P-wave durations predicted new-onset AF at ≤90ms [HR: 1.17(1.11, 1.50), P<0.01], 131-140ms [HR: 1.29(1.09, 1.54), P<0.001], and ≥141ms [HR: 1.52(1.32, 1.75), P<0.001]. Similarly, they predicted cardiovascular mortality at ≤90ms [HR: 1.50(1.08, 2.06), P<0.001] or ≥141ms [HR: 1.18(1.15, 1.45), P<0.001], and all-cause mortality at ≤90ms [HR: 1.26(1.04, 1.51), P<0.001], 131-140ms [HR: 1.15(1.01, 1.32), P<0.01], and ≥141ms [HR: 1.31(1.18, 1.46), P<0.001]. These remained significant after adjusting for significant demographics, past co-morbidities, P-wave dispersion, and maximum P-wave amplitude. Extreme values of maximum P-wave durations (≤90ms and ≥141ms) were significant predictors of new-onset AF, cardiovascular mortality, and all-cause mortality.

Assessing the measurement error of different methods used to calculate Pulse Pressure Variation

Objective: To assess the measurement error of different methods used to calculate Pulse Pressure Variation. Background: Many studies have demonstrated the use of pulse pressure variation (PPV) as a predictor of fluid responsiveness as long as the limitations to its use are understood and respected. These limitations have proven a constraint in the use of PPV and various researchers have published methods of overcoming these constraints in daily practice. Different methods also exist to calculate PPV. This study aims to systematically calculate the measurement error of the different methods used to calculate PPV and compare them. Methods: After approval of the institutional trial board and ethics committee of Ghent University Hospital, Ghent, Belgium, and registration with the local code number B670201629642 (intern:EC/2016/1113), postoperative measurements of invasive arterial pressure and ECG were simultaneously recorded over 1 minute between 29/11/2016 to 16/11/2018. Data was then analyzed using different methods, namely (i) individual PPV averaged over fixed number of respiratory cycles (iPPV family), (ii) pooled PPV over fixed number of respiratory cycles (pPPV family) and (iii) methods over fixed window in terms of time (Aboy and Lansdorp).The Taffe extension of the Bland Altman method was used to compare and determine the measurement error of these four different methods. IPPV1 was chosen as the common reference. Differential en proportional bias and precision are reported as the intercept and the slope respectively of the models studied. Results: Data from 27 subjects were collected. The iPPV showed minimal bias and improved precision. pPPV showed increasing bias (0.879 - 1.999) with the increase in the respiratory cycles as well as precision (0.633-1.08). The Aboy algorithm model showed reduction in bias (-0.473 - -0.139) and precision (0.235-0.146) by the larger fixed windows. Bias increases from the smaller windows to the larger windows in the Lansdorp method. Precision improves over the same range. Conclusions: Every method has its own measurement error. There is a proportionality in the measurement error in the methods we compared for calculating PPV. The bias is variable by each method we studied.

Abstract 11580: Clinical Validation of a Smartphone Compatible 6L Electrocardiogram Device in Ambulatory Patients

Introduction: Novel ECG devices seek to facilitate easier obtainment of ECG recordings, while not compromising accuracy, compared to a standard clinical 12-L ECG. We aimed to compare the accuracy of the AliveCor Kardia 6L device with simultaneously recorded 12-L ECG recording with the patient in the same position. Hypothesis: The AliveCor Kardia 6L ECG device demonstrates data highly correlated with a standard 12-L ECG. Methods: We included patients seen in an outpatient cardiology clinic. ECG data using the Kardia 6L and a standard 12-L were obtained simultaneously in the seated position. Median beats for each method were compared using Pearson’s correlation. Results: One hundred patients (mean age 57±18, 55% female) were included. Lead aVL R wave amplitude (r= 0.99), T wave amplitude (r= 0.99), T wave axis (r= 0.96) QRS axis (r= 1.00), QRS duration (r= 0.94), and QT duration (r= 0.96) demonstrated excellent correlation between the two methods (Figure). Conclusion: The 6L Kardia device allows for accurate ECG measurements in ambulatory patients. This illustrates implications for the diagnosis of cardiac diseases, including left ventricular hypertrophy, in both clinical and nonclinical settings.

Abstract 12072: EKG Appearance and Evolution of Baseline EKG-Characteristics in the Worldwide First Genetically Modified Porcine-to-Human Xenotransplant (“Pig Heart-in-Human Body”)

Introduction: The worldwide first genetically modified porcine-to-human cardiac xenotransplantation was performed in January 2022 at the University of Maryland with the recipient surviving for 61 days. Methods: Daily 12- lead ECGs were obtained during the post-operative period. Results: Accepted “pig heart-in-pig body” ECG parameters demonstrate a short PR interval (50-120ms), short QRS (70-90ms) and short QT (260-380ms). However, the first-ever EKG of a genetically modified heart xenotransplant (“pig heart-in-human body”) demonstrated a relatively longer PR interval of 190ms, QRS duration of 138ms and QT of 538ms (in setting of sinus bradycardia, interventricular conduction delay and inferior/antero-lateral T wave inversions; Figure 1). Prolonged intrinsic PR intervals remained stable during the post-operative course with 210±22ms [range 142-246ms]. However, atrial pacing demonstrated evidence of decremental intra-atrial conduction delay/AV-conduction delay most pronounced on day 12 (PR: 380ms). QRS duration remained prolonged with 145±16ms [range 116-192ms] but shortened during the post-operative course (day 21-40 vs 41-60: 148±14ms vs 132±11ms; p&lt;0.001). Increased QT persisted with 509±54ms [range 384-650ms] with dynamic fluctuations (QT nadir around day 14 (428±22ms vs 533±36ms; p&lt;0.001). Conclusions: ECG parameters of the first genetically modified porcine-to-human cardiac xenotransplant (“pig heart-in-human body”) demonstrated prolongation over the usually encountered ECG measurements in the donor (“pig heart- in-pig body”) including changes in cardiac depolarization and repolarization. Prolonged ECG parameters persisted but showed dynamic changes during post-operative period. This provides a first insight into the novel and evolving field of xenotransplanation suggesting a complex interplay of inter-species physiology and porcine denervation in addition to post-surgical and medication-related changes.

Measuring Suite for Vascular Response Monitoring during Hyperbaric Oxygen Therapy by Means of Pulse Transit Time (PTT) Analysis.

The efficacy of hyperbaric oxygen therapy in treating wound healing disorders is well established. The obvious explanation is the presence of elevated oxygen tissue tensions during the high-pressure oxygen exposure. This explanation omits that the effective agent, elevated oxygen tension, is only present for 6.25% of the time. To investigate possible prevailing vascular changes caused by HBOT, the presented device monitors the vascular response during therapy by Pulse-Transit-Time analysis. The device allows synchronous 1 kHz ECG and PPG measurements. The data are stored in a 1 GBit flash drive and retrieved post-therapy. Normoxic measurements on the authors with and without nicotine validate the device's functionality. Measurements during HBO therapy have been successfully performed.

On uniqueness theorems for the inverse problem of electrocardiography in the Sobolev spaces

AbstractWe consider a mathematical model related to reconstruction of cardiac electrical activity from ECG measurements on the body surface. An application of recent developments in solving boundary value problems for elliptic and parabolic equations in Sobolev type spaces allows us to obtain uniqueness theorems for the model. The obtained results can be used as a sound basis for creating numerical methods for non‐invasive mapping of the heart.

The Importance of ECG Offset Correction for Premature Ventricular Contraction Origin Localization from Clinical Data

Abstract In this study, the inverse solution with a single dipole was computed to localize the premature ventricular contraction (PVC) origin from long term multiple leads ECG measurements on fourteen patients. The stability of the obtained results was studied with respect to the preprocessing of signals used as an input to the inverse solution and the complexity of the torso model. Two methods were used for the baseline drift removal. After an averaging of the heartbeats, the influence of the retention or elimination of the remaining offset at the beginning of the PVC signal was examined. The inverse computations were performed using both homogeneous and inhomogeneous patient-specific torso models. It was shown that the remaining offset in the averaged signals at the beginning of the PVC signal had the most significant impact on the stability of the resulting position within the ventricles. Its elimination stabilizes the location of the results, decreases the sensitivity to the torso model complexity and decreases the sensitivity to the primary baseline drift removal method. The additional offset correction decreased the mean distance between the results for all patients from 17-18 mm to 1-2 mm, regardless of the baseline drift removal method or the torso model complexity.

Electromechanical factors associated with response to cardiac resynchronization therapy

Abstract Background Cardiac resynchronization therapy (CRT) is clinically proven in patients with heart failure (HF) and left bundle branch block (LBBB). However, approximately 30% of CRT individuals are non responsive to the therapy while factors affecting electromechanical coupling remain not fully understood. Objective To determine the optimal combination of electromechanical parameters associated with responsiveness to CRT. Methods Sixty-two patients with HF/LBBB underwent invasive anatomo-electromechanical mapping (AEMM) of the left ventricle using NOGA XP system (Biosense Webster), cardiac magnetic resonance (cMR), transthoracic echocardiography and 12-lead ECG. Remodeling was quantified based on the end-systolic volume (ΔESV) decrease at 6-month follow-up. Response to CRT was defined as ΔESV ≤−15%. QRS duration (QRSd) was measured from a surface ECG. Area strain was obtained from AEMM and used to calculate systolic stretch index (SSI) and total left ventricular mechanical time (TLVMT). Total left ventricular activation time (TLVAT) and transeptal time (TST) were derived from AEMM and ECG. Scar burden was evaluated from cMR late gadolinium enhancement imaging. Results Significant correlations were observed between ΔESV and TST (rho=0.42; responder: 50 [20–58] vs non-responder: 33 [8–44] ms), TLVAT (−0.68; 81 [73–97] vs 112 [96–127] ms), scar burden (−0.27; 0.0 [0.0–1.2] vs 8.7 [0.0 19.1]%) and SSI (0.41; 10.7 [7.1–16.8] vs 4.2 [2.9–5.5]), but not QRSd (0.11; 155 [140–176] vs 167 [155–177] ms). TLVAT and SSI had a high predictive value for CRT response (AUC&amp;gt;0.80). TLVAT (OR=1.50), scar burden (0.91) and SSI (0.04) were independent factors associated with a positive response to CRT. Individuals with SSI &amp;gt;7.9% and TLVAT &amp;lt;91 ms all responded to CRT, while low SSI and prolonged TLVAT were more common in non-responders. Conclusion Electromechanical parameters show better correlation with CRT response than traditional surface ECG measurements. The absence of scar combined with high SSI and low TLVAT ensures effectiveness of CRT. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Swiss National Science Foundation, Statutory funds of the Medical University of Silesia

Hybrid Mock Circulatory Loop Simulation of Extreme Cardiac Events.

This paper presents preliminary methods of incorporating the pathological conditions of cardiac arrhythmias and valvular stenosis in hybrid mock circulation loop (hMCL) operation for the enhanced verification and validation of mechanical circulatory support devices such as VADs. The MGH/MF Waveform datasets from PhysioNet database (including both nominal and clinically diagnosed arrhythmic ECG measurements) as well as cardiovascular system model updates are used to recreate arrhythmic events and valvular stenosis in vitro. Preliminary results show the hMCL can recreate each tested cardiac event within 2% and 4% mean error for reference pressure tracking in the aortic and left ventricular pressure chambers, respectively. Further, frequency spectrum analysis comparisons using the magnitude-squared coherence analysis shows close alignment between measured arrhythmic and hMCL realized pressure frequency content. The generation of cardiac arrhythmias and valvular stenosis around a VAD via both model and acute measurement based methods was achieved. Pathological conditions such as cardiac arrhythmias and valvular stenosis are limited in documentation despite the large percentage of patients who experience these events. This paper provides a means to begin incorporating these events into hardware-in-the-loop mock circulatory systems for next generation VAD validation and verification.

Contactless Monitoring of Heart Rate Variability During Respiratory Maneuvers

Heart rate variability (HRV) is a noninvasive marker for the cardiovascular system and the substantial influence of this system on the autonomous nervous system (ANS). Contact and remote photoplethysmography (PPG) proved to be a reliable means of pulse rate variability (PRV) measurement as in wearable devices or imaging PPG using video cameras with evolved technical specifications. However, PRV effectiveness remains unclear during controlled breathing rate. Specifically, respiratory rate, among other physiological factors, applies profound effects on ANS balance. In this study, we demonstrated that PRV can be a reliable marker, as HRV, during various controlled breathing rates. We evaluated the proposed PRV monitoring study against reference HRV obtained from an ECG measurement device. PRV was measured from video streams captured using a smartphone and a webcam for the upper abdominal section of healthy volunteers while in standing position and performing six different respiratory maneuvers. We evaluated frequency and nonlinear HRV indices. Agreement between all indices was tested for camera-based devices and ground truth for all performed tasks. The agreement presented a consistent mean close to 95% limit of agreement (LoA). Also, we employed spectral analysis and measured the spectral divergences, normalized root mean squared error (NRMSE), and correlation. Kullback-Leibler Divergence median and percentiles results indicate congruent spectrums, where the maximum median was 0.18. A significant spectral correlation with minimum value of 0.98, and relatively low spectrum NRMSE of 0.24 were observed. Moreover, the results led to HRV physiological related phenomena which agreed with physiologically interpretations found in the literature.

Ordered Nanopillar Arrays of Low Dynamic Noise Dry Bioelectrodes for Electrocardiogram Surface Monitoring.

Flexible bioelectric dry electrodes are an important part of long-term medical healthcare monitoring systems. In this study, a new method is proposed for the preparation of dry electrodes with micronanopillar arrays structured by designing dimensionally tunable anodized aluminum oxide (AAO) templates, by which polyaniline/thermoplastic polyurethane single-layer micronanopillar array structured dry electrodes (PANI/TPU-SE) and polyaniline/thermoplastic polyurethane double-layer micronanopillar array structured dry electrodes (PANI/TPU-DE) are prepared. Compared with the planar structure, the micronanopillar array structure can reduce the contact gap between the electrode and skin and increase the contact area, thus exhibiting lower contact impedance and higher signal quality. At 0.1 Hz, the impedances of the wet electrode, PANI/TPU-DE300, PANI/TPU-SE10, and planar structure electrodes are 269.5 kΩ, 375.5 kΩ, 398.1 kΩ, and 2.257 MΩ, respectively, and the impedance value for PANI/TPU-DE300 is smaller than that for PANI/TPU-SE10 and closer to that for the wet electrode. In addition, because the surface of the micronanostructure can conform to the human skin, about 210.7% increase in the peel strength of double-layer structure electrodes compared to flat structure electrodes, it shows a low baseline drift in the dynamic ECG measurement, and the signal-to-noise ratio in the walking state can reach 21.33 ± 5.4775 dB. Therefore, the prepared bioelectric dry electrode has a wide application prospect in the fields of wearable medical monitoring.

Heartbeat Detection in Seismocardiograms with Semantic Segmentation.

Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability.

Parasympathetic Reactivity and Oxidative Stress among Participants Suffering from the Polycystic Ovarian Syndrome

Background: Burden of PCOs is increasing throughout the world with a prevalence of 8% - 13% of females getting affected around the globe. Many studies have also reported autonomic dysfunction among participants with PCOs with poor response to the parasympathetic nervous system and increased response to the sympathetic nervous system in contrast to that in normal counterparts. This study will aim to assess the parasympathetic reactivity of the cardiac system and oxidative stress among participants suffering from PCOs in a tertiary care hospital in Pakistan. Females with PCOs will be compared to normal counterparts of similar age and weight. Methodology: A cross-sectional study was conducted in the Department of Physiology of Jinnah Hospital, Lahore Pakistan from January 2021 to December 2021. Participants were recruited by purposive sampling following meeting inclusion and exclusion criteria. PCOs participants were matched with non-affected females with similar weight and age. The participants were assessed for oxidative stress by undergoing examination of serum catalase and MDA levels. Autonomic reactivity was assessed by recording ECG on taking deep breaths, on standing and following Valsalva maneuver. Both groups were compared for outcomes and measurement. Results: The outcomes of this study showed that the group with PCOs females showed a significantly higher rate of heartbeat per minute, resting systolic and diastolic blood pressure in comparison to their normal counterparts. The serology reports showed PCOs affected group to have lower levels of catalase and increased levels of MDA in opposition to non-affected females. Furthermore, it was seen with serology and ECG measurement that E: I ratio, E-I difference, 30:15 ratio, and Valsalva ratio were all found to be positively associated with levels of plasma catalase, whereas all measurements were negatively associated with serum MDA levels in a group consisting of PCOs affected females. Conclusion: This study concludes that a variation is seen in the parasympathetic stimulation among participants suffering from PCOs and this alteration of autonomic reactivity correlates to reduced response to parasympathetic inputs leading to a deficiency. The results of the study did not signify ay neuropathy which could be suggested by measurement of assessment of cardiac autonomic function. However, it cannot be overlooked that these measurements were different from that of normal counterparts. Lower catalase levels and increased levels of MDA among the PCOs group signify oxidative stress. Keywords: Parasympathetic reactivity, Oxidative stress, Polycystic ovarian syndrome

&lt;em&gt;In Silico&lt;/em&gt; Clinical Trials for Cardiovascular Disease

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

In Silico Clinical Trials for Cardiovascular Disease.

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

Development and validation of a new ECG algorithm based on the analysis of lead V3 to determine the origin of outflow tract ventricular arrhythmias

Abstract Funding Acknowledgements Type of funding sources: None. Introduction (Aim) To preoperatively differentiate the site of origin outflow tract ventricular arrhythmias (OT-VA) it is of utmost importance for procedure planning yet challenging for those with a precordial transition (PT) at lead V3: close-proximity anatomical structures produce similar VA morphologies, thus leading to possible misdiagnosis. We sought to create an ECG algorithm that could be accurate and useful in overcoming this problem. Methods and Results 74 consecutive patients (Pts) with OT-VA who underwent successful radio-frequency catheter ablation (V3 PT 60.8%) were enrolled. The ECG characteristics of the first 30 Pts were analyzed (retrospective cohort); those with a PT at lead V3 underwent activation-mapping of both OT to ensure a correct diagnosis. LV-OT and RV-OT groups shared similar characteristics, including BMI and BSA. Above all ECG measurements, the V3 duration index (DI) and amplitude percentage, both calculated with the formula [R\(R+S)]*100 (computing duration and amplitude measurements respectively), showed the greatest AUC. A V3 DI &amp;lt; 50% established a certain diagnosis of origin at RV-OT (sensitivity 86.7%, specificity 100%, PPV 100%, NPV 88.2%; AUC 0.931). If the V3 DI was ≥ 50%, a V3 R wave percentage ≥ 50% established a certain diagnosis of origin at LV-OT (sensitivity 86.7%, specificity 100%, PPV 100%, NPV 88.2%, AUC 0.951). While a direct study of the RV-OT or LV-OT is suggested in these first two cases, few classification errors occurred only in the rare event of a V3 DI ≥ 50% with a V3 R wave percentage &amp;lt; 50% (Figure 1). In the prospective cohort, the two indices were confirmed to have the two best AUCs (0.992 and 0.986, respectively), and the algorithm showed an accuracy of 95.45%. Since the aortic cusps are structures posterior to RV-OT, therefore further away from V2, these foci more frequently show a QRS onset at ≥ V3; a characteristic which in our series was associated with an odds ratio for LV-OT origin of 4.1 [95% CI 1.47 – 11.39], p = 0.007. Using the transition ratio, we found a statistical significance only in lead V3, with a cut-off of ≥ 1 for predicting an LV-OT origin (LV-OT vs. RV-OT: 7.70 ± 12.75 vs. 0.70 ± 0.55, p &amp;lt; 0.001; AUC 0.898). Analyzing the overall case series, we can generalize the following: duration and amplitude indices showed an increasing AUC passing from lead V1 to V3 (Figure 2). The indices based on the complete measurement of the wave R compared to the QRS showed better AUCs than those on partial measures in V2 and V3: the duration index was better than the deflection index, as well as the amplitude percentage was better than the amplitude ratio. Conclusions The usefulness of the algorithm lies in providing preoperatively two assured outcomes in most cases (86.36%), allowing to limit the procedure to one OT directly - even in the event of a V3 PT - while it can select a small subgroup of complex Pts in which a study of both OT is recommended.