Brugada syndrome is a genetically determined familial disease with autosomal dominant transmission and variable penetrance, conferring a predisposition to sudden cardiac death due to ventricular arrhythmias. The syndrome is characterized by a typical electrocardiographic pattern in the right precordial leads. This article will focus on the new electrocardiographic features recently agreed on by expert consensus helping to identify this infequent electrocardiographic pattern.
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http://dx.doi.org/10.2174/1573403x10666140514101546 | DOI Listing |
BMC Med Inform Decis Mak
January 2025
Department of Biomedical Engineering, National Defense Medical Center, Taiwan, No.161, Sec.6, Minchiuan E. Rd., Neihu Dist, Taipei, 11490, Taiwan.
Background: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum.
View Article and Find Full Text PDFHeart
January 2025
Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
Background: Cardiac sarcoidosis (CS) is a chronic inflammatory disease characterised by non-caseating granulomas, while arrhythmogenic cardiomyopathy (ACM) is a genetic condition mainly affecting desmosomal proteins. The coexistence of CS and genetic variants associated with ACM is not well understood, creating challenges in diagnosis and management. This study aimed to describe the clinical, imaging and genetic features of patients with both conditions.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, Ancona, 60131, Italy.
Background: Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.
View Article and Find Full Text PDFArtif Intell Med
February 2025
Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, China; Clinical Medical Research Center of Imaging in Liaoning Province, Shenyang, China.
Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential indicator for evaluating left ventricular function in clinical practice, the current echocardiography-based evaluation method is not avaliable in primary care and difficult to achieve real-time monitoring capabilities for cardiac dysfunction.
View Article and Find Full Text PDFTraffic Inj Prev
January 2025
China Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd, Chongqing, China.
Objective: This study aimed to analyze the influence of different tunnel reinforcement measures on drivers and to evaluate the associated driving safety risks.
Methods: Experimental data of driving behavior and physiological response were collected under different driving simulation scenarios, such as cover arch erection, corrugated steel, grouting, Steel strips, and fire; an evaluation index system was established based on electrocardiographic (ECG), electrodermal activity(EDA), standard deviation of speed (SDSP), Steering Entropy(SE), standard deviation of lateral position (SDLP) and other indices. The classical domain rank standard of each evaluation index was divided using K-Means algorithm, and a synthetic evaluation matter-element model was established to comprehensively evaluate and analyze the safety risks of each scenario.
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