ECG Challenge

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN-LSTM were proposed...

Focal atrial tachycardia (FAT) is an organized atrial rhythm >100 beats per minute initiated from a discrete origin and spreading over both atria in a centrifugal pattern. The arrhythmia may be sustained or incessant. Dynamic forms with recurrent interruptions and reinitiating may be frequent. In this report, we present a 36-year-old man who came to the emergency room complaining of palpitation and shortness of breath. All laboratory evaluations were normal. With an initial electrocardiogram (ECG) the patient was admitted with the initial diagnosis of atrial flutter...

BACKGROUND: The potential of utilizing artificial intelligence with electrocardiography (ECG) for initial screening of aortic dissection (AD) is promising. However, achieving a high positive predictive rate (PPR) remains challenging. METHODS AND RESULTS: This retrospective analysis of a single-center, prospective cohort study (Shinken Database 2010-2017, N = 19,170) used digital 12-lead ECGs from initial patient visits. We assessed a convolutional neural network (CNN) model's performance for AD detection with eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs via five-fold cross-validation...

In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance...

INTRODUCTION: Autonomic dysfunction (AuD) is a significant clinical challenge in patients with Dementia with Lewy Bodies (DLB). Manifestations of AuD such as orthostatic hypotension (OH) is associated with falls and decreased quality of life. Cardiac autonomic denervation is an early phenomenon in DLB and a potential contributor to OH. This retrospective study was undertaken to explore whether routine ECG tracings could be used to identify signs of autonomic dysfunction in DLB. METHODS: 18 patients with DLB and 18 age-matched patients with Alzheimer's disease (AD) were included...

Background: The prevalence of long-COVID (LC) presents a significant challenge to healthcare systems globally. There are still some discrepancies on the role of sex as an independent risk factor of LC complications. Thus, we aimed to determine the differences in clinical and cardiovascular complications between males and females without comorbidities after COVID-19. Methods: Clinical data on the course of the disease with the accompanying symptoms and post-COVID-19 symptoms were compiled from both male and female subjects with a minimum 12-week interval after COVID-19 recovery...

In the increasing number of medical education topics taught with virtual reality (VR), the prehospital management of ST-segment elevation myocardial infarction (STEMI) had not been considered. This article proposes an implemented VR system for STEMI training and introduces it in an institutional course addressed to emergency nurses and case manager (CM) doctors. The system comprises three different applications to, respectively, allow (a) the course instructor to control the conditions of the virtual patient, (b) the CM to communicate with the nurse in the virtual field and receive from him/her the patient's parameters and electrocardiogram, and (c) the nurse to interact with the patient in the immersive VR scenario...

BACKGROUND: A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO2 , which allows for research on how patients demonstrate different responses to CPR. The aim of this review is to give an overview of methodological challenges and opportunities in using defibrillator data for research. METHODS: The successful collection of defibrillator files has several challenges...

Bradycardia, renal failure, atrioventricular (AV) block, shock, and hyperkalemia (BRASH) syndrome is a rare clinical entity that poses challenges for healthcare practitioners. It is characterized by bradycardia, renal failure, atrioventricular (AV) obstruction, shock, and hyperkalemia. This case is an interesting instance of BRASH syndrome in the setting of COVID-19 infection and end-stage renal disease (ESRD). Initial laboratory results revealed macrocytic anemia, renal dysfunction, acidosis, and mild hyponatremia, along with hyperkalemia...

BACKGROUND: Diagnosis of pericarditis may be challenging, since not all patients meet conventional criteria. An overlooked diagnosis implies longer course of symptoms and increased risk of recurrences. C-reactive protein(CRP), widely used as inflammation marker, has some limitations. This study aimed to assess usefulness and prognostic value of INFLA-score, a validated index assessing low-grade inflammation, in the definite diagnosis of pericarditis. METHODS: Patients with suspected pericarditis were included...

Enteric fever typically displays symptoms like high fever, abdominal pain, constipation, and headaches, primarily affecting the digestive system. While it is commonly seen as a gastrointestinal infection, it can also lead to rare but significant cardiovascular issues. There have been only a few reported cases of enteric fever causing heart manifestations. We present a case of a young male with enteric fever-induced myocarditis, which, due to its rarity, can be challenging to diagnose and is essentially a diagnosis of exclusion...

INTRODUCTION: Precise electrocardiographic localization of accessory pathways (AP) can be challenging. Seminal AP localization studies were limited by complexity of algorithms and sample size. We aimed to create a nonalgorithmic method for AP localization based on color-coded maps of AP distribution generated by a web-based application. METHODS: APs were categorized into 19 regions/types based on invasive electrophysiologic mapping. Preexcited QRS complexes were categorized into 6 types based on polarity and notch/slur...

Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical diagnosis. Despite individuals' normal outward appearance and routine activities, subtle indications of these diseases can manifest in the electrocardiogram (ECG) signals, often overlooked by standard interpretation. Current machine learning models have been ineffective in discerning these minor variations due to the irregular and subtle nature of changes in the ECG patterns. This paper uses a novel deep-learning approach to predict slight variations in ECG signals by fine-tuning the learning rate of a deep convolutional neural network...

INTRODUCTION: ChatGPT, developed by OpenAI, represents the cutting-edge in its field with its latest model, GPT-4. Extensive research is currently being conducted in various domains, including cardiovascular diseases, using ChatGPT. Nevertheless, there is a lack of studies addressing the proficiency of GPT-4 in diagnosing conditions based on Electrocardiography (ECG) data. The goal of this study is to evaluate the diagnostic accuracy of GPT-4 when provided with ECG data, and to compare its performance with that of emergency medicine specialists and cardiologists...

INTRODUCTION: SARS-CoV-2 infection affects the cardiopulmonary system in the acute as well as long-term phase. The aim of the present study was to comprehensively assess symptoms and possible long-term impairments 6 and 18 months after hospitalization for severe COVID-19 infection. METHODS: This prospective registry included patients with PCR-confirmed COVID-19 infection requiring hospitalization. Follow-up approximately 6 months post discharge comprised a detailed patient history, clinical examination, transthoracic echocardiography, electrocardiogram, cardiac magnetic resonance imaging (cMRI), chest computed tomography (CT) scan, pulmonary function test (PFT), six-minute walk test (6MWT) and a laboratory panel...

BACKGROUND: Paramedics are expected to record electrocardiograms (ECGs) as part of their clinical assessment; however, it is an extremely difficult skill to learn and understand as it has a high intrinsic cognitive load which can also be challenging to teach effectively. AIMS: This article will explore the use of cognitive load theory to assist in the teaching of ECG interpretation within the context of paramedical education. DESCRIPTION: Cognitive load theory can be useful to aid teaching within complex medical and health science domains including clinical skills teaching...

BACKGROUND: Cardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high-dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire images in a predefined cardiac phase, thus mitigating cardiac motion during image acquisition...

Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evaluate the detector's performance. In this paper, we first notice that statistics of true positive detections rely on researchers' arbitrary selection of time tolerance between QRS detector output and the database reference...

In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability...

This study introduces two models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), designed for abnormality identification using electrocardiogram (ECG) data. Trained on the Telehealth Network of Minas Gerais (TNMG) subset dataset, both models were evaluated for their performance, generalizability capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels...