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Case studies: cardiac dysrhythmias
Published in William H. Bush, Karl N. Krecke, Bernard F. King, Michael A. Bettmann, Radiology Life Support (Rad-LS), 2017
Thomas F. Bugliosi, William H. Bush, Geoffrey S. Ferguson
What is your diagnosis?Normal sinus rhythmPremature atrial contraction (PAC)Normal sinus rhythm with premature ventricular contractions (PVC)Ventricular fibrillation
A review of arrhythmia detection based on electrocardiogram with artificial intelligence
Published in Expert Review of Medical Devices, 2022
Jinlei Liu, Zhiyuan Li, Yanrui Jin, Yunqing Liu, Chengliang Liu, Liqun Zhao, Xiaojun Chen
According to the American Heart Association statistics, cardiovascular diseases (CVDs) have become the primary cause of death in the world [1]. Due to irregular and unhealthy lifestyles, patients with CVDs tend to become younger. The early symptoms of most CVDs are irregular heartbeats, also known as arrhythmia. Arrhythmia is generated by the disordered electrical activity of the heart, and some arrhythmia such as ventricular tachycardia (VT) and ventricular fibrillation (VF) can be life-threatening [2]. In addition, atrial fibrillation (AF), atrial flutter (AFL), premature ventricular contraction (PVC), premature atrial contraction (PAC), paroxysmal supraventricular tachycardia (PSVT), and bradycardia are also common types of arrhythmia [3]. Therefore, rapid detection and accurate diagnosis of cardiac arrhythmia are particularly essential.
Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
P. Lampreave, G. Jimenez-Perez, I. Sanz, A. Gomez, O. Camara
ECG data from the PhysioNet/CinC 2020 Challenge (Perez Alday et al. 2020) was used, specifically the first released data batch corresponding to the China Physiological Signal Challenge 2018 (Liu et al. 2018). The database consists of 12-lead ECG recordings of 6877 patients (46.21% female) collected from 11 hospitals. The recordings were sampled at 500 Hz, lasting between six and sixty seconds, and were classified into 9 possible cardiomyopathies, including Atrial fibrillation (AF), First-degree atrioventricular block (I-AVB), Left bundle branch block (LBBB), Right bundle branch block (RBBB), Premature atrial contraction (PAC), Premature ventricular contraction (PVC), ST-segment depression (STD) and ST-segment elevation (STE).