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| http://dx.doi.org/10.1016/j.hrcr.2021.07.007 | DOI Listing |
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The Prevalence and Predictors of Atrioventricular Blocks in Syrian Patients Reporting to the Emergency Department During the Ongoing Conflict: A Cross-Sectional Study.
J Cardiovasc Electrophysiol
January 2025
Faculty of Medicine, University of Damascus, Damascus, Syria.
Background: Atrioventricular block (AVB) is a cardiac conduction disorder that can lead to significant clinical outcomes, particularly in resource-limited and conflict-affected regions. In Syria, healthcare infrastructure has been severely impacted by ongoing conflict, potentially affecting the prevalence and management of AVB.
Methods: We conducted a cross-sectional study at Tishreen University Hospital in Latakia, Syria, with patients > 40 who presented to the emergency department (ED) from June 1 to August 1, 2024.
Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram.
NPJ Digit Med
January 2025
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.
View Article and Find Full Text PDFTinyML and edge intelligence applications in cardiovascular disease: A survey.
Comput Biol Med
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Department of Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Electronic address:
Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks.
View Article and Find Full Text PDFHolter electrocardiography findings in Fukuyama congenital muscular dystrophy.
Neuromuscul Disord
January 2025
Department of Child Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan; Translational Medical Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan.
Fukuyama congenital muscular dystrophy (FCMD) is the second most common childhood-onset muscular dystrophy in Japan. However, only a few comprehensive studies have investigated cardiac complications associated with FCMD, with none on arrhythmias. The present study evaluated 78 Holter electrocardiograms from 15 patients with FCMD.
View Article and Find Full Text PDFBioinspired Tunable Helical Fiber-Shaped Strain Sensor with Sensing Controllability for the Rehabilitation of Hemiplegic Patients.
ACS Appl Mater Interfaces
January 2025
Textile and Clothing College, Qingdao University, Qingdao 266071, China.
Fiber-based strain sensors, as wearable integrated devices, have shown substantial promise in health monitoring. However, current sensors suffer from limited tunability in sensing performance, constraining their adaptability to diverse human motions. Drawing inspiration from the structure of the spiranthes sinensis, this study introduces a unique textile wrapping technique to coil flexible silver (Ag) yarn around the surface of multifilament elastic polyurethane (PU), thereby constructing a helical structure fiber-based strain sensor.
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