A5035980027- ECG Monitoring and Analysis
- EEG and Brain-Computer Interfaces
- Anomaly Detection Techniques and Applications
- Organic Electronics and Photovoltaics
- COVID-19 diagnosis using AI
- Advanced Sensor and Energy Harvesting Materials
- Green IT and Sustainability
- Heart Rate Variability and Autonomic Control
- Machine Learning in Materials Science
- Cardiac electrophysiology and arrhythmias
- Voice and Speech Disorders
- Obstructive Sleep Apnea Research
VinUniversity
2022-2023
The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising need to develop novel tools provide rapid and cost-effective screening diagnosis. Clinical reports indicated that infection may cause cardiac injury, electrocardiograms (ECG) serve as a diagnostic biomarker for COVID-19. This study aims utilize ECG signals detect automatically. We propose method extract from paper records, which are then fed into one-dimensional convolution neural network (1D-CNN)...
Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, cardiomyopathy (enlargement the muscle tissue heart). The electrocardiogram (ECG) plays critical role in identifying SA since it might reveal abnormal cardiac activity. Recent research on ECG-based detection has focused feature engineering techniques that extract specific characteristics from multiple-lead ECG...
An increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). 12-lead ECG widely used in clinical practice and most current research. However, using fewer leads can make more pervasive as it be integrated portable or wearable devices. This article introduces two novel techniques to improve performance deep learning system 3-lead classification,...