Poor evidence for poor R wave progression in coronary disease: a scoping review

BACKGROUND Poor R wave progression (PRWP) and reversed R wave progression (RRWP) have long been noted in electrocardiograms as potential indicators of anterior wall fibrosis or chronic coronary artery disease; however, the quantity and quality of evidence supporting these associations warrants closer examination. OBJECTIVE The aim of this scoping review is to assess the breadth of evidence regarding the diagnostic significance of PRWP and RRWP, explore the extent of research, study populations and methodologies, and the presence of gaps in knowledge regarding these electrocardiographic phenomena and their association with coronary diseases. DESIGN We conducted a comprehensive search across PubMed, Web of Science, and Scopus, covering literature on PRWP or RRWP in the context of myocardial infarction, ischemia, or fibrosis from any time period and in any language. RESULTS A total of 20 studies were included in this review, highlighting the severe paucity of data. No high-quality accuracy studies have been identified, and existing research suffers from methodological issues, in particular selection bias. Prevalence and prognostic studies showed significant heterogeneity in terms of definitions and outcomes, which contributes to an alarming risk of bias. CONCLUSIONS The lack of solid evidence for PRWP and RRWP as diagnostic markers for acute and chronic coronary artery disease necessitates caution in clinical interpretation. Future research should focus on well-designed case-control studies to clarify the diagnostic accuracy of these markers. Until robust evidence is available, the reliance on PRWP/RRWP for diagnosing anterior infarction should be discouraged, reflecting a gap between clinical practice and evidence-based medicine.

Long-term follow-up of patients with Brugada syndrome: Foremost risk factors associated with overall arrhythmic events.

Brugada syndrome (BS) is characterized by ST segment elevation in right precordial leads (V1-V3), ventricular tachycardia (VT), ventricular fibrillation (VF), and sudden cardiac death (SCD) in individuals without structural heart disease. The aim of this study is to contribute to the controversial issue of finding the most valuable marker that can predict poor prognosis during follow-up in patients with a diagnosis of BS. A total of 68 patients diagnosed with BS or had Brugada-type ECG change between January 1997 and July 2012 at the Department of Cardiology of Baskent University Faculty of Medicine, Ankara, Turkey, were included in this cohort study. Patients were screened every 6 months for arrhythmia-related syncope, SCD, appropriate and inappropriate defibrillation (shock), AF development and death; collectively defined as "arrhythmic events" and were the primary endpoints. Patients with and without arrhythmic events were compared. The mean age was 34.9 ±â€…12.2 years (9-71 years), and 52 (76.5%) patients were male. Mean follow-up was 49.6 ±â€…37.6 months (4-188 months). Univariate analysis showed that male sex (P = .004), type 1 electrocardiographic pattern (P = .008), SCD (P = .036), VT/VF history (P = .046), requirement for electrophysiological studies (P = .034), implantable cardioverter-defibrillator placement (P = .014) were found to demonstrate significant differences in patients with and without arrhythmic events. In multivariable analyzes, spontaneous type 1 ECG presence (HR = 8.54, 95% CI: 0.38-26.37; P = .003) and VT/VF history (HR = 9.21, 95% CI: 0.004-1.88; P = .002) were found to be independently associated with arrhythmic events. We found the presence of spontaneous type 1 ECG and a history of VT/VF to be associated with increased likelihood of overall arrhythmic events in BS. Given the higher risk of poor prognosis, we recommend additional measures in patients with BS who have these features.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings.

Objective.Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.Approach.In this work, we proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Main results.Our results show thatPower-MFoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Significance.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

Predicting and Recognizing Drug-Induced Type I Brugada Pattern Using ECG-Based Deep Learning.

BACKGROUND: Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug-induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and evaluate its performance in recognizing and predicting BrS diagnosis. METHODS AND RESULTS: Consecutive patients who underwent ajmaline testing for BrS following a standardized protocol were included. ECG tracings from baseline and during ajmaline were transformed using wavelet analysis and a deep convolutional neural network was separately trained to (1) recognize and (2) predict BrS type I pattern. The resultant networks are referred to as BrS-Net. A total of 1188 patients were included, of which 361 (30.3%) patients developed BrS type I pattern during ajmaline infusion. When trained and evaluated on ECG tracings during ajmaline, BrS-Net recognized a BrS type I pattern with an AUC-ROC of 0.945 (0.921-0.969) and an AUC-PR of 0.892 (0.815-0.939). When trained and evaluated on ECG tracings at baseline, BrS-Net predicted a BrS type I pattern during ajmaline with an AUC-ROC of 0.805 (0.845-0.736) and an AUC-PR of 0.605 (0.460-0.664). CONCLUSIONS: BrS-Net, a deep convolutional neural network, can identify BrS type I pattern with high performance. BrS-Net can predict from baseline ECG the development of a BrS type I pattern after ajmaline with good performance in an unselected population.

Association Between Family History and Early-Onset Atrial Flutter Across Racial and Ethnic Groups.

BACKGROUND: Genetic and familial contributions to early-onset atrial fibrillation are described primarily in individuals of European ancestry. However, the role of racial and familial contributions in the pathogenesis of early-onset atrial flutter (EOAFL) is unclear. METHODS AND RESULTS: In this cross-sectional study, participants were enrolled prospectively from 2015 to 2021 in multiple academic centers with a diagnosis of atrial flutter (AFL) confirmed by ECG. EOAFL was defined as a diagnosis of AFL before age 66 years with no concomitant or previous diagnosis of atrial tachyarrhythmias. Family history was adjudicated through baseline questionnaires and direct family interviews about the diagnosis of atrial tachyarrhythmias, stroke, and cardiomyopathy. The primary exposure was a positive family history in first-degree relatives, and the primary outcome was the odds of EOAFL versus late-onset AFL. A total of 909 patients were enrolled. Participants with a positive family history of atrial tachyarrhythmias were younger, less likely to be of Black race, and more likely to have EOAFL. The adjusted odds ratio (OR) for EOAFL in those with a positive family history was 1.8 (95% CI, 1.1-3.0). There was an increased odds of EOAFL in those of Black race (OR, 2.1 [95% CI, 1.4-3.2]), alcohol use (OR, 1.6 [95% CI, 1.0-2.6]), and obstructive sleep apnea (OR, 1.9 [95% CI, 1.0-3.4]). Use of cardioselective ß blockers or calcium channel blockers before the diagnosis of AFL were associated with a lower odds of EOAFL (OR, 0.5 [95% CI, 0.2-0.9]). CONCLUSIONS: These findings suggest a potentially hereditary predisposition to EOAFL across race and ethnicity, warranting further study of the genetic contributions to AFL.

Diagnostic accuracy of digital technologies compared with 12-lead ECG in the diagnosis of atrial fibrillation in adults: A protocol for a systematic review.

BACKGROUND: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in the world. AF increases the risk of stroke 5-fold, though the risk can be reduced with appropriate treatment. Therefore, early diagnosis is imperative but remains a global challenge. In low-and middle-income countries (LMICs), a lack of diagnostic equipment and under-resourced healthcare systems generate further barriers. The rapid development of digital technologies that are capable of diagnosing AF remotely and cost-effectively could prove beneficial for LMICs. However, evidence is lacking on what digital technologies exist and how they compare in regards to diagnostic accuracy. We aim to systematically review the diagnostic accuracy of all digital technologies capable of AF diagnosis. METHODS: MEDLINE, Embase and Web of Science will be searched for eligible studies. Free text terms will be combined with corresponding index terms where available and searches will not be limited by language nor time of publication. Cohort or cross-sectional studies comprising adult (≥18 years) participants will be included. Only studies that use a 12-lead ECG as the reference test (comparator) and report outcomes of sensitivity, specificity, the diagnostic odds ratio (DOR) or the positive and negative predictive value (PPV and NPV) will be included (or if they provide sufficient data to calculate these outcomes). Two reviewers will independently assess articles for inclusion, extract data using a piloted tool and assess risk of bias using the QUADAS-2 tool. The feasibility of a meta-analysis will be determined by assessing heterogeneity across the studies, grouped by index device, diagnostic threshold and setting. If a meta-analysis is feasible for any index device, pooled sensitivity and specificity will be calculated using a random effect model and presented in forest plots. DISCUSSION: The findings of our review will provide a comprehensive synthesis of the diagnostic accuracy of available digital technologies capable for diagnosing AF. Thus, this review will aid in the identification of which devices could be further trialed and implemented, particularly in a LMIC setting, to improve the early diagnosis of AF. TRIAL REGISTRATION: Systematic review registration: PROSPERO registration number is CRD42021290542. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290542.

Spontaneous Coronary Artery Dissection in a Male Patient With Fibromuscular Dysplasia.

A male patient presented with cardiac arrest attributed to anterior ST-segment elevation myocardial infarction from type 1 spontaneous coronary artery dissection. Subsequent imaging confirmed fibromuscular dysplasia in noncoronary arterial segments. The patient was started on guideline-directed medical therapy and referred to cardiac rehabilitation, showing substantial improvements in clinical status. With greater awareness and advancements in imaging, spontaneous coronary artery dissection has been more frequently recognized, and although as many as 81% to 92% of all cases occur in female patients, it can be seen among men, as well. Adjunctive imaging for arteriopathies may help establish the diagnosis for equivocal causes of acute coronary syndrome in women and men.

Maternal childhood emotional abuse increases cardiovascular responses to children's emotional facial expressions.

Parents with a history of childhood maltreatment may be more likely to respond inadequately to their child's emotional cues, such as crying or screaming, due to previous exposure to prolonged stress. While studies have investigated parents' physiological reactions to their children's vocal expressions of emotions, less attention has been given to their responses when perceiving children's facial expressions of emotions. The present study aimed to determine if viewing facial expressions of emotions in children induces cardiovascular changes in mothers (hypo- or hyper-arousal) and whether these differ as a function of childhood maltreatment. A total of 104 mothers took part in this study. Their experiences of childhood maltreatment were measured using the Childhood Trauma Questionnaire (CTQ). Participants' electrocardiogram signals were recorded during a task in which they viewed a landscape video (baseline) and images of children's faces expressing different intensities of emotion. Heart rate variability (HRV) was extracted from the recordings as an indicator of parasympathetic reactivity. Participants presented two profiles: one group of mothers had a decreased HRV when presented with images of children's facial expressions of emotions, while the other group's HRV increased. However, HRV change was not significantly different between the two groups. The interaction between HRV groups and the severity of maltreatment experienced was marginal. Results suggested that experiences of childhood emotional abuse were more common in mothers whose HRV increased during the task. Therefore, more severe childhood experiences of emotional abuse could be associated with mothers' cardiovascular hyperreactivity. Maladaptive cardiovascular responses could have a ripple effect, influencing how mothers react to their children's facial expressions of emotions. That reaction could affect the quality of their interaction with their child. Providing interventions that help parents regulate their physiological and behavioral responses to stress might be helpful, especially if they have experienced childhood maltreatment.

Molecular Autopsy With Banked Cord Blood Reveals Brugada Syndrome in Past Sudden Death Case.

Molecular autopsy has recently been gaining attention as a means of postmortem diagnosis; however, it is usually performed using the victim's blood sample at the time of death. Here, we report the first case of a deceased infant with Brugada syndrome whose diagnosis was made with banked cord blood. A seemingly healthy 1-year-old male infant collapsed while having a fever; this collapse was witnessed by his mother. Despite cardiopulmonary resuscitation, he died of ventricular fibrillation. No abnormalities of cardiac structure were identified on autopsy. Genomic samples were not stored at the time because of a lack of suspicion for familial arrhythmia. Five years later, his sister showed Brugada electrocardiogram pattern while febrile from Kawasaki disease. Their father showed a spontaneous type 1 Brugada electrocardiogram pattern. A heterozygous SCN5A p.R893C variant was found by genetic testing in the proband's father and sister. Furthermore, the proband's genetic testing was performed using his banked cord blood, which identified the same variant. Family history of Brugada syndrome with an SCN5A-R893C variant and clinical evidence led to a postmortem diagnosis of Brugada syndrome in the proband. Identification of this variant in this case later contributed to verifying SCN5A-R893C as a pathogenic variant through data accumulation. Banked cord blood may prove useful for conducting molecular autopsies in previously undiagnosed cases of sudden death in which genomic samples were not stored.

Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis.

The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagnosis. In this paper, we propose a novel automatic CAD diagnosis approach. The proposed approach introduces a customized preprocessing method to obtain clear electrocardiograms (ECGs) from individual TET reports. Additionally, it presents TETDiaNet, a novel neural network designed to explore the temporal relationships within TET ECGs. Central to TETDiaNet is the TETDia block, which mimics clinicians' diagnostic processes to extract essential diagnostic information. This block encompasses an intra-state contextual learning module and an inter-state contextual learning module, modeling the temporal relationships within a single state and between states, respectively. These two modules help the TETDia block to capture effective diagnosis information by exploring the temporal relationships within TET ECGs. Furthermore, we establish a new TET dataset named TET4CAD for CAD diagnosis. It contains simplified TET reports for 192 CAD patients and 224 non-CAD patients, and each patient undergoes coronary angiography for labeling. Experimental results on TET4CAD underscore the superior performance of the proposed approach, highlighting the discriminative value of the temporal relationships within TET ECGs for CAD diagnosis.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture.

The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.

Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression.

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves ACCrobust scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate.

Legionella Serositis: A Rare Presentation.

BACKGROUND: Legionella has a higher prevalence in India than in the world. Legionaries' disease most commonly involves the lungs but because of increased awareness, extrapulmonary manifestations are also being diagnosed more frequently. CASE DESCRIPTION: We present a case of a young female with acute onset of fever and chest pain. On initial investigation, an electrocardiogram (ECG) reported widespread pulse rate (PR) depression suggestive of pericarditis which was confirmed by ECG. High-resolution computed tomography (HRCT) thorax suggested mild bilateral pleural effusion with normal lung parenchyma. elevated erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) added to the diagnosis of serositis. Serological study for atypical organisms was remarkable for positive immunoglobulin M (IgM) for Legionella. She was treated with a high dose of steroids and azithromycin successfully. CONCLUSION: Isolated extrapulmonary presentation of legionaries disease is often overlooked and is common. So it should be always included in the diagnostic armamentarium as treatment is highly efficacious if started early.

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis.

Drug-induced convulsions are a major challenge to drug development because of the lack of reliable biomarkers. Using machine learning, our previous research indicated the potential use of an index derived from heart rate variability (HRV) analysis in non-human primates as a biomarker for convulsions induced by GABAA receptor antagonists. The present study aimed to explore the application of this methodology to other convulsants and evaluate its specificity by testing non-convulsants that affect the autonomic nervous system. Telemetry-implanted males were administered various convulsants (4-aminopyridine, bupropion, kainic acid, and ranolazine) at different doses. Electrocardiogram data gathered during the pre-dose period were employed as training data, and the convulsive potential was evaluated using HRV and multivariate statistical process control. Our findings show that the Q-statistic-derived convulsive index for 4-aminopyridine increased at doses lower than that of the convulsive dose. Increases were also observed for kainic acid and ranolazine at convulsive doses, whereas bupropion did not change the index up to the highest dose (1/3 of the convulsive dose). When the same analysis was applied to non-convulsants (atropine, atenolol, and clonidine), an increase in the index was noted. Thus, the index elevation appeared to correlate with or even predict alterations in autonomic nerve activity indices, implying that this method might be regarded as a sensitive index to fluctuations within the autonomic nervous system. Despite potential false positives, this methodology offers valuable insights into predicting drug-induced convulsions when the pharmacological profile is used to carefully choose a compound.