Sinus Rhythm Electrocardiogram Research Articles

Predicting atrial fibrillation recurrence after catheter ablation using an artificial intelligence-enabled electrocardiogram algorithm

Abstract Introduction Predicting atrial fibrillation (AF) recurrence post catheter ablation may help to assess procedural eligibility and determine AF management. While several predictors of AF recurrence post ablation have been established and numerous clinical risk scores have been proposed, their performance remained underwhelming and clinical utility was limited. Hence, new predictors are needed. Artificial intelligence (AI) algorithms using deep neural networks (DNN) to analyze biometrical data might generate such predictors. DNN algorithms have been developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether these algorithms can function as a predictor of AF recurrence after AF ablation remains unknown. Purpose To evaluate the prediction of AF recurrence after ablation using an AI-enabled ECG algorithm trained to predict AF on an ECG in sinus rhythm. Methods This study retrospectively analyzed observational data from the DIGITOTAL study, that monitored AF recurrence with a PPG-based smartphone application in 96 subjects after AF ablation. Patients with a 12-lead ECG in SR available within a timeframe of 3 months before the ablation procedure were included in the analysis. Although all patients had a history of AF, an AF-risk score was calculated by the DNN described elsewhere.1 Results An ECG in sinus rhythm was available in 53 patients (14 women [26.4%]; mean [SD] age, 62.0 [9.7] years) out of the 96 patients followed-up in the DIGITOTAL study. Testing the DNN on the last ECG before the ablation procedure resulted in an area under the receiver operating curve (AUC) of 0.65 (95% CI, 0.49 - 0.80), and an area under the precision recall curve (AUPRC) of 0.56 (95% CI, 0.34 - 0.78). The optimal cutoff score resulted in a sensitivity of 60.0% (95% CI, 36.1% - 80.9%), specificity of 66.7% 66.7% (95% CI, 48.2% - 82.0%), accuracy of 0.64 (95% CI, 0.50 - 0.77), F1-score of 55.8% (95% CI, 38.5% - 74.4%), positive predictive value 52.2% (95% CI, 30.6% - 73.2%) and negative predictive value 73.3% (95% CI, 54.1% - 87.7%). Patients classified in the high-risk group versus low-risk group were more likely to exhibit AF recurrence up to one year after AF ablation (hazard ratio, 2.6; 95% CI, 1.1 - 6.5; P-value = 0.037). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a sinus rhythm ECG, was able to predict AF recurrence after ablation with an accuracy comparable to the existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.

Predicting post-operative atrial fibrillation after cardiac surgery using an artificial intelligence-enabled electrocardiogram algorithm

Abstract Introduction Postoperative atrial fibrillation (POAF) is common after cardiac surgery and is associated with adverse outcomes. Systematic monitoring of POAF is cumbersome, specifically beyond discharge. Therefore, risk stratification may aid to identify patients at high risk of POAF and guide monitoring strategies alongside preventive measures. However, the performance of bedside risk stratification models reliant on clinical risk factors remained underwhelming, necessitating the exploration of more sophisticated models that maintain clinical applicability. Hence, artificial intelligence algorithms (AI) have been suggested to reinforce or replace clinical risk scores. As such, a deep neural network (DNN) algorithm was developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether this algorithm can identify patients at high risk of POAF remains unknown. Purpose To evaluate the usability of an AI-enabled ECG algorithm, that was trained to predict AF in non-surgical conditions, for the prediction of POAF. Methods This study retrospectively analyzed data from the SURGICAL-AF trial that monitored patients after cardiac surgery. The inclusion criteria for this subanalysis comprised: (1) patients without a history of AF prior to cardiac surgery; (2) availability of the raw data of a pre-operative 12-lead ECG in sinus rhythm; and (3) patients with POAF (during hospitalization or up to 91 days after discharge) or patients having completed PPG-based rhythm monitoring per protocol. The AF-risk score was calculated by the DNN described elsewere.1 Results In total, 127 patients (mean [SD] age, 63.4 [8.4] years; 30 women [23.6%];, median [interquartile range] CHA2DS2-VASc score, 2 [1-3]) complied with the inclusion criteria, out of the 450 patients randomized in the SURGICAL-AF trial. Testing the DNN on the last ECG before cardiac surgery resulted in an area under the receiver operating curve (AUC) of 0.66 (95% CI, 0.56 - 0.77) and an area under the precision-recall curve of 0.57 (95% CI, 0.42 -0.72). The optimal cut of score resulted in a sensitivity of 64.3% (95% CI, 48.0%-78.4%), specificity of 64.7% (95% CI, 53.6%-74.8%), accuracy of 0.65 (95% CI, 0.56 - 0.73), F1-score of 54.5% (95% CI, 42.9% - 66.8%), positive predictive value of 47.4% (95% CI, 34.0%-61.0%), and negative predictive value of 78.6% (95% CI, 67.1%-87.5%). POAF occurred within three months after cardiac surgery in 23 patients out of 57 patients classified in the high-risk group (40.4%) versus 15 patients (21.4%) out of 70 patients classified in the low-risk group (hazard ratio, 2.2; 95% CI, 1.2 – 4.3; P-value = 0.020). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a pre-operative sinus rhythm ECG, was able to identify POAF with an accuracy comparable to existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.

Artificial intelligence-estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablations

Abstract Aims The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. Methods We validated a pre-trained residual network (ResNet)-based model for age prediction on four independent multinational datasets: CODE-15%, PTB-XL, UK Biobank, and SaMi-Trop cohorts. Then, we evaluated AI-estimated ECG (AI-ECG) age for AF recurrence in a single-center AF catheter ablation (AFCA) cohort, which included 4,794 patients with de-novo AFCA and pre-procedural sinus rhythm ECGs. We categorized the AI-ECG age gap into two groups: aged-ECG (>13 year) and normal age (≤13 year) groups based on the maximum log-likelihood for AF recurrence according to the difference between chronological age and AI-ECG age. Results The ResNet-based AI-ECG age model successfully reproduced the chronological age on the independent datasets (total ECG number=414,804): CODE-15% (r=0.83), PTB-XL (r=0.74), UK Biobank (r=0.53), and SaMi-Trop (r=0.60). During median 22 (9-47) months after AFCA, patients with aged-ECG had a significantly higher AF recurrence rate (adjusted hazard ratio 1.31, 95% confidence interval [1.17-1.48], p<0.001) than normal age group. In the sub-group analyses, the aged-ECG affected more in patients with paroxysmal AF than in non-paroxysmal AF (P for interactions <0.001) and after cryoballoon pulmonary vein isolation (Cryo-PVI) than radiofrequency PVI (P for interactions <0.001). Conclusions Pre-procedural AI- ECG age has a prognostic value for AF recurrence after AF catheter ablation.

AN UNUSUAL LEFT VENTRICULAR APICAL MASS COMPLICATING ACUTE MYOCARDIAL INFARCTION

Abstract An 34–year–old man presented to the emergency department of our hospital with palpitations and presyncope. He was a smoker but had no others known cardiovascular risk factors. At admission, his electrocardiogram (ECG) showed sustained monomorphic ventricular tachycardia (VT) at a rate of 187 beats per minute. The VT was successfully treated with synchronized direct current cardioversion. The ECG in sinus rhythm showed Qs from V1 to V6 with ventricular repolarization anomalies in the same site suspicious for previous anterior necrosis. A transthoracic echocardiogram revealed akinetic apex, akinesia of middle segments of the interventricular septum, anterior and lateral walls, Left Ventricular (LV) ejection fraction of 30% and a large apical mass suspicious for LV thrombus. The patient was admitted to the coronary care unit and taken to the Cath lab that showed total left anterior descending mid–segment occlusion. Due to the chronicity of the condition, the patient was recommended to optimal medical therapy. Suspecting LV thrombus, the patient began treatment with unfractionated heparin i.v. and subsequently imbricate therapy with enoxaparin s.c. and warfarin. Additionally, the patient underwent subcutaneous implantable cardioverter defibrillator (S–ICD) implantation for secondary prevention of sudden cardiac death. At echocardiographic follow–up one week after discharge, the apical mass remained unchanged despite warfarin therapy with INR at target. For this reason, the patient was recommended to perform a Cardiovascular magnetic resonance (CMR). Three months after S–ICD implantation, a CMR was performed which showed transmural areas of late gadolinium enhancement involving all apical segments and middle segments of the interventricular septum which allowed the diagnosis of a large intramyocardial dissecting hematoma (IDH) of left ventricle. IDH is a rare complication of ST–elevation myocardial infarction (STEMI), it is an unusual form of subacute cardiac rupture and could be fatal. It usually consists of a cavity or channels within the myocardium. Its prognosis varies according to its location, but generally apical IDH have more favorable outcome with a higher likelihood of spontaneous retraction. We report this case because the IDH is a diagnostic challenge and it represents an extremely rare and poorly described event in the literature.

Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms

BackgroundArtificial intelligence (AI)–enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS). ObjectiveThe purpose of this study was to assess the efficacy of an AI model in identifying AF based on SR ECGs in patients with ESUS. MethodsA transformer-based vision AI model was developed using 737,815 SR ECGs from patients with and without AF to detect current paroxysmal AF or predict the future development of AF within a 2-year period. Probability of AF was calculated from baseline SR ECGs using this algorithm. Its diagnostic performance was further tested in a cohort of 352 ESUS patients from 4 tertiary hospitals, all of whom were monitored using an insertable cardiac monitor (ICM) for AF surveillance. ResultsOver 25.1-month follow-up, AF episodes lasting ≥1 hour were identified in 58 patients (14.4%) using ICMs. In the receiver operating curve (ROC) analysis, the area under the curve for the AI algorithm to identify AF ≥1 hour was 0.806, which improved to 0.880 after integrating the clinical parameters into the model. The AI algorithm exhibited greater accuracy in identifying longer AF episodes (ROC for AF ≥12 hours: 0.837, for AF ≥24 hours: 0.879) and a temporal trend indicating that the AI-based AF risk score increased as the ECG recording approached the AF onset (P for trend <.0001). ConclusionsOur AI model demonstrated excellent diagnostic performance in predicting AF in patients with ESUS, potentially enhancing patient prognosis through timely intervention and secondary prevention of ischemic stroke in ESUS cohorts.

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

BackgroundAtrial 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. MethodsIn 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. ResultsThe 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. ConclusionOur 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.

Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm.

Guidelines recommend opportunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring. The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF. This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex. An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model. An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups.

Prediction of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms Based on Deep Neural Networks: Analysis of Time Intervals and Longitudinal Study

ObjectiveArtificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures. MethodsWe designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics. ResultsAfter balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09). ConclusionThis study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stability.

Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods.

This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: true-normal sinus rhythm (T-NSR), atrial fibrillation-normal sinus rhythm (AF-NSR), and clinically important arrhythmia-normal sinus rhythm (CIA-NSR). The T-NSR group comprised patients with at least three normal rhythms in a year and no atrial fibrillation or arrhythmias history. Clinically important arrhythmia included atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, ventricular premature contraction, ventricular tachycardia, right and left bundle branch block, and atrioventricular block over the second degree. The AF-NSR group included normal sinus rhythm paired with atrial fibrillation or atrial flutter within 14 days, and the CIA-NSR group comprised normal sinus rhythm paired with CIA occurring within 14 days. Three deep learning models, ResNet-18, LSTM, and Transformer-based models, were utilized to distinguish T-NSR from AF-NSR and T-NSR from CIA-NSR. The experiments demonstrated the potential of using discrete heartbeats in predicting future arrhythmia and atrial fibrillation incidences extracted from 12-lead electrocardiogram (ECG) signals alone, without any additional patient information. The analysis reveals that these discrete heartbeats contain subtle patterns that deep learning models can identify. Focusing on discrete heartbeats may lead to more timely and accurate diagnoses of these conditions, improving patient outcomes and enabling automated diagnosis using ECG signals as a biomarker.

Myocardial Fibrosis and Left Bundle Branch Block Complicating an Athlete's Heart Syndrome Treated Homeopathically: A Case Report

Abstract Background During 2013, a 59-year-old male patient, a medical practitioner and lifelong sportsperson, complained of being constantly conscious of his heart action, and left sternal heave, as well as retrosternal heaviness at rest. On October 23, 2013, an electrocardiogram (ECG) showed some signs of an athlete's heart syndrome. An ECG on 11 January, 2019, shockingly showed a complete left bundle branch block (LBBB), a broad complex QRS sinus rhythm ECG, without ever experiencing angina pectoris after doing four sets of 20 push-ups. Methods A complication of an athlete's heart may be myocardial fibrosis (MF). MF is the primary pathology to treat, as well as its comorbidities synergistically, when they obstruct progress. Thus, Calcarea fluorata (Calc-f) was prescribed for endocardial fibrosis, Iberis amara (Iber) for endomyocardial fibrosis, Cactus grandiflorus (Cact) for pulmonary arterial hypertension and right ventricular strain, and Crataegus laevigata (Crat) for subendocardial ischaemia. Results After 17 months on Calc-f, the LBBB was unchanged. Iber was again prescribed on February 13, 2022. On February 23, 2022, after 10 days on Iber, a narrow complex QRS sinus rhythm (SR) ECG was recorded with a QRS of 90 milliseconds. This was the first SR ECG after having had a permanent complete LBBB for at least 3 years. Conclusions This case report documents via ECGs the biological effectiveness of Calc-f and Iber for MF. SR returned after the employment of, respectively, Iber and Cact. After employing Crat, the abnormal inverted T waves in most leads became positive. Since October 16, 2022 and beyond, there is a stable SR, but any intense exertion causes an LBBB.

Deep convolutional neural network optimized with hybrid marine predator’s and nomadic people optimization for cardiac arrhythmia classification using ECG signals

Electrocardiogram (ECG) accounts are generally utilized for analyzing and figure cardiovascular arrhythmias for examining the heart ailment. In this situation, medical professionals may need to review the ECG records over a longer period of time to diagnose cardiac arrhythmias. Heart arrhythmia is a regular indication of cardiovascular arrhythmia. Several deep learning models have been suggested to classify the Cardiac Arrhythmia. But, the existing methods do not reach adequate efficiency and increase the computational time. To overcome these issues, deep convolutional neural network optimized with hybrid marine predators and nomadic people optimization for cardiac arrhythmia classification using electrocardiogram signals is proposed in this manuscript. The input data is pre-processed using anisotropic diffusion Kuwahara filtering (ADKF) for removing noise, error, blur and histogram noises. Then Term frequency-inverse document frequency (TF-IDF) based feature extraction is utilized for feature extraction. Afterward, the extracting features are given to the Weibull Distributive Generalized Multidimensional Scaling (WDGMS) feature selection for features selection. Thus, hybrid Marine Predator’s Algorithm (MPA) and Nomadic people optimizer (NPO) is employed for optimizing the DCNN weight parameters. The proposed Deep CNN-Hyb (MPA-NPO)-AC method is implemented in python. Thus the proposed Deep CNN-Hyb(MPA-NPO)-AC method attains 14.03 %, 22.30 % and 16.35 % lower error rate; 3.32 %, 4.27 %, 5.39 % and 2.05 % greater AUC compared with existing methods, like time-series augmented signals with deep learning (SVM-CA-ECG), Linearly Adaptive Sine–Cosine Algorithm along application in Deep Neural Network for Feature Optimization in Arrhythmia Categorization utilizing ECG Signals (DNN-CA-ECG) and Categorization of normal sinus rhythm, abnormal arrhythmia and congestive heart fault ECG signals under LSTM with hybrid CNN-SVM deep neural network (CNN-CA-ECG) respectively.

The R″ wave in V1 and the negative terminal QRS vector in aVF combine to a novel 12-lead ECG algorithm to identify slow conducting anatomical isthmus 3 in patients with tetralogy of Fallot.

Patients with repaired tetralogy of Fallot (rTOF) have an increased risk of ventricular tachycardia (VT), with slow conducting anatomical isthmus (SCAI) 3 as dominant VT substrate. In patients with right bundle branch block (RBBB), SCAI 3 leads to local activation delay with a shift of terminal RV activation towards the lateral RV outflow tract which may be detected by terminal QRS vector changes on sinus rhythm electrocardiogram (ECG). Consecutive rTOF patients aged ≥16 years with RBBB who underwent electroanatomical mapping at our institution between 2017-2022 and 2010-2016 comprised the derivation and validation cohort, respectively. Forty-six patients were included in the derivation cohort (aged 40±15 years, QRS duration 165±23 ms). Among patients with SCAI 3 (n = 31, 67%), 17 (55%) had an R″ in V1, 18 (58%) had a negative terminal QRS portion (NTP) ≥80 ms in aVF, and 12 (39%) had both ECG characteristics, compared to only 1 (7%), 1 (7%), and 0 patient without SCAI, respectively.Combining R″ in V1 and/or NTP ≥80 ms in aVF into a diagnostic algorithm resulted in a sensitivity of 74% and specificity of 87% in detecting SCAI 3. The inter-observer agreement for the diagnostic algorithm was 0.875. In the validation cohort [n = 33, 18 (55%) with SCAI 3], the diagnostic algorithm had a sensitivity of 83% and specificity of 80% for identifying SCAI 3. A sinus rhythm ECG-based algorithm including R″ in V1 and/or NTP ≥80 ms in aVF can identify rTOF patients with a SCAI 3 and may contribute to non-invasive risk stratification for VT.

WPW syndrome: review of literature

Wolff-Parkinson-White (WPW) disorder is a disorder where there is additional accessory electrical pathway in the heart. Wolff-Parkinson-White syndrome (WPW) is the commonest form of ventricular pre-excitation. Wolff-Parkinson-White syndrome is not rare in the emergency department. Its early recognition and prompt treatment gives fast restoration to sinus rhythm. Early refer to cardiologist for electrophysiological study is necessary for early diagnosis and needed intervention. The patient with the Wolff-Parkinson-White (WPW) ECG design has much in the same way as patients with different conditions, like, long-QT disorder and Brugada disorder, because in sinus rhythm electrocardiogram (ECG) is different as compared to tachyarrthymic status of the same patient another differential with WPW syndrome will be LGL syndrome where delta wave will be absent

Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors

Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors

Deep learning-based electrocardiogram analysis detecting paroxysmal atrial fibrillation during sinus rhythm in patients with cryptogenic stroke: validation study using implantable cardiac monitoring

Abstract Background Atrial fibrillation (AF) is the most cause of cardioembolic source causing cryptogenic stroke. In these, anticoagulation therapy could reduce recurrence of stroke. However, paroxysmal AF would not be detected even by 24 hours Holter monitoring. Deep learning-based electrocardiogram (ECG) analysis models were recently developed to detect AF during sinus rhythm. Purpose We aimed to develop a deep learning algorithm (DLA) to detect AF during sinus rhythm and validate the model in patients with cryptogenic stroke who underwent implantable cardiac monitoring (ICM) to diagnose paroxysmal AF. Methods This cohort study involved three hospitals (A, B, and C). We developed a DLA to detect AF using sinus rhythm 10 s 12-lead ECG. We included adult patients aged ≥18 years from hospital A and B. We used development data from AF adult patients who had at least one atrial fibrillation rhythm in the study period (Jan 2016 to Dec 2021) and non-AF patients who had no reference to AF in the ECG and electronic medical record. DLA was based on convolutional neural network (CNN) using 10 s 12-lead. For external validation, the ECGs from 217 patients (hospital C) with cryptogenic stroke who underwent ICM were analyzed by using the DLA for validating the accuracy in the real-world clinical situations. Results We included 10,605 AF adult patients and 50,522 non-AF patients as development data. During the internal validation, the area under the curve (AUC) of the final DLA based on CNN was 0.793 (95% Confidence interval 0.778–0.807). In external validation data from cryptogenic stroke patients, the mean ICM duration was 15.1 months, and AF &amp;gt;5 mins was detected in 32 patients (14.5%). The diagnostic accuracy of DLA was 0.793 to detect AF during sinus rhythm, and AUC was 0.824. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 0.844, 0.784, 0.403, and 0.967, respectively, which outperformed other conventional predictive methods based on clinical factors, such as CHARGE-AF, C2hest, and HATCH. Conclusions In this study, DLA accurately detected paroxysmal AF using 12-leads normal sinus rhythm ECG in patients with cryptogenic stroke and outperformed the conventional models. The DLA could be used as a screening tool to identify the cause of stroke in the future. Funding Acknowledgement Type of funding sources: None.

Detection of cardiac arrhythmia patterns in ECG through H × C plane.

The aim of this study is to formulate a new methodology based upon informational tools to detect patients with cardiac arrhythmias. As it is known, sudden death is the consequence of a final arrhythmia, and here lies the relevance of the efforts aimed at the early detection of arrhythmias. The information content in the time series from an electrocardiogram (ECG) signal is conveyed in the form of a probability distribution function, to compute the permutation entropy proposed by Bandt and Pompe. This selection was made seeking its remarkable conceptual simplicity, computational speed, and robustness to noise. In this work, two well-known databases were used, one containing normal sinus rhythms and another one containing arrhythmias, both from the MIT medical databank. For different values of embedding time delay τ, normalized permutation entropy and statistical complexity measure are computed to finally represent them on the horizontal and vertical axes, respectively, which define the causal plane H×C. To improve the results obtained in previous works, a feature set composed by these two magnitudes is built to train the following supervised machine learning algorithms: random forest (RF), support vector machine (SVM), and k nearest neighbors (kNN). To evaluate the performance of each classification technique, a 10-fold cross-validation scheme repeated 10 times was implemented. Finally, to select the best model, three quality parameters were computed, namely, accuracy, the area under the receiver operative characteristic (ROC) curve (AUC), and the F1-score. The results obtained show that the best classification model to detect the ECG coming from arrhythmic patients is RF. The values of the quality parameters were at the same levels reported in the available literature using a larger data set, thus supporting this proposal that uses a very small-sized feature space to train the model later used to classify. Summarizing, the attained results show the possibility to discriminate both groups of patients, with normal sinus rhythm or arrhythmic ECG, showing a promising efficiency in the definition of new markers for the detection of cardiovascular pathologies.

Abstract 14688: Identification of Patients With Atrial Fibrillation Using an Electrocardiogram in Sinus Rhythm: External Reproduction and Validation of a Deep Neural Network Algorithm

Introduction: Atrial fibrillation (AF) can occur asymptomatically and can only be diagnosed while the arrhythmia is present. Hence, it is underdetected. An artificial intelligence (AI) algorithm has been developed by Attia et al. to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm (SR). We aim to reproduce these findings in an unrelated patient population and unrelated clinical center using the same deep neural network (DNN) architecture. Methods: All patients older than 18 years with at least one ECG in SR performed at our center between 10/1/2002 and 8/31/2022 were included in the study. Diagnostic labels were given by the GE-Marquette ECG system. For patients without an ECG in AF, all ECGs in SR were included. For patients with one or more ECGs in AF, all ECG in SR starting 31 days before the AF event were included. The ECGs were randomly allocated to a training, internal validation, and testing dataset in a 7:1:2 ratio. A DNN following the architecture of Attia et al. was trained to discriminate between patients with and without AF. Results: The dataset consisted of 494.042 ECGs in SR from 142.310 patients. AF was diagnosed in 8.9%. Testing the model on the first ECG of each patient (main analysis) resulted in a sensitivity of 78.9% (95% CI, 77.6 - 78.5), specificity of 78.0% (95% CI, 77.5 - 78.5), accuracy of 78.1 (95% CI, 77.6 - 78.5), area under the receiver operating curve (AUC) of 0.87 (95% CI, 0.86 - 0.87), and F1 score of 39.3% (95% CI, 38.1 - 40.6). Testing the model on all ECGs in the first 31 days and selecting the maximum probability of AF (secondary analysis) increased the sensitivity to 81.1 (95% CI, 78.4 - 83.7), specificity to 79.8 (95% CI, 79.3 - 80.3), accuracy to 79.8 (95% CI, 79.4 - 80.3), AUC to 0.88 (95% CI, 0.87 - 0.89), but lowered the F1 score to 20.8% (95% CI, 19.51 - 22.0). Conclusion: An AI-enabled ECG algorithm for the identification of patients with AF on a SR-ECG can be reproduced with external data in an external center at clinical grade accuracy.

Identifying patients with paroxysmal atrial fibrillation from sinus rhythm ECG using random forests

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia which is often challenging to diagnose because patients may be asymptomatic, and episodes are usually intermittent. In this paper we describe a method for identifying patients with a history of PAF using electrocardiogram (ECG) recordings during sinus rhythm. We analyzed, on a beat-to-beat basis, the P-waves in the sinus rhythm ECGs of 69 patients with a history of PAF and 59 healthy individuals. From each subject’s P-waves, we calculated key electrocardiographic metrics, including some which are proposed here for the first time. Using means testing and feature selection methods, we discerned the features which were most useful for classification, and trained a Random Forest which identified PAF patients. Our approach achieved a 93.45% accuracy, sensitivity of 95.21%, and specificity of 91.40% using P-wave integral and novel amplitude and slope-based features which ranked highest in importance compared to other metrics from the literature. In particular, descriptive statistics of P-wave amplitudes, slopes, and integrals, were effective for identifying subjects with PAF history vs. healthy individuals. Our method has a high sensitivity and discrimination ability, with an AUC=0.9669 which is superior to others’, and can thus be potentially valuable for the early identification of patients who are prone to episodes of PAF, even as part of the standard cardiological checkup that most adults undergo periodically.

Deep learning-based electrocardiogram analysis detecting paroxysmal atrial fibrillation during sinus rhythm in patients with cryptogenic stroke: validation study using implantable cardiac monitoring

Abstract Background Atrial fibrillation (AF) is the most cause of cardioembolic source causing cryptogenic stroke. In these, anticoagulation therapy could reduce recurrence of stroke. However, paroxysmal AF would not be detected even by 24 hours Holter monitoring. Deep learning-based electrocardiogram (ECG) analysis models were recently developed to detect AF during sinus rhythm. Purpose We aimed to develop a deep learning algorithm (DLA) to detect AF during sinus rhythm and validate the model in patients with cryptogenic stroke who underwent implantable cardiac monitoring (ICM) to diagnose paroxysmal AF. Methods This cohort study involved three hospitals (A, B, and C). We developed a DLA to detect AF using sinus rhythm 10 s 12-lead ECG. We included adult patients aged ≥18 years from hospital A and B. We used development data from AF adult patients who had at least one atrial fibrillation rhythm in the study period (Jan 2016 to Dec 2021) and non-AF patients who had no reference to AF in the ECG and electronic medical record. DLA was based on convolutional neural network (CNN) using 10 s 12-lead. For external validation, the ECGs from 217 patients (hospital C) with cryptogenic stroke who underwent ICM were analyzed by using the DLA for validating the accuracy in the real-world clinical situations. Results We included 10,605 AF adult patients and 50,522 non-AF patients as development data. During the internal validation, the area under the curve (AUC) of the final DLA based on CNN was 0.793 (95% Confidence interval 0.778–0.807). In external validation data from cryptogenic stroke patients, the mean ICM duration was 15.1 months, and AF &amp;gt;5 mins was detected in 32 patients (14.5%). The diagnostic accuracy of DLA was 0.793 to detect AF during sinus rhythm, and AUC was 0.824. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 0.844, 0.784, 0.403, and 0.967, respectively, which outperformed other conventional predictive methods based on clinical factors, such as CHARGE-AF, C2hest, and HATCH. Conclusions In this study, DLA accurately detected paroxysmal AF using 12-leads normal sinus rhythm ECG in patients with cryptogenic stroke and outperformed the conventional models. The DLA could be used as a screening tool to identify the cause of stroke in the future. Funding Acknowledgement Type of funding sources: None.

Supraventricular tachycardia with double trouble.

A 23-year-old healthy woman who was 10 weeks pregnant presented to the hospital with palpitations. Her presenting rhythm is shown in Figure 1A. The tachycardia terminated with administration of adenosine. Her baseline sinus rhythm electrocardiogram (ECG) is shown in Figure 1B. Metoprolol was initiated, but she had a recurrence of tachycardia. What is your interpretation of the ECGs? What are treatment options?