A5019135393- ECG Monitoring and Analysis
- Digital Imaging in Medicine
- Surgical Simulation and Training
- Phonocardiography and Auscultation Techniques
- Anatomy and Medical Technology
- Cardiac Imaging and Diagnostics
- Healthcare Systems and Public Health
- Non-Invasive Vital Sign Monitoring
- Cardiac electrophysiology and arrhythmias
- Medical Research and Treatments
- EEG and Brain-Computer Interfaces
Xiamen University of Technology
2022
Yuan Ze University
2022
It is still a challenge to develop an electrocardiography (ECG) interpreter based on ECG basic characteristics because of the uncertainty delineation. Based clinical investigation in this study, devices generated interpretations Atrial Fibrillation (AF), Premature Ventricular Contraction (PVC), and (PAC) have high ratios false-positive errors. An interpretation gap exists between cardiologists. This study aimed improve performance AF, PVC, PAC ECGs. first adopted deep learning model...
Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by usage annotations. These annotations included frame-level presence information. 2020, we pixel-wise semantic instruments 4670 images sampled from 25 of CATARACTS training set. The 2020...
Based on the clinical investigation in this study, electrocardiography (ECG) devices generated interpretations atrial fibrillation (AF), premature ventricular contraction (PVC), and (PAC) have high ratios of false-positive errors. An ECG interpretation gap exists between cardiologists. The aim study was to develop an electrocardiogram interpreter improve performance AF, PVC, PAC screening based ECG. In we first adopted a deep learning model delineate features such as P, QRS, T waves 1160...
The QRS complex is one of the most distinctive features ECG signals, and it can play an essential role in understanding heart rate (HR) identifying diseases. Although many deep learning models applied to detection have shown good performances, they are limited ECGs with normal rates. accuracies on for fast beating whose HR > 100 not elucidated. present study aims develop segmentation based Transformer LSTM locate compare performance between traditional algorithms models. Firstly, we...
<sec> <title>BACKGROUND</title> Based on the clinical investigation in this study, electrocardiography (ECG) devices generated interpretations atrial fibrillation (AF), premature ventricular contraction (PVC), and (PAC) have high ratios of false-positive errors. An ECG interpretation gap exists between cardiologists. </sec> <title>OBJECTIVE</title> The aim study was to develop an electrocardiogram interpreter improve performance AF, PVC, PAC screening based ECG. <title>METHODS</title> In we...