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.

Heart rate variability and vasovagal tone index in brachycephalic dogs.

Brachycephalic breeds of dogs, most of which show signs of the brachycephalic syndrome may have greater parasympathetic stimulation than other breeds, leading to higher values of heart rate variability and vagal tone index. The aim of this study was to establish a computerized electrocardiographic study and an assessment of the vagus sympathetic balance through heart rate variability and vagal tone index of five brachycephalic breeds compared to mesocephalic dogs. Sixty dogs were used, divided into groups made up of Boxers, English Bulldogs, French Bulldogs, Pugs, Shih-Tzu and no defined breed mesocephalic dogs. Statistical analysis was carried out using the Shapiro-Wilk test, Kruskal-Wallis and Dunn's test or ANOVA and Bonferroni (p<0.05). In the evaluation of vagal sympathetic balance among all the dogs, there was a negative correlation between heart rate and HRV 10RR (r = - 0.7678; p < 0.0001), HRV 20RR (r = - 0.8548, p < 0.0001) and VVTI (r = - 0.2770; p = 0.0321). It can therefore be concluded that the dog's breed and morphology did not alter its electrocardiographic parameters or heart rate variability. The vagal tone index, which in other studies differed in brachycephalic dogs, showed no difference when compared separately in brachycephalic breeds.

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.

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcare expenditures, with anticipated escalation in the future. It is essential to classify HF patients into three groups based on their ejection fraction: reduced (HFrEF), mid-range (HFmEF), and preserved (HFpEF), such as for diagnosis, risk assessment, treatment choice, and the ongoing monitoring of heart failure. Nevertheless, obtaining a definitive prediction poses challenges, requiring the reliance on echocardiography. On the contrary, an electrocardiogram (ECG) provides a straightforward, quick, continuous assessment of the patient's cardiac rhythm, serving as a cost-effective adjunct to echocardiography. In this research, we evaluate several machine learning (ML)-based classification models, such as K-nearest neighbors (KNN), neural networks (NN), support vector machines (SVM), and decision trees (TREE), to classify left ventricular ejection fraction (LVEF) for three categories of HF patients at hourly intervals, using 24-hour ECG recordings. Information from heterogeneous group of 303 heart failure patients, encompassing HFpEF, HFmEF, or HFrEF classes, was acquired from a multicenter dataset involving both American and Greek populations. Features extracted from ECG data were employed to train the aforementioned ML classification models, with the training occurring in one-hour intervals. To optimize the classification of LVEF levels in coronary artery disease (CAD) patients, a nested cross-validation approach was employed for hyperparameter tuning. HF patients were best classified using TREE and KNN models, with an overall accuracy of 91.2% and 90.9%, and average area under the curve of the receiver operating characteristics (AUROC) of 0.98, and 0.99, respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm were the ones that contributed to the highest classification accuracy. The results pave the way for creating an automated screening system tailored for patients with CAD, utilizing optimal measurement timings aligned with their circadian cycles.

Cold pressor stress effects on cardiac repolarization.

The cold pressor test (CPT) elicits strong cardiovascular reactions via activation of the sympathetic nervous system (SNS), yielding subsequent increases in heart rate (HR) and blood pressure (BP). However, little is known on how exposure to the CPT affects cardiac ventricular repolarization. Twenty-eight healthy males underwent both a bilateral feet CPT and a warm water (WW) control condition on two separate days, one week apart. During pre-stress baseline and stress induction cardiovascular signals (ECG lead II, Finometer BP) were monitored continuously. Salivary cortisol and subjective stress ratings were assessed intermittently. Corrected QT (QTc) interval length and T-wave amplitude (TWA) were assessed for each heartbeat and subsequently aggregated individually over baseline and stress phases, respectively. CPT increases QTc interval length and elevates the TWA. Stress-induced changes in cardiac repolarization are only in part and weakly correlated with cardiovascular and cortisol stress-reactivity. Besides its already well-established effects on cardiovascular, endocrine, and subjective responses, CPT also impacts on cardiac repolarization by elongation of QTc interval length and elevation of TWA. CPT effects on cardiac repolarization share little variance with the other indices of stress reactivity, suggesting a potentially incremental value of this parameter for understanding psychobiological adaptation to acute CPT stress.

An Unusual Case of Delayed-Onset Rituximab-Induced Ventricular Tachycardia: A Case Report.

Cardiac disease associated with cancer treatment is a common adverse effect that is well-treated with appropriate monitoring. However, some cardiac adverse effects with cancer treatment are not well-understood, in particular rituximab-associated ventricular tachycardia. We present the fourth case of rituximab-associated ventricular tachycardia in a patient who is rituximab-naive and who does not have known cardiac disease history. This patient developed non-sustained polymorphic ventricular tachycardia 14 hours after rituximab was started and 6 hours after it was stopped, and after extensive monitoring including a 30-day event monitor, did not develop further significant runs of ventricular tachycardia.

A rare case of pheochromocytoma in a pregnant woman presenting with chest pain: extraordinary management.

BACKGROUND: Pheochromocytoma is rare in pregnant women. It presents as diverse symptoms, including hypertension and sweating. The symptoms of pregnant women with pheochromocytoma and comorbid hypertension often mimic the clinical manifestations of preeclampsia, and these women are often misdiagnosed with preeclampsia. CASE PRESENTATION: In this case, a pregnant woman presented with chest pain as the primary symptom, and a diagnosis of pheochromocytoma was considered after ruling out myocardial ischemia and aortic dissection with the relevant diagnostic tools. This patient then underwent successful surgical resection using a nontraditional management approach, which resulted in a positive clinical outcome. CONCLUSIONS: It is essential to consider pheochromocytoma as a potential cause of chest pain and myocardial infarction-like electrocardiographic changes in pregnant women, even if they do not have a history of hypertension.

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.

Opportunistic atrial fibrillation screening in primary care in Ireland: results of a pilot screening programme.

BACKGROUND: Atrial fibrillation (AF), a common, frequently asymptomatic cardiac arrhythmia, is a major risk factor for stroke. Identification of AF enables effective preventive treatment to be offered, potentially reducing stroke risk by up to two-thirds. There is international consensus that opportunistic AF screening is valuable though uncertainty remains about the optimum screening location and method. Primary care has been identified as a potential location for AF screening using one-lead ECG devices. METHODS: A pilot AF screening programme is in primary care in the south of Ireland. General practitioners (GPs) were recruited from Cork and Kerry. GPs invited patients ≥65 years to undergo AF screening. The screening comprised a one-lead ECG device, Kardia Mobile, blood pressure check and ascertainment of smoking status. Possible AF on one-lead ECG was confirmed with a 12-lead ECG. GPs also recorded information including medical history, current medication and onward referral. The Keele Decision Support tool was used to assess patients for oral anticoagulation (OAC). RESULTS: 3555 eligible patients, attending 52 GPs across 34 GP practices, agreed to undergo screening. 1720 (48%) were female, 1780 (50%) were hypertensive and 285 (8%) were current smokers. On the one-lead ECG, 3282 (92%) were in normal sinus rhythm, 101 (3%) had possible AF and among 124 (4%) the one-lead ECG was unreadable or unclassified. Of the 101 patients with possible AF, 45 (45%) had AF confirmed with 12-lead ECG, an incidence rate of AF of 1.3%. Among the 45 confirmed AF cases, 27 (60%) were commenced on OAC therapy by their GP. CONCLUSION: These findings suggest that AF screening in primary care may prove useful for early detection of AF cases that can be assessed for treatment. One-lead ECG devices may be useful in the detection of paroxysmal AF in this population and setting. Current OAC of AF may be suboptimal.

A Near Miss in the Emergency Department: Atypical Presentation of Acute Coronary Syndrome.

Acute coronary syndrome is an umbrella term encompassing three types of coronary artery disease that affect millions worldwide annually. Despite the availability of diagnostic tests (blood analysis, imaging, electrocardiogram, and screening tools), the diagnosis of myocardial infarction (MI) is still sometimes missed. According to the Centers for Disease Control and Prevention, the reported prevalence of heart disease is higher among males than females, with adults over the age of 75 having the highest prevalence. Typical "heart attack" features include chest pain that feels like pressure or squeezing, pain or discomfort in one or both arms that can radiate to the neck or jaw, shortness of breath, diaphoresis, nausea, vomiting, and lightheadedness. However, there are three subgroups where the typical warning signs do not always present: the elderly, individuals with diabetes, and females. The following is an atypical case presentation of unstable angina and non-ST-elevation MI.