bluuebunny/crossref_metadata_2025_split huggingface dataset:
The performance of preliminary version of irregular heart rhythm notification algorithm of a consumer smartwatch for screening of atrial fibrillation
Authors: Lee J H , Cho Y , Oh I Y , Lee S , Kim M , Lee J , Lee W , Kwon S , Seo H , Han J , Seo J 
Published: November 2023

Abstract

Background
Smartwatch photoplethysmography (PPG) is a useful non-invasive tool for atrial fibrillation (AF) screening. However, the process of signal acquisition is vulnerable to variable factors such as motion, respiration, and unstable sensor contact which may lead to false positive results. Thus, an optimal signal acquisition and processing algorithm is needed to balance the inherent trade-off between sensitivity and specificity for smartwatch PPG to be used for AF screening in daily life.

Purpose
This study aimed to evaluate the efficacy of the preliminary version of the Irregular Heart Rhythm Notification (IHRN) feature of a consumer smartwatch for AF screening.

Methods
We obtained time-synchronized PPG and ECG signals using smartwatches and wearable ECG patches in 49 patients with paroxysmal and persistent AF in a free-living setting for up to 7 days. The IHRN algorithm selectively schedules PPG tachogram data acquisition at specific time interval, classifies heart rhythm for each interval, and integrates the classification results to determine whether to alert the user of their irregular heart rhythm. We tested the diagnostic performance of the IHRN algorithm for identifying AF episodes lasting 30 minutes or more.

Results
We obtained a total of 364,260 minutes of ECG recording and time-synchronized 27,524 minutes of PPG signal in 49 AF patients. Out of the PPG signals collected, 19,341 minutes (70.3%) were considered valid for the analysis. On the ECG analysis, 754 AF episodes (≥30 seconds) were identified. Longer-lasting AF episodes had a higher likelihood of being captured by PPG and consequently classified as AF by the algorithm (Figure 1). AF episodes lasting over 30 minutes were identified in 28 out of 49 patients. Regarding the subjective-level analysis, the sensitivity and specificity of IHRN for AF ≥ 30 minutes were 82.1% and 100.0%, respectively. In tachogram-level analysis, the positive predictive value was 98.7%.

Conclusions
The preliminary version of the IHRN algorithm of the smartwatch demonstrated excellent performance in identifying AF in a free-living setting. Further large-scale research is needed to determine whether this algorithm is useful for AF screening in a real-world setting.Figure 1
Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population
Authors: Ramirez J , Martinez J P , Van Duijvenboden S , Tinker A , Lambiase P , Orini M , Munroe P B , Miguel A 
Published: November 2023

Abstract

Background
Life-threatening ventricular arrhythmias (LTVA) are a leading cause of mortality, but early identification of high-risk individuals remains a major challenge. An ECG index, TMV (T-wave morphology variations), predicts LTVA in 51,794 individuals without known cardiovascular disease from the UK Biobank (UKB) within a 10-year follow up (cohort 1), with an area under the ROC curve (AUC) of 0.558 and a hazard ratio (HR) of 1.57. Machine learning on the ECG has shown a high accuracy in detecting patients with cardiovascular disease, but its LTVA predictive value in a low-risk population and comparisons with hypothesis-driven risk markers, like TMV, is unknown.

Purpose
We tested the LTVA predictive value of a score combining a multi-layer convolutional neural network (CNN) model and TMV in individuals without known cardiovascular disease.

Methods
Using UKB we split the individuals from cohort 1 into training (90%) and internal test (10%) sets (Figure 1). In the training set, we applied 10-fold cross validation to train a multi-layer CNN with an attention layer, where the input was a 15-second ECG at rest (lead I), and the output was occurrence (or not) of LTVA within a 10-year follow-up. We used 10-second ECG data (lead I) from an independent cohort of 32,209 individuals without cardiovascular disease from UKB (cohort 2) as an external test set (median follow-up was 3.4 years), where we also calculated TMV. We next combined them into a score (weighted by their relative contribution to LTVA in a logistic regression). The AUC, and Cox regression HRs were calculated to estimate the performance of the CNN, TMV and the combined score.

Results
In cohort 1, 217 subjects had a LTVA during the follow-up period. In the internal test set, the AUC of the CNN was 0.707. In the external test set (60 LTVA events during the follow-up period), the CNN’s prediction and TMV led to AUCs of 0.610 and 0.604, respectively. Interestingly, the CNN’s prediction and TMV were not correlated (ρ = 0.005), and the combined score led to an AUC of 0.627. We set a threshold at the score value that maximised sensitivity and specificity, and survival analyses showed a HR of 3.007 (P &lt; 0.0001) for individuals in the score+ (score &gt; threshold) versus those in the score- (score &lt; threshold) group, after adjusting for age and gender (Figure 2). The continuous score also predicted LTVA independently from age and gender.

Conclusions
A CNN-based model predicts LTVA in a low-risk population independently from TMV, a strong ECG risk predictor derived from knowledge on the underlying electrophysiology. When TMV and the CNN are combined into a score, the LTVA risk stratification improves, independently from age and gender. Our findings support the combination of hypothesis-based risk markers and CNN approaches to optimise LTVA prediction in a low-risk population.Study designSurvival curves
Reducing the burden of inconclusive smart-device single-lead ECG tracings via a novel atificial intelligence algorithm
Authors: Weidlich S , Mannhart D , Kennedy A , Doggart P , Serban T , Knecht S , Du Fay De Lavallaz J , Kuehne M , Sticherling C , Badertscher P 
Published: November 2023

Abstract

Introduction
Multiple smart-devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are presently available. The rate of inconclusive tracings by the manufacturers’ algorithms is currently too high to be clinically useful. Reliable artificial intelligence (AI) algorithms may be valuable for managing the large amount of data created by smart-devices.

Method
This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart-device agnostic AI-based algorithm for detecting AF from four different commercially available smart-devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and four smart-device SL-ECGs. Single-lead ECGs were exported as PDF files and converted into Scalable Vector Graphics (SVG) images for signal extraction. The novel AI-algorithm was compared with each manufacturer’s algorithm.

Results
We enrolled 206 patients (31% female, median age 64 years). AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI-algorithm vs. manufacturer algorithm were: 88% vs. 81% (p=0.34) and 97% vs. 77% (p&lt;0.001) for the AliveCor KardiaMobile, 86% vs. 81% (p=0.45) and 95% vs. 83% (p&lt;0.001) for the Apple Watch 6, 91% vs. 67% (p&lt;0.01) and 94% vs. 82% (p&lt;0.001) for the Fitbit Sense, 86% vs. 82% (p=0.63), and 94% vs. 80% (p&lt;0.001) for the Samsung Galaxy Watch3, respectively. (Figure 1) In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2%) was significantly lower for all smart-devices using the AI-based algorithm compared to manufacturer’s algorithms (14-17%) p&lt;0.001.

Conclusion
A novel AI-algorithm reduced the rate of inconclusive SL-ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers’ algorithms.Central Illustration
Artificial intelligence-assisted examination of cardiac health status through electrocardiogram
Authors: Sung D Y , Sung S H , Huang J T , Huang W M 
Published: November 2023

Abstract

Background
Cardiac diseases are the main causes of death. Echocardiogram is the standard testing tool of heart diseases but it is inconvenient and expensive. This study aims at developing an artificial intelligence (AI)-assisted method to interpret electrocardiograms (ECG) for cardiac health status.

Method
A total of 70757 subjects undergoing concomitant ECG and echocardiography were enrolled. ECG was analyzed by using Philips DXL algorithm to obtain 512 parameters, and a healthy heart is defined by normal cardiac chamber size, myocardial thickness, systolic function, diastolic function, and valvular function. A multi-layer perceptron (MLP) and Long short term memory (LSTM) were developed with hold-out validation (training dataset 80% and testing dataset 20%). Three categories of input data are made based on the percentage of healthy data of 30.6%. Death of the participants were confirmed via linking database to the National Death Registry A Kaplan-Meier survival curve analysis was conducted in the testing dataset stratified by AI-determined cardiac health status.

Results
The testing results of the proposed methods on an independent dataset are shown in Table 1. It is interesting that using MLP has similar performance as the LSTM model. Furthermore, the 30-layer MLP model has a F1 value of 83.8% and the 50-layer MLP model has a F1 value of 83.8, while the F1 value of 2-layer LSTM_0.2 is 85.2% and 2-layer LSTM_0.2 is 84.6%. In addition, AI-determined healthy hearts were associated with better long-term survival.

Conclusion
An AI-assisted analysis on ECG can act as a preliminary examination of cardiac diseases to facilitate the arrangement of echocardiograms for early detections.
The exploratory clinical trial of AI-enabled Holter electrocardiogram for diagnosis of paroxysmal atrial fibrillation from sinus rhythm waveforms
Authors: Tamura Y , Taniguchi H , Iwasawa J , Watanabe E , Ogawa S , Takechi M , Takata T , Taniguchi T 
Published: November 2023

Abstract

Backgrounds
Machine learning (ML) models using convolutional neural networks (CNNs) have been successfully applied to arrhythmia detection and prediction. The current study aimed to develop a CNN-based AI algorithm for the prediction of paroxysmal atrial fibrillation (PAF) patients during sinus rhythm waveforms from Holter electrocardiograms.

Methods
A ML model was developed by using 2-leads from Holter ECG waveforms divided into slots of 30 seconds each. As training data, we prepared &gt; 300000 slots with sinus rhythm and they were subsequently converted to image data for application to the CNN-based AF detection algorithm. The performance metrics obtained for the ML model for PAF detection were as follows: sensitivity, 85.2 %; specificity, 64.0 % and AUC for PAF prediction was 0.84. To validate the performance of the ML model, multicenter clinical trial was conducted. The clinical trial was conducted at three independent centers and included 24 patients with PAF and 20 patients in true sinus rhythm. Five factors were matched between the PAF and sinus groups: age, gender, hypertension under treatment, diabetes under treatment, and smoking. Waveform data from validation datasets to Grad-CAM were applied to obtain explainable AI (XAI) images, which allowed for a heat map over the waveform to be produced.

Results
For the primary endpoint, the estimated sensitivity and specificity [95% CI] were 91.7% [73.0-99.0%] and 65.0% [40.8-84.6%], respectively (Table 1). The sensitivity and specificity of the secondary endpoints using 10 time-independent slots were the same as for the primary endpoint. Regarding safety endpoints, no adverse events attributable to the test device itself were observed. Based on the regions of interest highlighted via Grad-CAM, the most contributing featured regions to predict PAF were indicated by areas on P waves and QT area (Figure 1). Featured regions to rule out the existence of PAF indicated areas just on QRS waves were also present. It was therefore determined that the proposed CNN-based ML model focuses on the P-wave area and atrial repolarization area in the case of PAF.

Conclusion
The study suggested that 2-lead Holter ECG with 30 second slot could detect PAF patients from their sinus rhythm waveforms and the performance was more effectively than using 12-lead ECGs. The results of this clinical trial are expected to contribute to the screening of patients with PAF that could not be detected by a single time Holter ECG.Table 1Figure 1
Validation of electrocardiographic artificial intelligence model for outcome prediction in patients with heart failure
Authors: Cho Y , Yoon M , Kim J , Lee J H , Oh I Y , Park J J , Lee C J , Kang S M , Choi D J 
Published: November 2023

Abstract

Introduction
Although several valuable biomarkers have been introduced for heart failure (HF) patients, their utilization in routine clinical practice is often constrained by cost and limited availability. Electrocardiogram (ECG) is an essential and cost-effective tool for evaluating cardiovascular diseases. We tested artificial intelligence (AI) algorithm analyzing a printed ECG image for the outcome prediction in patients with acute HF.

Methods
Two tertiary centers prospectively enrolled 1,254 patients with acute HF syndrome. Baseline ECG images were analyzed using a deep learning system called Quantitative ECG (QCG™), which features a CNN-based binary classifier trained to detect several urgent clinical conditions including shock, cardiac arrest, myocardial infarction, and HF, in addition to rhythm diagnosis.

Results
Patients with reduced left ventricular ejection fraction (LVEF, &lt;40%) had significantly higher QCG scores for LV dysfunction (QCG-LVdys) (0.80 ± 0.20 vs. 0.38 ± 0.27, P &lt; 0.001), and the AUC of QCG-LVdys for low LVEF was 0.884. Fifty-three patients (4.2%) resulted in in-hospital cardiac death (IHCD), and QCG score for critical events (QCG- Critical) was significantly higher in these patients than in survivors (0.57 ± 0.23 vs. 0.29 ± 0.20, P &lt; 0.001). QCG-Critical was an independent predictor for IHCD after adjustment for age, sex, comorbidities, etiology/type of HF, atrial fibrillation, and QRS widening (adjusted OR = 1.68 [95% CI, 1.47 – 1.92], P &lt;0.001, per 0.1). After further adjustments for echocardiographic LVEF and NTproBNP, QCG-Critical was still significantly correlated with IHCD (adjusted OR = 1.59 [95% CI, 1.36 – 1.87], P &lt;0.001, per 0.1). The AUC of QCG-Critical for IHCD was 0.821 which was higher than the AUC of echocardiographic LVEF (P&lt;0.001) or NTproBNP (P=0.07) (Fig 1A). The AUC of the multivariate model with QCG-Critical and other covariates was 0.866 (Fig 1B). During long-term follow-up, patients with higher QCG-Critical scores (&gt;0.5) showed higher all-cause mortality rate compared to those with low QCG-Critical scores (&lt;0.25) (adjusted HR = 2.69 [2.14 – 3.38], P &lt; 0.001) (Fig 2).

Conclusion
Predicting outcomes in patients with acute HF appeared feasible using the newly developed Quantitative ECG (QCG) scores. These results suggest the possibility that QCG may serve as a novel biomarker for HF patients.ROC curves for In-hospital cardiac deathSurvival curves according to QCG scores
ECG-based deep learning for detecting epicardial coronary occlusion in acute myocardial infarction
Authors: Herman R , Meyers H P , Smith S W , Demolder A , Bertolone D T , Leone A , Bermpeis K , Viscusi M M , Boza V , Kresnakova V , Martonak M , Perl L , Hatala R , Bartunek J , Barbato E 
Published: November 2023

Abstract

Background
One third of Non-ST-elevation myocardial infarction (NSTEMI) patients have occlusion myocardial infarction (OMI) associated with poor short- and long-term outcomes due to delayed invasive management.

Purpose
We sought to develop and externally validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art criteria.

Methods
An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international database and a tertiary care center. This AI model was tested on a holdout set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, and predictive values.

Results
The OMI AI model achieved an AUROC of 0.941 (95% CI: 0.926, 0.954) in identifying the primary outcome of OMI on 12-lead ECGs in the holdout set [Figure 1A], yielding robust performance across genders and age subgroups (ranging from 0.907 to 0.951 AUROC) [Figure 1B]. Sensitivity in detecting OMI was significantly higher for OMI AI model compared to STEMI criteria (82.6% [95% CI: 78.9%, 86.1%] vs. 34.4% [95% CI: 30.0%, 38.8%]) and statistically equal compared to ECG experts 75.9% [95% CI: 71.9%, 80%] in identifying OMI [Table 1]. The OMI AI Model showed statistically superior performance compared to STEMI criteria and equal (non-inferior) performance to ECG experts when evaluated using industry-standard metrics.

Conclusions
This sizeable validation of an AI ECG model demonstrates the ability of AI to detect acute OMI and suggests a potential in improving ACS patient triage and need for immediate revascularization.AI Model Performance on Holdout SetBenchmark Comparison
Development of a machine learning model using 12-lead ECG to improve acute dianosis of pulmonary embolism
Authors: Silva B V , Marques J , Nobre Menezes M , L Oliveira A , J Pinto F 
Published: November 2023

Abstract

Introduction
Pulmonary embolism (PE) is a life-threatening condition. Given the lack of specificity in symptoms and clinical decision rules, diagnostic uncertainty in PE remains high and in most of the cases requires confirmation by computed tomography pulmonary angiogram (CTPA). This could be critical to decide fibrinolysis indication in hemodynamic unstable patients (pts) with PE suspicion in out-of-hospital setting or if CTPA is not immediately available.
The implementation of artificial intelligence (AI) in medical diagnosis has attracted major attention last years. Electrocardiography (ECG) signals and patterns can be detected by AI networks with precision. The purpose of this study was to develop an AI model for predicting PE using 12-lead ECG.

Methods
We extracted 1014 ECGs of pts admitted to emergency department who underwent CTPA due to PE suspicion: 911 ECGs were used for development of the AI model (derivation cohort) and 103 ECGs were used for testing the PE prediction model (validation cohort). An AI algorithm based on an ensemble neural network was developed using 12-lead ECG signal.
The first endpoint was the diagnosis of PE. To evaluate the performance of the AI model, we compare the performance of AI model against the recommend clinical prediction rules for PE based on clinical probability and D-dimer measurement (Wells and Geneva criteria combined with fixed and age-adjusted D-dimer cut-off, YEARS and PEGeD algorithms).

Results
On validation cohort, AI model achieves greater specificity to detect PE than the commonly used clinical prediction rules (p&lt;0.001) -Table 1. The AI model shown a specificity of 100%, which is particularly relevant in the context of fibrinolysis decision. Although the sensitivity of the AI model is lower (p=0.001), the biggest gain of this model is to provide security to the physician to stablish a definite diagnosis. Globally, the AI model performed significantly better than all the other models (AUC 0.75, p&lt;0.001), which had nearly no discriminative power.
The incidence of typical PE ECG features (S1Q3T3, right bundle branch block and V1-V3 T wave inversion) was similar in pts with and without PE, meaning that AI model provide information beyond these findings and can improve PE prediction.
ECGs were included regardless of cardiac rhythm (including pacing), so these findings can be generalized to all pts. Model performance is similar across gender.

Conclusion
In this study we developed and validated a deep learning-based AI algorithm for PE detection using a 12-lead ECG which demonstrated high performance.
Detection of left ventricular contractile dysfunction in patients with premature ventricular contractions using electrocardiogram trained using a deep learning model
Authors: Kowlgi G , Dai Q , Greason G , Woelber T , Wight J , Noseworthy P A , Friedman P A , Asirvatham S J , Attia I Z 
Published: November 2023

Abstract

Background
Premature ventricular contractions (PVCs) are the most common arrhythmia affecting the ventricle. Frequent PVCs are associated with the development of PVC-cardiomyopathy (CM). It is known that the PVC burden has a direct correlation to the severity of left ventricular (LV) dysfunction. However, only 5% of patients with frequent PVCs (&gt;10,000 per day) have associated PVC-CM. Thus, selecting patients for therapy options with drugs or catheter ablation is challenging, especially, when patients are asymptomatic with the PVCs.

Purpose
To train a deep learning model to predict the probability of low LV ejection fraction (LVEF) in patients with PVCs on a 12-lead electrocardiogram (ECG).

Methods
Using 69,440 paired 12-lead ECGs (containing at least one PVC), and echocardiogram data, including the left ventricular ejection fraction at Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. ECGs were acquired at a sampling rate of 500 Hz using a Marquette ECG machine (GE Healthcare) and stored using the MUSE data management system for later retrieval. Comprehensive 2-D or 3-D and Doppler echocardiography were available for all patients. We trained a convolutional neural network using the Keras framework with TensorFlow (Google). The ECG-echocardiogram pairs were divided into a 60:20:20 training:validation:test split.

Results
The mean age of the patients used to train the model was 69.9 ± 13.0 years, and 65.2 percent of patients were male. The mean LVEF for this population was 52 ± 14.9 %, and the proportion of patients with low LVEF was 16.8 percent. The model yielded values for area under the curve, sensitivity, specificity, and accuracy of 0.897, 0.830, 0.813, and 0.816, respectively (Figure 1). A representative example of model prediction of normal LVEF and low LVEF is shown in Figure 2.

Conclusion
Using large-scale data, we have trained a highly accurate convolutional neural network with excellent predictive accuracy for LV systolic dysfunction using the surface ECG. Such a tool would be invaluable for cardiologists when risk-stratifying patients with frequent PVCs, especially those that are asymptomatic. Patients at risk will benefit from early referral to electrophysiologists for drug therapy and catheter ablation to mitigate the risk of PVC-CM.Figure 1Figure 2