Focus on ECGs

Congenital short QT syndrome is a very low prevalence inherited primary arrhythmia syndrome first reported in 2000 by Gussak et al., who described two families with a short QT interval, syncope, and sudden cardiac death. In 2004, Ramon Brugada et al. identified the first genetic type of this entity. To date, a total of nine genotypes have been described. The diagnosis is easy from the electrocardiogram (ECG), not only due to the short QT duration, but also based on other aspects covered in this review. During 24-h Holter monitoring, paroxysmal atrial fibrillation spontaneously converting to sinus rhythm may be found...

ETHNOPHARMACOLOGICAL RELEVANCE: Green tea (Camellia sinensis) is a popular beverage consumed all over the world due to its health benefits. Many of these beneficial effects of green tea are attributed to polyphenols, particularly catechins. AIM OF THE STUDY: The present study focuses on underlying anti-platelet aggregation, anti-thrombotic, and anti-lipidemic molecular mechanisms of green tea in South Indian smokers. MATERIALS AND METHODS: We selected 120 South Indian male volunteers for this study to collect the blood and categorised them into four groups; control group individuals (Controls), smokers, healthy control individuals consuming green tea, and smokers consuming green tea...

In recent years, the development and use of various devices for the screening of atrial fibrillation (AF) have significantly increased. Such devices include 12-lead electrocardiogram (ECG), photoplethysmography systems, and single-lead ECG and ECG patches. This review outlines several studies that have focused on the feasibility and efficacy of such devices for AF screening, and summarizes the risks and benefits involved in the initiation of anticoagulant therapy after early detection of AF. We also describe several ongoing trials on unresolved issues associated with AF screening...

Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models' outputs remains a significant challenge. This is crucial for fostering the development of trusted models and facilitating domain expert validation, thereby empowering users and domain experts to utilize them confidently in high-risk decision-making contexts (e...

BACKGROUND: Electrocardiography (ECG) interpretation is a fundamental skill for medical students and practicing medical professionals. Recognizing ECG pathologies promptly allows for quick intervention, especially in acute settings where urgent care is needed. However, many medical students find ECG interpretation and understanding of the underlying pathology challenging, with teaching methods varying greatly. OBJECTIVE: This study involved the development of novel animations demonstrating the passage of electrical activity for well-described cardiac pathologies and showcased them alongside the corresponding live ECG traces during a web-based tutorial for final-year medical students...

BACKGROUND: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias. OBJECTIVE: With the advancement of deep learning, end-to-end ECG classification models based on neural networks have been developed. However, deeper network layers lead to gradient vanishing. Moreover, different channels and periods of an ECG signal hold varying significance for identifying different types of ECG abnormalities...

BACKGROUND: Corynebacterium diphtheriae infection, causing diphtheria, is a public health concern, particularly in developing nations like Pakistan. Despite immunization efforts, recent outbreaks since 2022 have emphasized the continuing threat. This study focuses on describing the clinical characteristics of children with diphtheria-induced myocarditis and exploring the association between early cardiac abnormalities, future fatality rates, and contributing factors. METHODS: A one-year cross-sectional study was undertaken at Lady Reading Hospital MTI Peshawar, encompassing 73 pediatric patients diagnosed with diphtheria-associated myocarditis...

Emotion affects postural control during quiet standing. Emotional states can be defined as two-dimensional models comprising valence (pleasant/unpleasant) and arousal (aroused/calm). Most previous studies have investigated the effects of valence on postural control without considering arousal. In addition, studies have focused on the center of pressure (COP) trajectory to examine emotional effects on the quiet standing control; however, the relationship between neuromuscular mechanisms and the emotionally affected quiet standing control is largely unknown...

Previous works have focused on enhancing the tensile properties, mechanical flexibility, biocompatibility, and biodegradability of wearable devices for real-time and continuous health management. Silk proteins, including silk fibroin (SF) and sericin, show great advantages in wearable devices due to their natural biodegradability, excellent biocompatibility, and low fabrication cost. Moreover, these silk proteins possess great potential for functionalization and are being explored as promising candidates for multifunctional wearable devices with sensory capabilities and therapeutic purposes...

Arrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models...

BACKGROUND: Understanding the factors that contribute to diagnostic errors is critical if we are to correct or prevent them. Some scholars influenced by the default interventionist dual-process theory of cognition (dual-process theory) emphasise a narrow focus on individual clinician's faulty reasoning as a significant contributor. In this paper, we examine the validity of claims that dual process theory is a key to error reduction. METHODS: We examined the relationship between a clinical experience (staff and resident physicians) and viewing time on accuracy for categorising chest X-rays (CXRs) and electrocardiograms (ECGs)...

The pathophysiology of heart failure with preserved ejection fraction (HFpEF) driven by lipotoxicity is incompletely understood. Given the urgent need for animal models that accurately mimic cardio-metabolic HFpEF, a hyperlipidemia-induced murine model was developed by reverse engineering phenotypes seen in HFpEF patients. This model aimed to investigate HFpEF, focusing on the interplay between lipotoxicity and metabolic syndrome. Hyperlipidemia was induced in wild-type (WT) mice on a 129J strain background through bi-weekly intraperitoneal injections of poloxamer-407 (P-407), a block co-polymer that blocks lipoprotein lipase, combined with a single intravenous injection of adeno-associated virus 9-cardiac troponin T-low-density lipoprotein receptor (AAV9-cTnT-LDLR)...

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring...

Many studies have examined the effects of meditation practice focused on the normal breath on vagal tone with mixed results. Heart Rhythm Meditation (HRM) is a unique meditation form that engages in the deep slow full breath, and puts the focus of attention on the heart. This form of breathing likely stimulates the vagus nerve with greater intensity. The purpose of this study was (a) to examine how the practice of HRM affects vagal activity as measured by heart rate variability (HRV); and (b) to examine how it affects participants' well-being...

INTRODUCTION: The use of electrocardiograms (ECGs) is widespread among emergency room (ER) nurses for diagnosis and triage, making it crucial for them to have the appropriate level of competency in interpreting ECGs. This can lead to better healthcare and patient outcomes. OBJECTIVES: This study aims to assess the competency level of emergency nurses in Palestine in interpreting normal ECG and certain cardiac arrhythmias, and to explore the association between socio-demographic characteristics and their ECG interpretation competency...

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks...

Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct...

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification...

BACKGROUND: The heartbeat evoked potential (HEP) is a brain response time-locked to the heartbeat and a potential marker of interoceptive processing. The insula and dorsal anterior cingulate cortex (dACC) are brain regions that may be involved in generating the HEP. Low-intensity focused ultrasound (LIFU) is a non-invasive neuromodulation technique that can selectively target sub-regions of the insula and dACC to better understand their contributions to the HEP. OBJECTIVE: Proof-of-concept study to determine whether LIFU modulation of the anterior insula (AI), posterior insula (PI), and dACC influences the HEP...

This study aims to investigate the relationship between different phases of bipolar disorder (depressive, manic, and euthymic) and myocardial deformation, assessed by echocardiography, compared to healthy controls. It seeks to elucidate whether these phases of bipolar disorder are associated with different myocardial strain patterns, thus contributing to the understanding of cardiovascular implications in bipolar disorder. A cross-sectional design was employed at Dursun Odabaş Medical Centre, Psychiatry Clinic of Van Yüzüncü Yl University...