bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recognition of Brugada type 1 pattern on ECG by Echo State Networks
Authors: Vozzi F , Pedrelli L , Dimitri M G , Piacenti M , Rossi A , Zucchelli G , Solarino G , Nesti M , Notarstefano P , Pieragnoli P , Persiani E , Morales M A , Micheli A 
Published: May 2024

Abstract

Background/Introduction
The development of artificial intelligence (AI) and machine learning (ML) techniques, which can identify data patterns in electrocardiography, has led to the development of automated analysis tools for standard 12-lead ECGs. These tools are able to identify specific features associated with different cardiac diseases, which may be helpful in clinical practice [1]. One field of application is represented by Brugada Syndrome (BrS) diagnosis, which, despite characteristic ECG features, can be challenging to diagnose and correctly distinguish from other diseases.

Purpose
Within the Brugada Syndrome and Artificial Intelligence Applications to Diagnosis system study, we are developing an innovative model for diagnosing Type 1 BrS based on electrocardiographic pattern recognition through the Echo State Networks (ESN), a Recurrent Neural Network (RNN) type of architecture.

Methods
From patients enrolled in 5 Centers, 306 12-lead ECGs were obtained. The datasets were composed of spontaneous Type 1 pattern (coved) (group A, 98 patients); patients undergoing ajmaline test, classified as positive (group B, 47 patients) or negative (group C, 35 patients) according to test results; 220 controls with no clinical and familial history of arrhythmias (group D). ESN analyzed, as input, data collected from 1 beat of the ECG V2 lead. The datasets were used for the ESN model training and were assessed through a double cross-validation approach. The ESN performance was compared with the evaluation of 4 independent cardiologists, and sensitivity, specificity and accuracy were determined.

Results
The ESN model correctly classified 280/306 ECGs (91.5%), with a performance of 3.6 points superior to clinicians (269/306, 88.0%). ESN sensitivity (87.0% vs. 86.9%) and specificity (94.5% vs. 89.1%) are described in Table 1; accuracy in each group of the dataset (groups A, B, C, and D), represented in Table 2, were comparable to or greater than those of clinicians.

Conclusion(s)
In this work, an ESN-based system for diagnosing Type 1 BrS pattern on ECG was developed. Our results show that this ML model can detect ECG patterns associated with Type 1 BrS with accuracy, sensitivity and specificity comparable or superior to clinicians. In the future, we will use a larger dataset to increase the model performance further, with the real possibility of becoming a tool for everyday clinical practice.ESN and clinicians' diagnostic accuracyECG accuracy in each dataset group
Deep embedded clustering of physical behaviour in patients with an implanted cardioverter-defibrillator identifies high and low risk profiles
Authors: Kolk M Z H , Frodi D M , Langford J , Andersen T O , Jacobsen P K , Risum N , Tan H L , Svendsen J H , Knops R E , Diederichsen S Z , Tjong F V Y 
Published: May 2024

Abstract

Introduction
Patients at high risk of sudden cardiac death are treated with an implantable cardioverter-defibrillator (ICD). Patterns in physical behaviour may be prognostic for clinical deterioration. Deep embedded clustering (DEC) employs deep neural networks to learn compact and meaningful representations from high-dimensional data, on which an unsupervised clustering then operates.

Purpose
The objectives of this study were to use DEC to identify and describe behavioural profiles in a prospective ICD cohort and assess their risk of clinical outcomes.

Methods
Data from the multicenter, prospective SafeHeart study conducted at our 2 centers, was used. Patients received an implantable cardioverter defibrillator (ICD) between May 2021 and September 2022, who then wore wearable devices during 6 months that capture physical behaviour. Latent representations were extracted from the time-series behavioural data using a variational autoencoder, and used as input to an unsupervised machine learning algorithm (Figure 1). Kaplan-Meier survival curves were used to visualise the annual risk of appropriate ICD-therapy, and the composite of all ICD-therapy and death.

Results
A total of 272 patients (mean age of 63.1 ±10.2 years, 81% male) were eligible, with a total of 37478 days of behavioural data (138 ±47 days per patient). DEC identified five distinct behavioural profiles: Cluster A (n=70) had high activity levels, mainly at light-to-moderate intensity. Cluster B (n=51) showed above-average sleep efficiency. Cluster C (n=63) exhibited a high-intensity activity profile. Cluster D (n=42) had frequent waking episodes and poor sleep. Cluster E (n=46) had very low physical activity levels and an impaired sleep architecture. Significantly differences were observed between clusters A-E in ICD-therapy and mortality (log-rank p-value 0.04) (Figure 2). Clusters B and D exhibited a threefold decrease in the annual risk of appropriate ICD therapy compared to cluster E.

Conclusion
DEC reveals distinct behavioural profiles in a prospective ICD cohort, with their inherent risk on outcomes. Wearable accelerometer devices enable behavioural monitoring that may provide opportunities for improved personalised risk prediction in a notoriously heterogeneous high-risk patient population.Deep embedded clustering of behaviourEvent rates stratified for clusters
Comparative analysis of classical artificial intelligence and deep learning models for atrial fibrillation detection in electrocardiograms
Authors: Mota D , Filho F N B , Sousa E B , Chagas R A , Sassaki E M , Candoti M W , Kuster G W , Lopes J H 
Published: May 2024

Abstract

Background/Introduction
Early diagnosis of atrial fibrillation (AF) presents a challenging yet critical task for appropriate interventions aimed at reducing disease-related burden. In this context, strategies employing classical artificial intelligence (CAI) and deep learning (DL) have emerged as promising approaches to optimize cardiac disorder screening and detection.

Purpose
This study aimed to compare a CAI model and a DL model for the detection of AF in patients undergoing electrocardiographic (ECG) examinations in tertiary healthcare centers.

Methods
Between December 2022 and November 2023, a total of 135,476 ECGs were performed, comprising 5,067 with AF and 130,409 without AF. The ECGs were analyzed using both artificial intelligence models. The obtained results were then compared to the gold standard (cardiologist's report). In the CAI model, signals were extracted from ECG images, analyzing five key parameters: cardiac rhythm, atrial depolarization, atrioventricular conduction, ventricular depolarization, and ventricular repolarization (figure 1A). These parameters were benchmarked against the standard values from the Brazilian Society of Cardiology guidelines for detecting cardiac anomalies. Conversely, the DL model utilized a one-dimensional ResNet-based Convolutional Neural Network (CNN). This model was trained using ADAM optimization and binary cross-entropy loss, enabling the learning of complex patterns in the data (figure 1B).

Results
The mean age was 54.6 years (71.9 years with AF and 53.9 without AF). In the AF population, 52.2% were male (46% were male in the overall sample). In the analysis conducted, the CAI model showed a sensitivity and specificity of 90% and 62%, respectively, while the DL model had 90% and 69%, respectively. ROC curves were generated for both models, demonstrating the superior performance of the DL model (figure 2A).

Conclusions
Although the sensitivity remained similar between the models, the DL model distinguished itself with higher specificity. These results suggest that artificial intelligence, particularly the deep learning approach, holds promise as a supportive resource in AF diagnosis. However, further studies are needed to evaluate the models more thoroughly and determine their clinical applicability in a broader context.Artificial intelligence modelsROC curves
Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm
Authors: Santini L , Calo L , Bianchi V , Manzo M , Dello Russo A , Savarese G , Pecora D , Amellone C , Santobuono V E , Calvanese R , Viscusi M , Pisano E , Pangallo A , Valsecchi S , Boriani G 
Published: May 2024

Abstract

Background
Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation.

Purpose
This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm.

Methods
The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th–75th percentile: 15-37).

Results
During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02-9.72; P=0.045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74-9.06; P=0.001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74%-98%]) and 55% specificity (95% CI [51%-60%]), while BiV% ≤96% predicted alerts with 74% sensitivity (95% CI [55%-88%]) and 81% specificity (95% CI [77%-85%]).

Conclusions
A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.Time to event
Right ventricular pacing-evoked left ventricular dysfunction is exaggerated by sustained left ventricular pressure overload in a rat model
Authors: Ruppert M , Balint T , Nagy D , Lakatos B K , Olah A , Sayour A A , Barta B A , Osztheimer I , Merkely B , Radovits T 
Published: May 2024

Abstract

Introduction
A growing body of evidence suggests that in the presence of left bundle branch block-induced electromechanical dyssynchrony, the function of the left ventricle (LV) becomes extremely sensitive to arterial afterload. However, data is scarce whether right ventricular pacing (RVP) also sensitizes the LV to alterations in afterload.

Purpose
Hence, in the present translational study we aimed to characterize the acute effect of RVP on LV hemodynamics under standard laboratory conditions using a rat model of chronic LV pressure overload (PO).

Methods
Transverse aortic constriction (TAC group) was carried out in male Wistar rats to evoke sustained LV PO for six weeks. Age- and sex-matched sham-operated animals served as controls (Sham group). LV hemodynamics was assessed in anesthetized rats by pressure-volume analysis using a pressure-conductance micro-catheter (SPR-838) introduced to the LV via the right carotid artery. To evoke RVP, the right jugular vein was cannulated and an ultra-miniature octopolar electrophysiology catheter (EPR-800) was placed into the right ventricle. Pressure-volume loops were registered in the TAC and the Sham groups during intrinsic narrow QRS rhythm (termed as TAC-Vsense and Sham-Vsense) as well as during RVP (termed as TAC-Vpace and Sham-Vpace).

Results
RVP was associated with impaired diastolic function (active relaxation time constant [τ]: 8.6±0.3 vs. 9.6±0.3ms, Sham-Vsense vs. Sham-Vpace and 8.0±0.3 vs. 9.6±0.4ms, TAC-Vsense vs. TAC-Vpace; P&lt;0.001 for both comparisons) and decreased LV contractility (preload recruitable stroke work [PRSW]: 139±9 vs. 114±9mmHg, Sham-Vsense vs. Sham-Vpace and 255±23 vs. 169±17mmHg, TAC-Vsense vs. TAC-Vpace; P&lt;0.001 for both comparisons) in both the TAC and the Sham groups. Nevertheless, the extent of RVP-evoked active relaxation prolongation (Δτ: 12±2 vs. 20±2%, Sham vs. TAC, P=0.005) and LV contractility reduction (ΔPRSW: -18±4 vs. -34±3%, Sham vs. TAC, P=0.007) was found to be more robust in rats with sustained LV PO compared to controls.

Conclusion
Our experimental results indicate that RVP-induced LV dysfunction is exaggerated in case of sustained LV PO.
Spatial relationship between left peri-atrial fat with underlying arrhythmogenic substrate in patients with atrial fibrillation - innocent bystander or culprit?
Authors: Vickneson K , Tonko J , Gharaviri A , Williams M C , Dweck M , Vigneswaran V , Baptiste T , Alonso Solis Lemus J , Corrado C , Niederer S , O'neill M , Whitaker J , Williams S E 
Published: May 2024

Abstract

Introduction
Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Consistent with prior evidence, we have previously shown that peri-atrial fat volume, but not fat attenuation (an imaging biomarker of inflammation), was associated with prevalent atrial fibrillation. Whether this association is secondary to "outside-to-inside" tissue cross talk with local adverse electrical remodelling of adjacent atrial cardiomyocytes is not known.

Purpose
In this novel proof of concept study, we aimed to identify a spatial relationship between left peri-atrial fat and potentially arrhythmogenic substrate (low voltage areas and slow conduction pathways) in the left atrial myocardium.

Methods
Contrast-enhanced cardiac computed tomography (CT) imaging and electroanatomic mapping, during coronary sinus pacing, were performed pre-ablation in atrial fibrillation patients (n=27). The left atrium and surrounding peri-atrial fat were segmented and quantified using seg3D2 and CEMRGapp. We performed registration of the electroanatomic map onto the CT-derived left atrial mesh using OpenEP and EP Workbench. Mean fat volume and attenuation around each electroanatomic point (within a 3mm radial region of interest) were correlated with local voltage and conduction velocity.

Results
A total of 24,839 electroanatomic points were registered onto the 3D CT-derived left atrial mesh, with a median of 935 points (range 673 to 1150) per patient. The average bipolar voltage and conduction velocity in the left atrium was 1.92±1.91 mV and 0.66±0.47 ms-1 respectively. In comparison to areas with little/no surrounding periatrial fat (Quartile 1, ≤0.93 mm3 fat per electroanatomic point), areas with the highest burden of fat (Quartile 4, ≥17.6 mm3) had significantly lower bipolar voltage (1.69±1.7 vs 2.09±2.0 mV, p&lt;0.001) and conduction velocity (0.627±0.43 vs 0.700±0.47 ms-1, p&lt;0.001). However, the overall correlation between periatrial fat volume with bipolar voltage (r=-0.06, p&lt;0.001) and conduction velocity (r=-0.07, p&lt;0.001) was weak. High, not low, fat attenuation (Quartile 4; &gt;-46.5HU vs Quartile 1; ≤-75.5HU) was associated with increased bipolar voltage (2.23±2.1 vs 1.63±1.6 mV) and conduction velocity (0.703±0.42 vs 0.623±0.39 ms-1 respectively).

Conclusion
There is a weak correlation between periatrial fat and localized adverse electrical remodelling. The relationship between fat attenuation and electrophysiological properties was inverted. These findings confirm an association between peri-atrial fat burden but call question as to whether peri-atrial fat inflammation is implicated in atrial fibrillation pathogenesis.
Exercise-induced regional heterogeneity of myocardial left ventricular contraction in hereditary long QT syndrome
Authors: Wiedenhofer J M , Weiss K , Denzler S , Gerds-Li J H , Tscholl V , Scherr D , Parwani A S , Will J , Zhang K , Heinzel F R 
Published: May 2024

Abstract

Background
Hereditary long QT syndrome (LQTS) is characterized by prolonged ventricular repolarization and an increased risk for ventricular arrhythmias (VA) and sudden cardiac death. Dispersion of ventricular repolarization in LQTS may be more pronounced during exercise and not only impacts the heart’s electrical functions but also the pattern of mechanical contraction. Little is known about the intraventricular heterogeneity of wall motion abnormalities in LQTS patients.

Methods
In this cross-sectional study we quantify regional, transmural, and longitudinal alterations in left ventricular (LV) contraction at rest, during exercise, and recovery in genetically confirmed LQTS patients and healthy controls to evaluate their value in differentiating high risk patients as defined by a history of VA. ECG and echocardiographic assessments were conducted during rest, peak ergometer activity (105 to 125 bpm), and four minutes into recovery phases. Longitudinal strain (LStr) parameters were measured for each LV segment separately. We differentiated LStr variations as heterogeneity of LStr amplitude (hetLStr; standard deviation [SD] or mean range) and dispersion of time-to-peak LStr (disLStr; SD or mean range). Here, we present the analyses of disLStr.

Results
At present, 15 patients with genetically confirmed LQTS (LQT1: 8, LQTS 2: 7; mean QTc 475 ± 46 ms) have been included of which 9 had a history of VA. In addition, we collected data from 8 age- and gender-matched control patients (mean QTc 401 ± 18 ms). LV ejection fraction and LStr were not significantly different between the LQTS and control groups. However, LQTS patients exhibited a significantly lower mean LStr in both the endocardial and epicardial layers across all phases compared to control. We observed a significantly more pronounced disLStr across all segments in the LQTS group during the rest and recovery phases in the endocardial layer and during all phases in the epicardial layer. Regional differences (apical vs. basal) were found in all LQTS patients only endocardially at rest. Globally, disLStr showed transmural differences (epicardial vs. endocardial) in both asymptomatic and symptomatic LQTS patients during peak exercise and recovery. Upon segmental analysis, it was observed that symptomatic patients exhibited these transmural differences apically already at rest (p=.004), as well as in the basal region during peak exercise (p=.01), and in the apical region during recovery (p=.02).

Conclusion
LQTS patients show regional (base to apex) and transmural (endocardial to epicardial) heterogeneities in myocardial contraction at rest, during exercise, and recovery as compared to healthy patients. These regional variations in myocardial contraction patterns may be more pronounced in high risk LQTS patients. Ongoing analyses of hetLStr are in progress to corroborate our findings.
P-wave duration and interatrial block as predictors of ischemic stroke: a systematic review and meta-analysis
Authors: Nedios S , Lioutas V , Koutalas E , Zagoridis K , Intzes S , Symeonidou M , Dilk P , Seewoester T , Bode K , Bollmann A 
Published: May 2024

Abstract

Introduction
Ischemic stroke-risk has been associated with clinical characteristics, atrial remodeling and P- wave changes, like P-wave duration (PWD) or interatrial block (IAB). Despite the increasing evidence though, their use in risk-stratification remains limited.

Purpose
The aim of this review and meta-analysis was to examine the predictive value of PWD/IAB for stroke and offer practical implications.

Methods
Publication databases were systematically searched and studies reporting PWD and/or morphology at baseline and thromboembolic events (TEs) during follow-up (FUP) were included. IAB was defined as partial (pIAB) if PWD≥120 ms or advanced (aIAB) if the P-wave was biphasic in the inferior leads. After quality assessment and data extraction, random-effects analysis calculated odds ratio (OR) and confidence intervals (CI). Subgroup-analysis was performed to address heterogeneity.

Results
Among 180.997 patients (17 studies, mean 66 years old), including primary care patients (n=152.759), 15.578 (9%) had a new TE during 4 years of FUP. The pIAB was significant TE-risk predictor for non-primary care patients (OR:1.7; 95% CI:1.4-2.1) and those with prior TE (OR:1.8; 95% CI:1.4-2.2), but not for primary-care (OR:1.0) or those without prior TE (OR:1.3). The aIAB was significant TE-risk predictor both for primary (OR:1.6; 95% CI:1.4-1.9) and nonprimary care (OR:2.5; 95% CI:1.8-3.4) as well as those without (OR:2.2; 95% CI:1.1-4.4) or with prior TE (OR:2.6; 95% CI:1.4-4.7). There was heterogeneity but no publication bias in the subgroup-analysis.

Conclusion
Interatrial block is an independent predictor of TE-risk. The association is stronger for non-primary care patients or those with prior TE. Thus, PWD and IAB could be used as selection criteria for intensive screening, follow-up or interventions.
Heart rate from intermittent home measurements within an atrial fibrillation screening program is associated with mortality
Authors: Van Der Velden R , Linz D , Svennberg E 
Published: May 2024

Abstract

Introduction
A high resting heart rate is a predictor of cardiovascular disease and cardiovascular mortality. Studies on the association between heart rate and outcomes often measure heart rate at a single time point, even though multiple measurements may be a stronger predictor of cardiovascular risk. Indeed, the association between short-term home heart rate measurements and outcomes has not been extensively studied.

Purpose
This sub study of the STROKESTOP trial explores the association of baseline and home resting heart rate measurements with all-cause and cardiovascular mortality.

Methods
The STROKESTOP randomised controlled trial consisted of an atrial fibrillation (AF) screening program among the 75- and 76-year-old inhabitants of two Swedish regions. Participants in the intervention group were invited to a two week intermittent screening program, consisting of twice daily measurements with an ECG-based handheld device. For this sub analysis, baseline heart rate, the median of 5 home morning heart rates and the median of 5 home evening heart rates were evaluated. Associations were evaluated by heart rate quintiles (41-63 bpm, 64-70 bpm, 71-76 bpm, 77-84 bpm and ≥85 bpm), with 64-70 bpm as the reference category. Hazard rations were obtained by Cox regression analysis. The full multivariable model included demograhic factors such as sex and educational level, medical history of several diseases and the use of cardiovascular medication. Both all-cause mortality and cardiovascular mortality were evaluated.

Results
In total, 5873 individuals were included in this analysis. Overall, 55.6% were women and the median baseline heart rate was 73 [65-81] bpm, whereas the median of morning heart rates was 71 [64-78] bpm and that of evening heart rates 71 [65-79] bpm. Having a baseline heart rate in the highest quintile compared to the reference quintile, showed a hazard ratio (HR) for all-cause mortality in unadjusted analysis of 1.39 (1.11-1.73) (p=0.004), and of 1.51 (1.20-1.90) in the fully adjusted analysis (p&lt;.001). For cardiovascular mortality, HRs were 1.78 (1.21-2.63) (p=0.004) and 2.13 (1.42-3.20) (p=&lt;.001), respectively. The association between the highest heart rate and all-cause mortality and cardiovascular mortality were substantially strengthened when median morning heart rates were considered (HR 2.03 (1.61-2.56) and 2.18 (1.72-2.76), p&lt;.001 for all-cause mortality on univariable and multivariable analysis, HR 2.35 (1.57-3.51) and 2.59 (1.72-3.92), p&lt;.001) for cardiovascular mortality). Associations between home evening heart rates and all-cause and cardiovascular mortality were not substantially stronger compared to the associations with baseline heart rate.

Conclusion
Our study shows that home-measured morning heart rate is more strongly associated with all-cause and cardiovascular mortality compared to a single time-point office heart rate.Hazard ratios for mortality