Conventional Electrocardiogram Research Articles

EASI monitoring in ventricular tachycardia, an alternative to conventional electrocardiography?

Abstract Background 12-lead electrocardiogram (ECG) is the gold standard for detecting ventricular arrhythmias. In cardiac critical care units (UCC), monitoring systems are used to detect transient events such as arrhythmias or ST-segment acute changes. The discomfort caused in the patient by the need for 10 electrodes to obtain the 12 leads continuously means that complete monitoring is not routine. The EASI monitoring system, based on vector electrocardiography, is an alternative that uses only 5 electrodes (4 thoracic and 1 ground) and theoretically allows obtaining a complete standard ECG. Objectives The main endpoint of this study was to evaluate the diagnostic accuracy of the EASI monitoring system in patients with ventricular tachycardia (VT), compared with conventional ECG. Methods Seven cases of patients admitted to the CCU due to episodes of VT are presented. The patients were monitored with the EASI system upon arrival, and the conventional ECG and the one derived from the EASI system were taken simultaneously for VT events for comparison. The 4 electrodes of the EASI system were placed, as recommended, on the sternal manubrium (S), lower sternum at the level of the 5th intercostal space (E), at the level of the 5th intercostal space, and on the right and left midclavicular lines (I and A). Results Analysing case by case, the axis of the tachycardia was similar in 5 patients, being able to assess the origin of the tachycardia in them. In 2 patients it was different. These cases of patients that differ, one episode has its origin in the right ventricular outflow tract (endocardial) and the other in the area of the left ventricular summit (epicardial). Two patients with different tachycardias and no cardiac characteristics in common. Figure 1. Conclusions EASI monitoring is a feasible alternative for monitoring patients in the CCU, which could give us reliable information (compared with standard ECG) with limitations in some cases in which the cardiac axis differs. For patients admitted for arrhythmic events, a better characterization of the differences would be interesting in order to optimize its use.

Influence of different electrode placement patterns on electromagnetic interference generated by Left Ventricular Assist Devices (LVADs) during electrocardiogram performance

Abstract Background Heart failure (HF), with an incidence of approximately 3/1000 person-years, often progresses to advanced stages where symptoms persist despite treatment. In such cases, mechanical circulatory assist devices, particularly left ventricular assist devices (LVADs) in ambulatory patients, enhance survival and quality of life. These LVADs generate electromagnetic interference, resulting in noise artefacts in conventional electrocardiograms (ECGs). The conventional ECG should be configured with a low-pass filter of 150 Hz and a high-pass filter of 0.05 Hz, as recommended by the American Heart Association/American College of Cardiology/Heart Rhythm Society. LVADs, such as the HeartMate 3 (Abbott®), typically operate at a rotational speed of 5000 to 6000 rpm, corresponding to oscillatory frequencies of 83.3 to 100 Hz. Objective To assess the impact of various surface electrode placement strategies and modifications to the low-pass filter on the electromagnetic interference produced by LVADs. Methods We conducted an observational study involving seven patients with HeartMate 3 adjusted to various rotational speeds (refer to Table 1). Three distinct electrode placement schemes (AHA, Mason Likar, and modified Torso) were employed for the frontal plane leads. The horizontal plane leads remained unmodified. Each patient underwent a total of 6 ECGs, corresponding to each of the aforementioned schemes. ECG recordings were performed using a high-pass filter set at 0.05 Hz and a low-pass filter set at 150 Hz. Additionally, a set of ECGs was obtained by modifying the low-pass filter to 40 Hz for further analysis. Results Figure 1 displays ECGs obtained from a male with a HeartMate 3 operating at 4600 rpm or 76.6 Hz. This device employs an algorithm to generate an "artificial pulse" by cyclically accelerating and decelerating the pump speed by 2000 rpm every 2 seconds, resulting in rotational speeds of 2600 rpm and 6600 rpm (43.3 Hz and 110 Hz, respectively). No specific placement scheme demonstrated consistent noise reduction across all cases, likely due to inter-individual variability resulting from factors such as the final position of the LVAD the rpm in each case, and the thoracic impedance of each patient. However, the modification of the low-pass filter to 40 Hz yielded a noteworthy improvement in noise reduction across all cases. In our limited sample, this adjustment did not pose issues for ECG interpretation. Nevertheless, it is essential to acknowledge that excessively low filter settings may not only eliminate noise but also potentially discard clinically relevant high-frequency signals (e.g., pacemaker spikes, large amplitude Rs, QRS notches). Conclusions Individualizing the electrode placement scheme is crucial for minimizing noise generated by LVADs. The adjustment of the low-pass filter to 40 Hz could generally prove beneficial in reducing artifact generation, despite the clinical limitations it may entail.

Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80–90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.

Prognostic relevance of ventricular arrhythmias in surgical patients with gastrointestinal tumors.

Individuals diagnosed with gastrointestinal tumors are at an increased risk of developing cardiovascular diseases. Among which, ventricular arrhythmia is a prevalent clinical concern. This suggests that ventricular arrhythmias may have predictive value in the prognosis of patients with gastrointestinal tumors. To explore the prognostic value of ventricular arrhythmias in patients with gastrointestinal tumors receiving surgery. We retrospectively analyzed data from 130 patients undergoing gastrointestinal tumor resection. These patients were evaluated by a 24-h ambulatory electrocardiogram (ECG) at the Sixth Affiliated Hospital of Sun Yat-sen University from January 2018 to June 2020. Additionally, 41 general healthy age-matched and sex-matched controls were included. Patients were categorized into survival and non-survival groups. The primary endpoint was all-cause mortality, and secondary endpoints included major adverse cardiovascular events (MACEs). Colorectal tumors comprised 90% of cases. Preoperative ambulatory ECG monitoring revealed that among the 130 patients with gastrointestinal tumors, 100 (76.92%) exhibited varying degrees of premature ventricular contractions (PVCs). Ten patients (7.69%) manifested non-sustained ventricular tachycardia (NSVT). The patients with gastrointestinal tumors exhibited higher PVCs compared to the healthy controls on both conventional ECG [27 (21.3) vs 1 (2.5), P = 0.012] and 24-h ambulatory ECG [14 (1.0, 405) vs 1 (0, 6.5), P < 0.001]. Non-survivors had a higher PVC count than survivors [150.50 (7.25, 1690.50) vs 9 (0, 229.25), P = 0.020]. During the follow-up period, 24 patients died and 11 patients experienced MACEs. Univariate analysis linked PVC > 35/24 h to all-cause mortality, and NSVT was associated with MACE. However, neither PVC burden nor NSVT independently predicted outcomes according to multivariate analysis. Patients with gastrointestinal tumors exhibited elevated PVCs. PVCs > 35/24 h and NSVT detected by 24-h ambulatory ECG were prognostically significant but were not found to be independent predictors.

Comparison of continuous 24-hour and 14-day ECG monitoring for the detection of cardiac arrhythmias in patients with ischemic stroke or syncope.

Previous studies show that using 12-lead electrocardiogram (ECG) or 24-h ECG monitor for the detection of cardiac arrhythmia events in patients with stroke or syncope is ineffective. The 14-day continuous ECG patch has higher detection rates of arrhythmias compared with conventional 24-h ECG monitoring in patients with ischemic stroke or syncope. This cross-sectional study of patients with newly diagnosed ischemic stroke or syncope received a 24-h ECG monitoring and 14-day continuous cardiac monitoring patch and the arrhythmia events were measured. This study enrolled 83 patients with ischemic stroke or syncope. The detection rate of composite cardiac arrhythmias was significantly higher for the 14-day ECG patch than 24-h Holter monitor (69.9% vs. 21.7%, p = .006). In patients with ischemic stroke, the detection rates of cardiac arrhythmias were 63.4% for supraventricular tachycardia (SVT), 7% for ventricular tachycardia (VT), 5.6% for atrial fibrillation (AF), 4.2% for atrioventricular block (AVB), and 1.4% for pause by 14-day ECG patch, respectively. The significant difference in arrhythmic detection rates were found for SVT (45.8%), AF (6%), pause (1.2%), AVB (2.4%), and VT (9.6%) by 14-day ECG patch but not by 24-h Holter monitor in patients with ischemic stroke or syncope. A 14-day ECG patch can be used on patients with ischemic stroke or syncope for the early detection of AF or other cardiac arrhythmia events. The patch can be helpful for physicians in planning medical or mechanical interventions of patients with ischemic stroke and occult AF.

One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms

Background and ObjectiveLeft ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN). MethodsUtilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis. ResultsFor LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model's performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807). ConclusionsThis study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.

The clinical value of P-wave terminal force in lead V1 in evaluating pericardial thickness in tuberculous constrictive pericarditis

AimTo investigate the relationship between p wave terminal force (Ptfv1) and pericardial thickness in patients with tuberculous constrictive pericarditis.MethodsFrom January 2018 to October 2022, 95 patients with tuberculous constrictive pericarditis who needed pericarditis dissection in a hospital were collected, and 3 patients who did not meet the criteria were excluded, a total of 92 cases. The absolute value of Ptfv1 in conventional electrocardiogram was tested before surgery, and pericardial thickness was measured by echocardiography and chest CT. Pericardial thickness was measured after pericardial dissection. Pearson correlation analysis was used, R software was used to make scatter plot, and non-parametric square test was used. The correlation of postoperative measurements with echocardiography, chest CT and absolute value of Ptfv1 was analyzed.ResultsPearson correlation analysis was conducted with postoperative measurements and echocardiography measurements, postoperative measurements and chest CT measurements, and postoperative measurements and absolute value of Ptfv1. Pearson correlation analysis showed that the correlation coefficients between postoperative measurements and echocardiography, chest CT and Ptfv1 values were statistically significant. Scatter plot and nonparametric Chi-square test showed that postoperative measurements were consistent with absolute values of echocardiography, chest CT and Ptfv1 (p < 0.05). And this study found that the distribution of the value of Ptfv1 ≥ 5 was higher than the value of Ptfv1 < 5 after pericardiectomy (0.95:0.05) in the absolute value of Ptfv1 ≥ 0.04 which measured before pericardiectomy. The hypothesis was statistically significant (p < 0.05).ConclusionThe absolute value of Ptfv1 in electrocardiogram can be used as an auxiliary diagnostic index to evaluate pericardial thickness in tuberculous constrictive pericarditis.

ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy

BackgroundPhospholamban (PLN) p.(Arg14del) variant carriers are at risk for development of malignant ventricular arrhythmia (MVA). Accurate risk stratification allows timely implantation of intracardiac defibrillators and is currently performed with a multimodality prediction model. ObjectiveThis study aimed to investigate whether an explainable deep learning–based approach allows risk prediction with only electrocardiogram (ECG) data. MethodsA total of 679 PLN p.(Arg14del) carriers without MVA at baseline were identified. A deep learning–based variational auto-encoder, trained on 1.1 million ECGs, was used to convert the 12-lead baseline ECG into its FactorECG, a compressed version of the ECG that summarizes it into 32 explainable factors. Prediction models were developed by Cox regression. ResultsThe deep learning–based ECG-only approach was able to predict MVA with a C statistic of 0.79 (95% CI, 0.76–0.83), comparable to the current prediction model (C statistic, 0.83 [95% CI, 0.79–0.88]; P = .054) and outperforming a model based on conventional ECG parameters (low-voltage ECG and negative T waves; C statistic, 0.65 [95% CI, 0.58–0.73]; P < .001). Clinical simulations showed that a 2-step approach, with ECG-only screening followed by a full workup, resulted in 60% less additional diagnostics while outperforming the multimodal prediction model in all patients. A visualization tool was created to provide interactive visualizations (https://pln.ecgx.ai). ConclusionOur deep learning–based algorithm based on ECG data only accurately predicts the occurrence of MVA in PLN p.(Arg14del) carriers, enabling more efficient stratification of patients who need additional diagnostic testing and follow-up.

Accuracy of Smartwatch Electrocardiographic Recording in the Acute Coronary Syndrome Setting: Rationale and Design of the ACS WATCH II Study.

Background: Acute Coronary Syndrome (ACS), with or without ST-segment elevation, is a major contributor to global mortality and morbidity. Swift diagnosis and treatment are vital for mitigating cardiac damage and improving long-term outcomes. The 12-lead electrocardiogram (ECG) currently serves as the gold standard for diagnosis in ACS with ST-segment elevation and may support the diagnosis in ACS without ST-segment elevation. However, the growing prevalence of smartwatches enables the acquisition of electrocardiographic data without traditional ECG equipment. While smaller studies support smartwatch ECG use, larger-scale validation within ACS remains lacking. The ACS WATCH II study aims to validate smartwatch ECG recordings for ACS. Methods: The primary objective is to validate smartwatch-obtained electrocardiographic data in patients presenting with ACS. Two cohorts of 120 patients each, presenting ACS with and without ST-segment elevation, will be assessed. Smartwatches will capture recordings of leads I, III, and V2 alongside standard ECGs. These leads, chosen due to a 97% ACS diagnosis sensitivity in previous studies, will undergo blind evaluation by two experienced external assessors against conventional ECG. Additionally, a control sample of 60 healthy individuals will be included. Conclusions: ACS WATCH II pioneers large-scale prospective validation of smartwatch ECG recordings in ACS patients. Additionally, it indirectly validates a swift diagnostic approach using three leads (I, III, and V2). This could expedite time-critical ACS diagnoses and simplify access through smartwatch-based diagnosis.

Application of remote electrocardiogram monitoring systems in chest pain centers for patients with high-risk chest pain.

Chinese chest pain centers (CPCs) have been expanding and maturing for the past decade, but patient wait times for pre-hospital care remain long. To demonstrate that the remote electrocardiogram (ECG) monitoring system can ensure more efficient treatment for patients with ST-elevation myocardial infarction (STEMI) in CPCs, we compared patients with high-risk chest pain who used remote ECG monitoring systems to those who used conventional ECGs in retrospective cohort study. Based on the inclusion and exclusion criteria, 290 patients who visited our CPC between June 2019 and March 2022 with acute chest pain and a diagnosis of STEMI as well as patients who had undergone an emergency primary percutaneous coronary intervention were selected. Among them, 73 patients with STEMI had employed remote real-time dynamic 12-lead ECG monitoring devices, while 217 patients with STEMI (i.e., the controls) had used conventional ECG monitoring. The effectiveness of treatment procedures for the two groups was investigated. As statistical measures, the symptom onset-to-wire times, first medical contact (FMC)-to-wire times, door-to-wire times, major adverse cardiac events in hospital, and the troponin T levels were analyzed. Compared with the control group, the patients with remote real-time dynamic 12-lead ECG monitoring devices showed shorter times for both symptom onset-to-wire (234.8 ± 95.8 min vs. 317.6 ± 129.6 min, P= 0.0321) and from symptom onset-to-FMC (170.5 ± 86.3 min vs. 245.3 ± 115.6min, P= 0.0287); this group also had a lower 30-day mortality rate (2.73% vs. 4.14%, P= 0.003). The differences between the two groups were statistically significant (P< 0.05). With remote real-time dynamic 12-lead ECG monitoring equipment, myocardial ischemia can be treated more quickly, leading to fewer possible cardiac events and a better prognosis.

The Reliability of the Apple Watch's Electrocardiogram.

Background An electrocardiogram (ECG) is a standard tool used to detectvarious cardiovascular abnormalities. Detection sensitivity for atrial fibrillation (AF) was recently shown to be greatly increased by using short, intermittent ECG recordings.Modern mobile ECG recording devices that can monitor patients' heart activities around the clock have made this a reality.The Apple Watch is one of these portable ECG devices that can detect heart rhythms and is approved by the American FDA for screening and detecting AF. Objectives We compared the results of the Apple Watch I lead ECG with conventional ECG results to assess the sensitivity and specificity of the Apple Watch I lead ECG. We then determined the abnormalities that can be detected by the Apple Watch I lead ECG. Methods This study was conducted on outpatient cardiac clinics at King Abdullah bin Abdulaziz University Hospital (KAAUH), and Prince Sultan Cardiac Center (PSCC),from May to October 2021. A standard 12-lead ECG was recorded and compared with the Apple Watch I lead ECG. A total of 469 ECG comparisons were included in this study and evaluated by two investigators. The data on patient demographics, medical and medication history, and ECG data were reviewed and analyzed using IBMSPSSStatistics for Windows, Version 23 (Released 2015; IBM Corp., Armonk, New York, United States). Results No significant differences were seen between the Apple Watch and the 12-lead ECG in terms of the studied ECG characteristics. A significant and strong positive correlation between the heart rate measurements in the 12-lead ECG and Apple Watch ECG was documented. The most commonly found abnormalities in the Apple Watch ECG wereAF in 37 (7.9%), followed by first-degree atrioventricular (AV) block in 32 (6.8%). The sensitivity of Apple Watch's automated interpretation to detect an AF was 99.54%, while the manual interpretation yielded a sensitivity of 100%. Conclusion The results of this study demonstrated a robust relationship between the 12-lead ECG and Apple Watch ECG in the diagnosis of arrhythmias. Consequently, cardiac patients may consider the Apple Watch ECG a trustworthy remote monitoring technique.

Deep learning-based regional ECG diagnosis platform.

To enable the intelligent diagnosis of a variety of common Electrocardiogram (ECG), we investigate the deep learning-based ECG diagnosis system. From January 2015 to December 2019, four consecutive years of 100,120 conventional 12-lead ECG data were collected in our hospital. Utilizing this dataset, we constructed a deep learning model designed to intelligently diagnose prevalent ECG anomalies by employing a multi-task learning framework. The system performance was evaluated using various metrics, including sensitivity, specificity, negative predictive value, positive predictive value, and so forth. Additionally, we employed an ECG intelligent diagnostic platform for clinical application to undertake real-time online analysis of 2500 conventional 12-lead ECG samples in June 2020, aiming to validate our model. At this stage, we compared the performance of our model against the traditional manual identification method. The efficacy of the ECG intelligent diagnostic model was notably high for common and straightforward ECG patterns, such as sinus rhythm (F1=98.01%), sinus tachycardia (F1=96.26%), sinus bradycardia (F1=94.88%), and a normal electrocardiogram (F1=91.71%), as well as for Premature Ventricular Contractions (F1=91.62%). Nevertheless, when diagnosing rarer and more intricate ECG anomalies, the system requires an increased number of samples to refine the deep learning models. During the validation stage, our model exhibited better efficiency in terms of accuracy, labor time and labor cost when compared to the manual identification approach. Our deep learning-driven intelligent ECG diagnostic model clearly demonstrates significant clinical utility. The integrated artificial intelligence diagnosis system not only has the potential to augment physicians in their diagnostic processes but also offers a viable avenue to reduce associated labor costs.

Comparison of vest-recorded electrocardiogram versus conventional electrocardiogram concerning ST-deviation in acute coronary syndrome

Abstract Background and Aim With the 12 lead ECG being the cornerstone in the diagnosis of ACS Development of a home-based, user-friendly recording of a 12 lead ECG will be a breakthrough in combating delay in diagnosing Acute Coronary Syndrome (ACS). This study evaluates the potential of a wearable vest 12-lead ECG (V-ECG) compared with the gold standard, conventional 12-lead ECGs (C-ECG) in diagnosing ACS. Methodology A prospective single-centre observational study conducted in a tertiary care centre. C-ECG and wearable V-ECG were recorded in hemodynamically stable adult patients and patients with normal ECG. The location of the electrodes in the vest included ten specific torso positions. Two independent physicians interpreted all the ECGs in a blinded fashion. Results Out of 241 patients, the vest fitted 236 patients (97.9%). Among 234 patients (72.5% males, 27.5% females; mean age 57.5 years), 154 had ACS. Three patients had an additional set of ECGs recorded, given their dynamic ECG changes during hospitalisation. The sensitivity and specificity of V-ECG, compared with the gold standard C-ECG for the diagnosis of ACS, were 98.7% ( CI 95.36-99.77) and 96.5% (CI 90.2-99.1), respectively. Among patients with STEMI, the sensitivity of V-ECG went up to 100% (CI 95.9-100). V-ECG has correctly identified anterior and inferior STEMI patients with 100% accuracy. Conclusion 12 lead ECG recording by a wearable garment using novel torso positions is feasible in the vast majority, including both genders, and reasonably accurate for diagnosing ACS. Highlights Commercial availability of this vest might enable an otherwise lay person to record an ECG in an out-of-hospital setting (even at home), thus leading to an expedited diagnosis of ACS and saving precious time and lives.

EASI Monitoring: An Alternative to Conventional Electrocardiography in the Recording of Ventricular Tachycardia

This study compares the EASI monitoring setting with the conventional 12-lead electrocardiogram (ECG) in patients experiencing ventricular tachycardia (VT) episodes. The results revealed that the EASI monitoring setting demonstrated a VT morphology that closely resembled that of the conventional ECG in the majority of cases. This finding suggests that EASI monitoring could serve as a valuable alternative in situations where obtaining a conventional ECG is either risky or not feasible.

Deep learning unmasks the ECG signature of Brugada syndrome.

One in 10 cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a nonintrusive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine-learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians in identifying the presence of this potentially life-threatening heart disease.

Removal of Motion Artifacts in Capacitive Electrocardiogram Acquisition: A Review.

Capacitive electrocardiogram (cECG) systems are increasingly used for the monitoring of cardiac activity. They can operate within the presence of a small layer of air, hair or cloth and do not require a qualified technician. They can be integrated into wearables, clothing or objects of daily life, such as beds or chairs. While they offer many advantages over conventional electrocardiogram systems (ECG) that rely on wet electrodes, they are more prone to be affected by motion artifacts (MAs). These effects, which are due to the relative movement of the electrode in relation to the skin, are several orders of magnitude higher than ECG signal amplitudes, they occur in frequencies that might overlap with the ECG signal, and they may saturate the electronics in the most severe cases. In this paper, we provide a detailed description of MA mechanisms that translate into capacitance variations due to electrode-skin geometric changes or into triboelectric effects due to electrostatic charge redistribution. A state-of-the-art overview of the different approaches based on materials and construction, analog circuits and digital signal processing is provided as well as the trade-offs to be made using these techniques, to mitigate MAs efficiently.

PO-02-226 NON-INVASIVE ELECTROCARDIOGRAPHIC IMAGING FOR THE CHARACTERIZATION OF COMPLEX ATRIAL TACHYARRHYTHMIAS

Non-invasive characterization of complex atrial tachyarrhythmias, regarding atrial flutter (AFL) and focal atrial tachycardia (AT) is challenging. Conventional electrocardiogram (ECG) faces several limitations. Planning a preprocedural strategy before AFL-AT catheter ablation (CA) is needed. To evaluate the diagnostic capacity of a novel non-invasive electrocardiographic imaging (ECGI) system that does not require previous CT or MRI cardiac imaging. 42 patients (27 males, CHA2DS2-VASc score 2±1, LVEF 54.5 ± 11.0 %, and LA area 31.3 ± 7.0 cm2) undergoing CA for AT-AFL were prospectively included in the study. Before the procedure, the ECGI system estimated the patient atria geometry using a 3D torso reconstruction and an artificial intelligence-based algorithm. Multiple electrocardiograms were recorded using a 64-electrodes vest during the CA procedure. The different diagnostic capacities of the periprocedural ECG and ECGI were assessed compared with endocavitary electroanatomical mapping (EAM) regarding three endpoints: (1) the involved atrial cavity, (2) the mechanism (focal or macro-reentry), and (3) the ablation target site defined as the area where the earliest activation was located for focal arrhythmias, and the precise anatomical pathway for macro re-entrant tachyarrhythmias. Regarding the ECG, endpoints were assessed based on validated algorithms by 2 different observers blinded from the diagnosis. 48 atrial tachyarrhythmias were evaluated and its characterization using this novel ECGI system was possible in 94% of the cases: directly for 36 arrhythmias and after decreasing ventricular response using vagal maneuvers for 9. ECGI obtained global accuracy to identify the involved atrial cavity of 92%, the mechanism of 90%, and the ablation target of 83% (Table). In challenging cases, such as in patients with a history of CA for atrial fibrillation and in patients without a final diagnosis of typical counterclockwise AFL, the accuracy of ECGI was consistent compared to the whole study population (Table). The ECGI diagnostic capacity significantly outclassed the ECG regarding the identification of the involved cavity (p<0.001), the mechanism (p<0.05), and the ablation target (p<0.001). ECGI could have prevented all unnecessary transseptal punctures (n=7), representing 28% of the overall punctures. This novel non-invasive ECGI system accurately characterizes complex atrial tachyarrhythmias (Figure) without needing previous CT or MRI cardiac imaging.

PO-02-167 IMPACT OF OBESITY ON QUALITY OF LIFE AND CLINICAL OUTCOME IN PATIENTS WITH ATRIAL FIBRILLATION UNDERGOING PRIMO PULMONARY VEIN ISOLATION

Obesity is a major risk factor for the incidence and progression of atrial fibrillation (AF), negatively impacting AF ablation outcomes. Weight loss in obese patients prior to AF ablation may improve quality of life (QoL) and AF ablation success rates. To assess the impact of overweight and obesity on patient-reported outcomes (QoL scores) and clinical outcomes (AF recurrence) in patients undergoing index AF ablation procedure. This single-center cohort study retrospectively included 238 patients undergoing primo radiofrequency (RF) pulmonary vein isolation (PVI) who completed 1 year follow-up, including the Toronto AF Severity Scale (AFSS) questionnaire at baseline, 4-, and 12 months post-PVI for assessment of QoL (AF severity, AF burden, and global well-being). At baseline, patients were categorized by body mass index (BMI): normal (<25 kg/m2); overweight (≥25 - <30 kg/m2); and obese (≥30 kg/m2). AF recurrence was evaluated by either 24-h Holter monitoring, AliveCor KardiaMobile devices, or conventional electrocardiograms (ECG). Patients were divided based on BMI: 37.8% normal; 44.1% overweight; and 18.1% obese. Obese patients were more likely to be women (men 46.5%,p<0.01), and have diabetes mellitus (25.6%,p<0.01). AF recurrence rates detected during 12 months follow-up were similar among the three BMI groups: normal 27.8%, overweight 33.3%, and obese 30.2% (p=0.70). After AF ablation, all QoL scores in each BMI group improved, with no between-group differences (Table 1). Subgroup analysis showed significantly improved QoL scores in patients without AF recurrence independent of BMI category, however in patients with AF recurrence no significant improvement of AF severity and global well-being was found (Fig 1). Despite AF recurrence, self-reported AF burden significantly improved in the normal and overweight categories, but not in obese patients. A high BMI was not a major determinant of AF recurrence rates after a primo PVI. QoL after PVI was found to be determined by AF recurrence rather than BMI. This study suggests that obese patients might benefit from PVI to a similar extent compared to non-obese patients, though self-reported AF burden might differ between groups.

Evaluation of a new smartphone-based digital stethoscope featuring phonocardiography and electrocardiography in dogs and cats

This study assessed a new smartphone-based digital stethoscope (DS) featuring simultaneous phonocardiographic and one-lead electrocardiogram (ECG) recording in dogs and cats. The audio files and ECG traces obtained by the device were compared with conventional auscultation and standard ECG. A total of 99 dogs and nine cats were prospectively included. All cases underwent conventional auscultation using an acoustic stethoscope, standard six-lead ECG, standard echocardiography and recordings with the DS. All the audio recordings, phonocardiographic files and ECG traces were then blind reviewed by an expert operator. The agreement between methods was assessed using Cohen’s kappa and the Bland-Altman test. Audio recordings were considered interpretable in 90% animals. Substantial agreement was found in the diagnosis of heart murmur (κ = 0.691) and gallop sound (k = 0.740). In nine animals with an echocardiographic diagnosis of heart disease, only the DS detected a heart murmur or gallop sound. ECG traces recorded with the new device were deemed interpretable in 88 % animals. Diagnosis of heart rhythm showed moderate agreement in the identification of atrial fibrillation (k = 0.596). The detection of ventricular premature complexes and bundle branch blocks revealed an almost perfect agreement (k = 1). Overall, the DS showed a good diagnostic accuracy in detecting heart murmurs, gallop sounds, ventricular premature complexes and bundle branch blocks. A clinically relevant overdiagnosis of atrial fibrillation was found but without evidence of false negatives. The DS could represent a useful screening tool for heart sound abnormalities and cardiac arrhythmias..

An ECG Signal Compression Technique Using Fast Normalised Least Mean Square Algorithm

ABSTRACT One of the primary issues is cardiovascular disease in various countries worldwide. Proper diagnosis of heart disease at the right time can minimise the loss of life in heart patients. One of the most prevalent approaches for identifying and classifying heart-related disorders is the electrocardiogram (ECG). ECG can give information mainly on heart functioning, recording heart rate, and tracking heart rhythms. ECG denoising is one of the important tasks in medical diagnostics because it contains life-saving information about a patient. Before analysing, compressing, and classifying, the ECG signal has to be denoised for better performance. This work proposes a new RTL architecture to perform ECG signal denoising using the Fast Normalised Least Mean Square (FNLMS) technique. The proposed technique achieved higher accuracy when compared with the conventional signal denoising technique. The proposed work is implemented with a Field Programmable Gate Array device (FPGA) to analyse hardware resource utilisation. This work achieves RMSE of 0.027, PRD of 42.126 and SNR of 33.125. This result is high performance when compared with existing works. This work consumed less computational time and hardware resource utilisation when compared with the conventional ECG denoising technique.