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Beyond arrhythmias in the ECG: Is there any correlation between QT interval and stroke subtype and severity?
J Clin Neurosci
January 2025
Comprehensive Centre for Stroke Care, Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala 695011, India. Electronic address:
Background: The QT interval in ECG is susceptible to autonomic fluctuations, a known occurrence in acute ischemic stroke (AIS). Previous research has highlighted QT interval changes between ischemic and haemorrhagic strokes. However, there is scarce literature on the differential effect of AIS subtypes on QT interval.
View Article and Find Full Text PDFSmart furniture using radar technology for cardiac health monitoring.
Sci Rep
January 2025
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
The integration of radar technology into smart furniture represents a practical approach to health monitoring, circumventing the concerns regarding user convenience and privacy often encountered by conventional smart home systems. Radar technology's inherent non-contact methodology, privacy-preserving features, adaptability to diverse environmental conditions, and high precision characteristics collectively establish it a compelling alternative for comprehensive health monitoring within domestic environments. In this paper, we introduce a millimeter (mm)-wave radar system positioned strategically behind a seat, featuring an algorithm capable of identifying unique cardiac waveform patterns for healthy subjects.
View Article and Find Full Text PDFDeep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.
Methods: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study.
Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.
Sensors (Basel)
December 2024
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN).
View Article and Find Full Text PDFThe Prognostic Significance of QTc Prolongation in Lead aVR in Patients with Acute Coronary Syndrome with ST Elevation or Depression.
Medicina (Kaunas)
December 2024
Department of Emergency Medicine, Faculty of Medicine, Gaziantep University, Gaziantep 27410, Turkey.
: In patients with acute coronary syndrome, electrocardiographic parameters, including ST elevation in lead aVR (aVR-STE), ST depression (aVR-STD), and QTc prolongation, are crucial. This study aims to show the predictive value of a longer QTc in emergency department patients with acute coronary syndrome and ≥1 mm ST elevation or depression in the aVR lead in electrocardiography. : A retrospective analysis was conducted on 1273 patients admitted to the emergency department with a preliminary diagnosis of acute coronary syndrome between 2020 and 2023.
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