Fasciculoventricular accessory pathway masked extensive atrioventricular conduction system disease in a patient with PRKAG2 syndrome.

A 23-year-old male with a history of ventricular pre-excitation and atrial flutter presented for evaluation after recurrent syncope. The possible mechanism of syncope erroneously attributed to pre-excited atrial flutter with fast heart rates in the first hospitalization. The patient was found to have advanced heart block and PRKAG2 genetic mutation in the second hospitalization. The genetic findings and clinical features are consistent with PRKAG2 syndrome (PS). PS is a rare, autosomal dominant inherited disease, characterized by ventricular pre-excitation, supraventricular tachycardia, and cardiac hypertrophy. It is frequently followed by atrial-fibrillation-induced ventricular fibrillation and advanced heart blocks. An accurate differential diagnosis of syncope is important because of the different arrhythmic features and clinical course of PS.

Can the introduction of a 12-lead ECG help reduce mortality in those presenting with foot ulceration to multidisciplinary diabetic foot clinics? An observational evaluation of a real-world implementation pilot in England.

AIMS/HYPOTHESIS: The risk of dying within 2 years of presentation with diabetic foot ulceration is over six times the risk of amputation, with CVD the major contributor. Using an observational evaluation of a real-world implementation pilot, we aimed to assess whether for those presenting with diabetic foot ulceration in England, introducing a 12-lead ECG into routine care followed by appropriate clinical action was associated with reduced mortality. METHODS: Between July 2014 and December 2017, ten multidisciplinary diabetic foot services in England participated in a pilot project introducing 12-lead ECGs for new attendees with foot ulceration. Inception coincided with launch of the National Diabetes Footcare Audit (NDFA), whereby all diabetic footcare services in England were invited to enter data on new attendees with foot ulceration. Poisson regression models assessed the mortality RR at 2 and 5 years following first assessment of those receiving care in a participating pilot unit vs those receiving care in any other unit in England, adjusting for age, sex, ethnicity, deprivation, type and duration of diabetes, ulcer severity, and morbidity in the year prior to first assessment. RESULTS: Of the 3110 people recorded in the NDFA at a participating unit during the pilot, 33% (1015) were recorded as having received an ECG. A further 25,195 people recorded in the NDFA had attended another English footcare service. Unadjusted mortality in the pilot units was 16.3% (165) at 2 years and 37.4% (380) at 5 years for those who received an ECG, and 20.5% (430) and 45.2% (950), respectively, for those who did not receive an ECG. For people included in the NDFA at other units, unadjusted mortality was 20.1% (5075) and 42.6% (10,745), respectively. In the fully adjusted model, mortality was not significantly lower for those attending participating units at 2 (RR 0.93 [95% CI 0.85, 1.01]) or 5 years (RR 0.95 [95% CI 0.90, 1.01]). At participating units, mortality in those who received an ECG vs those who did not was lower at 5 years (RR 0.86 [95% CI 0.76, 0.97]), but not at 2 years (RR 0.87 [95% CI 0.72, 1.04]). Comparing just those that received an ECG with attendees at all other centres in England, mortality was lower at 5 years (RR 0.87 [95% CI 0.78, 0.96]), but not at 2 years (RR 0.86 [95% CI 0.74, 1.01]). CONCLUSIONS/INTERPRETATION: The evaluation confirms the high mortality seen in those presenting with diabetic foot ulceration. Overall mortality at the participating units was not significantly reduced at 2 or 5 years, with confidence intervals just crossing parity. Implementation of the 12-lead ECG into the routine care pathway proved challenging for clinical teams-overall a third of attendees had one, although some units delivered the intervention to over 60% of attendees-and the evaluation was therefore underpowered. Nonetheless, the signals of potential mortality benefit among those who had an ECG suggest that units in a position to operationalise implementation may wish to consider this. DATA AVAILABILITY: Data from the National Diabetes Audit can be requested through the National Health Service Digital Data Access Request Service process at: https://digital.nhs.uk/services/data-access-request-service-dars/dars-products-and-services/data-set-catalogue/national-diabetes-audit-nda.

Wearable 12-Lead ECG Acquisition Using a Novel Deep Learning Approach from Frank or EASI Leads with Clinical Validation.

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions.

Fully Screen-Printed and Gentle-to-Skin Wet ECG Electrodes with Compact Wireless Readout for Cardiac Diagnosis and Remote Monitoring.

Recent advances in electrocardiogram (ECG) diagnosis and monitoring have triggered a demand for smart and wearable ECG electrodes and readout systems. Here, we report the development of a fully screen-printed gentle-to-skin wet ECG electrode integrated with a scaled-down printed circuit board (PCB) packaged inside a 3D-printed antenna-on-package (AoP). All three components of the wet ECG electrode (i.e., silver nanowire-based conductive part, electrode gel, and adhesive gel) are screen-printed on a flexible plastic substrate and only require 265 times less metal for the conductive part and 176 times less ECG electrode gel than the standard commercial wet ECG electrodes. In addition, our electrically small AoP achieved a maximum read range of 142 m and offers a 4 times larger wireless communication range than the typical commercial chip antenna. The adult volunteers' study results indicated that our system recorded ECG data that correlated well with data from a commercial ECG system and electrodes. Furthermore, in the context of a 12-lead ECG diagnostic system, the fully printed wet ECG electrodes demonstrated a performance similar to that of commercially available wet ECG electrodes while being gentle on the skin. This was confirmed through a blind review method by two cardiology consultants and one family medicine consultant, validating the consistency of the diagnostic information obtained from both electrodes. In conclusion, these findings highlight the potential of fully screen-printed wet ECG electrodes for both monitoring and diagnostic purposes. These electrodes could serve as potential candidates for clinical practice, and the screen-printing method has the capability to facilitate industrial mass production.

Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline information.

BACKGROUND: Atrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment model for AF gradation. METHODS: In total, 189 12-lead electrocardiogram (ECG) recordings from 64 patients were included in this study. The data were annotated into two groups (mild and severe) according to the 4S-AF scheme. Using a preprocessed ECG during the sinus rhythm (SR), we obtained a synthesized vectorcardiogram (VCG). Subsequently, various features were calculated from both signals, and age, sex, and medical history were included as baseline characteristics. Different machine learning models, including support vector machines, random forests (RF), and logistic regression, were finally tested with a combination of feature selection techniques. RESULTS: The proposed method demonstrated excellent performance in the classification of AF gradation. With an optimized feature set of VCG and baseline features, the RF model achieved accuracy, sensitivity, and specificity of 83.02 %, 80.56 %, and 88.24 %, respectively, under the inter-patient paradigm. CONCLUSION: Our results demonstrate the value of physiological signals in AF gradation evaluation, and VCG signals were effective in identifying mild and severe AF. Considering its low computational complexity and high assessment performance, the proposed model is expected to serve as a useful prognostic tool for clinical AF management.

A Simple Score for Predicting Paroxysmal Atrial Fibrillation in Patients with Embolic Stroke of Undetermined Source in a Tunisian Cohort Study.

BACKGROUND: The annualized recurrent stroke rate in patients with Embolic Stroke of Undetermined Source (ESUS) under antiplatelet therapy is around 4.5%. Only a fraction of these patients will develop atrial fibrillation (FA), to which a stroke can be attributed retrospectively. The challenge is to identify patients at risk of occult AF during follow-up. OBJECTIVE: This work aims to determine clinical factors and electrocardiographic and ultrasound parameters that can predict occult AF in patients with ESUS and build a simple predictive score applicable worldwide. METHODS: This is a single-center, registry-based retrospective study conducted at the stroke unit of Sahloul University Hospital, Sousse, Tunisia, between January 2016 and December 2020. Consecutive patients meeting ESUS criteria were monitored for a minimum of one year, with a standardized follow-up consisting of outpatient visits, including ECG every three months and a new 24-hour Holter monitoring in case of palpitations. We performed multivariate stepwise regression to identify predictors of new paroxysmal AF among initial clinical, electrocardiographic (ECG and 24-hour Holter monitoring) and echocardiographic parameters. The coefficient of each independent covariate of the fitted multivariable model was used to generate an integerbased point-scoring system. RESULTS: Three hundred patients met the criteria for ESUS. Among them, 42 (14%) patients showed at least one episode of paroxysmal AF during a median follow-up of two years. In univariate analysis, age, gender, coronary artery disease, history of ischemic stroke, higher NIHSS at admission and lower NIHSS at discharge, abnormal P-wave axis, prolonged P-wave duration, premature atrial contractions (PAC) frequency of more than 500/24 hours, and left atrial (LA) mean area of more than 20 cm2 were associated with the risk of occurrence of paroxysmal AF. We proposed an AF predictive score based on (1.771 x NIHSS score at admission) + (10.015 x P-wave dispersion; coded 1 if yes and 0 if no) + (9.841x PAC class; coded 1 if ≥500 and 0 if no) + (9.828x LA class surface; coded 1 if ≥20 and 0 if no) + (0.548xNIHSS score at discharge) + 0.004. A score of ≥33 had a sensitivity of 76% and a specificity of 93%. CONCLUSION: In this cohort of patients with ESUS, NIHSS at both admission and discharge, Pwave dispersion, PAC≥500/24h on a 24-hour Holter monitoring, and LA surface area≥20 cm2 provide a simple AF predictive score with very reasonable sensitivity and specificity and is applicable almost worldwide. An external validation of this score is ongoing.

Can DALL-E 3 Reliably Generate 12-Lead ECGs and Teaching Illustrations?

The recent integration of the latest image generation model DALL-E 3 into ChatGPT allows text prompts to easily generate the corresponding images, enabling multimodal output from ChatGPT. We explored the feasibility of DALL-E 3 for drawing a 12-lead ECG and found that it can draw rudimentary 12-lead electrocardiograms (ECG) displaying some of the parameters, although the details are not completely accurate. We also explored DALL-E 3's capacity to create vivid illustrations for teaching resuscitation-related medical knowledge. DALL-E 3 produced accurate CPR illustrations emphasizing proper hand placement and technique. For ECG principles, it produced creative heart-shaped waveforms tying ECGs to the heart. With further training, DALL-E 3 shows promise to expand easy-to-understand visual medical teaching materials and ECG simulations for different disease states. In conclusion, DALL-E 3 has the potential to generate realistic 12-lead ECGs and teaching schematics, but expert validation is still needed.

Novel application of convolutional neural networks for artificial intelligence-enabled modified moving average analysis of P-, R-, and T-wave alternans for detection of risk for atrial and ventricular arrhythmias.

BACKGROUND: T-wave alternans (TWA) analysis was shown in >14,000 individuals studied worldwide over the past two decades to be a useful tool to assess risk for cardiovascular mortality and sudden arrhythmic death. TWA analysis by the modified moving average (MMA) method is FDA-cleared and CMS-reimbursed (CAG-00293R2). OBJECTIVE: Because the MMA technique is inherently suitable for dynamic tracking of alternans levels, it was selected for development of artificial intelligence (AI)-enabled algorithms using convolutional neural networks (CNN) to achieve rapid, efficient, and accurate assessment of P-wave alternans (PWA), R-wave alternans (RWA), and TWA. METHODS: The novel application of CNN algorithms to enhance MMA analysis generated efficient and powerful pattern-recognition algorithms for highly accurate alternans quantification. Algorithm reliability and accuracy were verified using simulated ECGs achieving R2 ≥ 0.99 (p < 0.01) in response to noise inputs and artifacts that emulate real-life conditions. RESULTS: Accuracy of the new AI-MMA algorithms in TWA analysis (n = 5) was significantly improved over unsupervised, automated MMA output (p = 0.036) and did not differ from conventional MMA analysis with expert overreading (p = 0.21). Accuracy of AI-MMA in PWA analysis (n = 45) was significantly improved over unsupervised, automated MMA output (p < 0.005) and did not differ from conventional MMA analysis with expert overreading (p = 0.89). TWA and PWA by AI-MMA were correlated with conventional MMA output over-read by an expert reader (R2 = 0.7765, R2 = 0.9504, respectively). CONCLUSION: This novel technique for AI-MMA analysis could be suitable for use in diverse in-hospital and out-of-hospital monitoring systems, including cardiac implantable electronic devices and smartwatches, for tracking atrial and ventricular arrhythmia risk.

A "two-step classification" machine learning method for non-invasive localization of premature ventricular contraction origins based on 12-lead ECG.

BACKGROUND: Premature ventricular contraction (PVC) is a type of cardiac arrhythmia that originates from ectopic pacemaker in the ventricles. The localization of the origin of PVC is essential for successful catheter ablation. However, most studies on non-invasive PVC localization focus on elaborate localization in specific regions of the ventricle. This study aims to propose a machine learning algorithm based on 12-lead electrocardiogram (ECG) data that can improve the accuracy of PVC localization in the whole ventricle. METHODS: We collected 12-lead ECG data from 249 patients with spontaneous or pacing-induced PVCs. The ventricle was divided into 11 segments. In this paper, we propose a machine learning method consisting of two consecutive classification steps. In the first classification step, each PVC beat was labeled to one of the 11 ventricular segments using six features, including a newly proposed morphological feature called "Peak_index." Four machine learning methods were tested for comparative multi-classification performance and the best classifier result was kept to the next step. In the second classification step, a binary classifier was trained using a smaller combination of features to further differentiate segments that are easily confused. RESULTS: The Peak_index as a proposed new classification feature combined with other features is suitable for whole ventricle classification by machine learning methods. The test accuracy of the first classification reached 75.87%. It is shown that a second classification for confusable categories can improve the classification results. After the second classification, the test accuracy reached 76.84%, and when a sample classified into adjacent segments was considered correct, the test "rank accuracy" was improved to 93.49%. The binary classification corrected 10% of the confused samples. CONCLUSION: This paper proposes a "two-step classification" method to localize the origin of PVC beats into the 11 regions of the ventricle using non-invasive 12-lead ECG. It is expected to be a promising technique to be used in clinical settings to help guide ablation procedures.

DAMS-Net: Dual attention and multi-scale information fusion network for 12-lead ECG classification.

Automated 12-lead electrocardiographic (ECG) classification algorithms play an important role in the diagnosis of clinical arrhythmias. Current methods that perform well in the field of automatic ECG classification are usually based on Convolutional Neural Networks (CNN) or Transformer. However, due to the intrinsic locality of convolution operations, CNN can't extract long-dependence between series. On the other side, the Transformer design includes a built-in global self-attention mechanism, but it doesn't pay enough attention to local features. In this paper, we propose DAMS-Net, which combines the advantages of Transformer and CNN, introducing a spatial attention module and a channel attention module using a CNN-Transformer hybrid encoder to adaptively focus on the significant features of global and local parts between space and channels. In addition, our proposal fuses multi-scale information to capture high and low-level semantic information by skip-connections. We evaluate our method on the 2018 Physiological Electrical Signaling Challenge dataset, and our proposal achieves a precision rate of 83.6%, a recall rate of 84.7%, and an F1-score of 0.839. The classification performance is superior to all current single-model methods evaluated in this dataset. The experimental results demonstrate the promising application of our proposed method in 12-lead ECG automatic classification tasks.

A scalogram tensor decomposition based ECG quality assessment.

ECG quality assessment is crucial for reducing false alarms and physician strain in automated diagnosis of cardiovascular diseases. Recent researches have focused on constructing an automatic noisy ECG record rejection mechanism. This work develops a noisy ECG record rejection system using scalogram and Tucker tensor decomposition. The system can reject ECG records, which cannot be analyzed or diagnosed. Scalogram of all 12­lead ECG signals per subject are stacked to form a 3-way tensor. Tucker tensor decomposition is applied with empirical settings to obtain the core tensor. The core tensor is reshaped to form the latent features set. When tested using the PhysioNet challenge 2011 dataset in five-fold cross validation settings, the RusBoost ensemble classifier proved to be a very reliable option, producing an accuracy of 92.4% along with sensitivity of 87.1% and specificity of 93.5%. According to the experimental findings, combining the scalogram with Tucker tensor decomposition yields competitive performance and has the potential to be used in actual evaluation of ECG quality.

Design and technical validation to generate a synthetic 12-lead electrocardiogram dataset to promote artificial intelligence research.

Purpose: The purpose of this study is to construct a synthetic dataset of ECG signal that overcomes the sensitivity of personal information and the complexity of disclosure policies. Methods: The public dataset was constructed by generating synthetic data based on the deep learning model using a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM), and the effectiveness of the dataset was verified by developing classification models for ECG diagnoses. Results: The synthetic 12-lead ECG dataset generated consists of a total of 6000 ECGs, with normal and 5 abnormal groups. The synthetic ECG signal has a waveform pattern similar to the original ECG signal, the average RMSE between the two signals is 0.042 µV, and the average cosine similarity is 0.993. In addition, five classification models were developed to verify the effect of the synthetic dataset and showed performance similar to that of the model made with the actual dataset. In particular, even when the real dataset was applied as a test set to the classification model trained with the synthetic dataset, the classification performance of all models showed high accuracy (average accuracy 93.41%). Conclusion: The synthetic 12-lead ECG dataset was confirmed to perform similarly to the real-world 12-lead ECG in the classification model. This implies that a synthetic dataset can perform similarly to a real dataset in clinical research using AI. The synthetic dataset generation process in this study provides a way to overcome the medical data disclosure challenges constrained by privacy rights, a way to encourage open data policies, and contribute significantly to promoting cardiovascular disease research.

A Prospective Observational Study to Evaluate Cardiovascular Changes in Patients of Hypothyroidism.

Introduction Hypothyroidism is a common endocrine disorder in India and is easy to diagnose based on clinical manifestations and signs. Thyroid hormone affects the cardiovascular system. Fatiguability, dyspnea, weight gain, lower limb swelling, and bradycardia are some clinical manifestations. ECG changes in hypothyroidism include sinus bradycardia, prolonged QTc interval, changes in the morphology of the T-wave, QRS duration, and low voltage. Echocardiography changes include diastolic dysfunction, asymmetrical septal hypertrophy, and pericardial effusion. This study aimed to examine the cardiovascular changes in patients with hypothyroidism. Methodology Patients with hypothyroidism and cardiovascular changes were assessed using an electrocardiogram and echocardiography. Results A total of 68 hypothyroid patients were enrolled in the study. The mean age of patients was 41.93 ± 15.36 years, and the mean BMI was 24.64 ± 4.30 kg/m2. Of 68 hypothyroid patients, 57 (83.8%) were females, and 11 (16.2%) were males. The mean thyroid-stimulating hormone (TSH) level in the study population was 11.48 ± 22.02 (mIU/mL). The most common symptoms reported among the study participants were tiredness or weakness (67.6%), followed by dyspnea (42.6%). The mean pulse rate, systolic blood pressure, and diastolic blood pressure were 81.50 ± 16.16, 112.76 ± 7.05, and 70.68 ± 7.46, respectively. Pallor was the most common sign (22.1%) among all the people who participated in the study. The most common findings on the ECG were low voltage complexes (25%) followed by inversion of the T wave (23.5%). Other ECG findings were bradycardia (10.3%), right bundle branch block (7.4%), and QRS prolongation (2.9%). Echocardiography revealed 21 (30.8%) patients with grade 1 left ventricular diastolic dysfunction and pericardial effusion in two patients (2.94%). There was a significantly greater increase in the level of TSH in study participants. Conclusion Patients with abnormal ECG and echocardiography without other cardiovascular changes should be evaluated for hypothyroidism to improve the quality of care.

Are Electrocardiographic Criteria Reliable for Left Ventricular Hypertrophy Detection in Indian Adults?

BACKGROUND: Left ventricular hypertrophy (LVH) detection is vital to the risk stratification of adults at risk of adverse cardiovascular events such as coronary heart disease, cerebrovascular disease, and aortic aneurysms. Electrocardiogram (ECG), a non-invasive, cost-effective instrument has been widely used as a screening tool for LVH. The objective of this study was to determine the diagnostic accuracy of seven frequently used ECG criteria in high-risk Indian adults in comparison with echocardiography. METHODS: ECG and transthoracic echocardiography were performed in adults older than 18 years with at least one cardiac risk factor (chronic hypertension, obesity, ischemic heart disease, and type 2 diabetes mellitus). Precision and accuracy were calculated for the various ECG criteria against LVH based on left ventricular mass index (LVMI) and cardiac remodeling by echocardiography. RESULTS: A total of 220 participants were enrolled. Of these, 96 had LVH by echocardiography. There was marked variability in LVH detection by the different ECG criteria: 28 by Sokolow-Lyon criteria, 26 by Cornell criteria, 24 by Lewis criteria, 46 by Scott criteria, eight by Romhilt-Estes criteria, six by Modified Cornell criteria, and only two by Roberts criteria. Agreement statistics between ECG criteria and LVMI showed that none of them had a good agreement for LVH detection. CONCLUSION: None of the ECG criteria were sensitive enough to rule out ventricular hypertrophy. In the context of cardiac remodeling, the ECG criteria had high sensitivity but low specificity and, hence, limited clinical relevance.

Prognostic Significance of Electrocardiography, Echocardiography, and Troponin in Patients Admitted With Non-ST Elevation Myocardial Infarction.

BACKGROUND: Non-ST segment elevation myocardial infarction (NSTEMI) is a clinical condition characterized by typical symptoms of myocardial ischemia along with electrocardiographic changes and a positive value of troponin. After presentation in the emergency department, these patients have their troponin I value and electrocardiography done. Echocardiography (echo) should also be performed on these patients. This study was conducted to determine the prognostic significance of ECG, echo, and troponin. METHODS: This observational study was conducted at a tertiary care cardiac hospital on 221 diagnosed patients of NSTEMI. Electrocardiography was performed to see any particular resting ECG findings and the peak values of cardiospecific troponin were analyzed for associations with major adverse events after a six-month period of follow-up. On echo, the left ventricular ejection fraction was divided into two categories: left ventricular ejection fraction (LVEF) <40% and LVEF >40%. RESULTS:  The most frequent finding on presenting ECG was ST depression in anterior leads (V1-V6) in 27.6%. Median troponin I at presentation was 3.2 ng/dl and the median ejection fraction was 45%. The overall all-cause mortality rate at six months was observed to be 8.6%; re-infarction in 5%, re-hospitalization in 16.3%, and heart failure in 25.3% were observed. However, mortality was higher for patients with baseline ECG findings of A-fib, generalized ST-depression, poor R-wave progression, Wellens sign, and T-wave inversion in inferior; the mortality rate was also relatively higher among patients with poor LVEF (<30%). CONCLUSION: ECG and echo were prognostically significant and with the combined incidence of adverse events. However, troponin lacks prognostic significance at six months.

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network.

Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, F1-score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.

12-lead ECG signal processing and atrial fibrillation prediction in clinical practice.

BACKGROUND: Because clinically used 12-lead electrocardiography (ECG) devices have high falsepositive errors in automatic interpretations of atrial fibrillation (AF), they require substantial improvements before use. OBJECTIVE: A clinical 12-lead ECG pre-processing method with a parallel convolutional neural network (CNN) model for 12-lead ECG automatic AF recognition is introduced. METHODS: Raw AF diagnosis data from a 12-lead ECG device were collected and analyzed by two cardiologists to differentiate between true- and false-positives. Using a stationary wavelet transform (SWT) and independent component analysis (ICA) noise reduction was conducted and baseline wandering was corrected for the raw signals. AF patterns were learned and predicted using a parallel CNN deep learning (DL) model. (1) The proposed method alleviates the decreased ECG QRS amplitude enhances the signal-to-noise ratio and clearly shows atrial and ventricular activities. (2) After training, the CNNbased AF detector significantly reduced false-positive errors. The precision of AF diagnosis increased from 77.3% to 94.0 ± 1.5% as compared to ECG device interpretation. For AF screening, the model showed an average sensitivity of 96.8 ± 2.2%, specificity of 79.0 ± 5.8%, precision of 94.0 ± 1.5%, F1-measure of 95.2 ± 1.0%, and overall accuracy of 92.7 ± 1.5%. CONCLUSIONS: The method can bridge the gap between the research and clinical practice The ECG signal pre-processing and DL-based AF interpretation can be rapidly implemented clinically.

Anatomical considerations and clinical interpretation of the 12-lead ECG in the prone position: a prospective multicentre study.

AIMS: The aim of this study is to provide guidance for the clinical interpretation of electrocardiograms (ECGs) in prone position and to establish the electroanatomic explanations for the possible differences to supine position ECGs that may be observed. Additionally, to determine if prone back ECG can be used as an alternative to standard ECG in patients who may benefit from prone position. METHODS AND RESULTS: The ECG in supine (standard ECG), prone back (precordial leads placed on the patient's back), and prone anterior position (precordial leads placed in the standard position with the subjects in prone position) were prospectively examined on 85 subjects. Comparisons of ECG parameters between these positions were performed. Computed tomography (CT) scans were performed in both positions to determine possible electroanatomic aetiologies for prone-associated ECG changes. There were significant differences in QRS amplitude in Leads V1-V5 between supine and prone positions. Q waves were more frequently observed in prone back position vs. supine position (V1: 74.1 vs. 10.6%, P < 0.0001; V2: 23.5 vs. 0%, P < 0.0001, respectively). Flat and inverted T waves were more common in prone back leads (V1: 98 vs. 66%, P < 0.0001; V2: 96 vs. 8%, P < 0.0001; V3: 45 vs. 7%, P < 0.0001). The 3D-CT reconstructions measurements corroborated the significant inverse correlation between QRS amplitude and the distance from the centre of the heart to the estimated lead positions. CONCLUSION: In prone back position ECG, low QRS amplitude should not be misinterpreted as low voltage conditions, neither should Q waves and abnormal T waves are considered anteroseptal myocardial infarction. These changes can be explained by an increased impedance (due to interposing lung tissue) and by the increased distance between the electrodes to the centre of the heart.

A Young Male Presenting With Chest Pain, Elevated Troponin Levels, and a Clinical Dilemma: A Case Report.

Chest pain is a common presentation that may represent a wide variety of underlying etiologies ranging from mild self-limiting conditions to immediately life-threatening emergencies. The combination of "cardiac-sounding chest pain" and elevated troponin levels would raise suspicion of an acute ischemic event. An acute coronary syndrome is a diagnosis that may be straightforward; however, oftentimes, patients with elevated troponin levels and chest pain may bring about a state of diagnostic uncertainty. Alternative diagnoses to consider would be inflammatory or infectious conditions of the myocardium and pericardium. We present the case of a young gentleman in his twenties who presents with cardiac chest pain, elevated troponin, and non-specific changes on his electrocardiogram who was treated for an alternative cause of elevated troponin and chest pain, myopericarditis. We present the case of a 24-year-old male who presented with a six-hour history of debilitating retrosternal chest pain. Initial workup showed a Troponin I level greater than 15,000 ng/L, D-Dimer greater than 1,000 mcg/L with no overt ischemic features on electrocardiogram. The patient had no high-risk features in his medical history & denied the use of recreational drugs. A formal same-day echocardiogram revealed normal biventricular systolic function and no evidence of regional wall motion abnormality (RWMA). He was eventually treated clinically for myopericarditis. A Cardiac MRI (CMR) imaging was done to confirm the diagnosis and rule out, most importantly, ischemic heart disease or any other underlying pathology. The main dilemma in this case was working out whether there was indeed peri-myocardial inflammation, or an acute coronary event (such as spontaneous coronary artery dissection) given his age and clinical history. Patients presenting with a very high troponin level, particularly in young patient cohorts, should raise suspicion of a myocardial or pericardial inflammatory process. In addition to a thorough history and in the absence of ischemic changes on the electrocardiogram, subtle findings such as PR segment depression may point to a diagnosis of pericardial inflammation. While urgent echocardiography is useful to quickly assess ventricular function and for RWMA, CMR imaging is the Gold Standard modality of investigation to provide detailed structural information of the heart.