[HTML][HTML] Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the
coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people …
coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people …
An overview on state-of-the-art electrocardiogram signal processing methods: Traditional to AI-based approaches
VA Ardeti, VR Kolluru, GT Varghese… - Expert Systems with …, 2023 - Elsevier
Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death
globally. Early prediction of CVD's can help in reducing the complications of high-risk …
globally. Early prediction of CVD's can help in reducing the complications of high-risk …
Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …
critical to timely medical treatment to save patients' lives. Routine use of the …
[HTML][HTML] Study of the few-shot learning for ECG classification based on the PTB-XL dataset
The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists
of P, QRS, and T waves. Information provided from the signal based on the intervals and …
of P, QRS, and T waves. Information provided from the signal based on the intervals and …
A novel time representation input based on deep learning for ECG classification
Y Huang, H Li, X Yu - Biomedical Signal Processing and Control, 2023 - Elsevier
Electrocardiogram (ECG) is an important tool used to analyze abnormal heart activity and
assess heart health, especially in remote cardiac health monitoring. Although deep learning …
assess heart health, especially in remote cardiac health monitoring. Although deep learning …
Unsupervised ECG analysis: A review
Electrocardiography is the gold standard technique for detecting abnormal heart conditions.
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …
[HTML][HTML] Using minimum redundancy maximum relevance algorithm to select minimal sets of heart rate variability parameters for atrial fibrillation detection
Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node.
In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete …
In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete …
[HTML][HTML] Learning explainable time-morphology patterns for automatic arrhythmia classification from short single-lead ECGs
H Lee, M Shin - Sensors, 2021 - mdpi.com
Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals
obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily …
obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily …
[HTML][HTML] Automated signal quality assessment of single-lead ecg recordings for early detection of silent atrial fibrillation
Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence
in aging societies, which is associated with a risk for stroke and heart failure. However, early …
in aging societies, which is associated with a risk for stroke and heart failure. However, early …
A holistic overview of artificial intelligence in detection, classification and prediction of atrial fibrillation using electrocardiogram: a systematic review and meta-analysis
Atrial Fibrillation (AF) is the most studied cardiac arrhythmias due to its increasing
prevalence in today's scenario. Application of Artificial Intelligence (AI) for early identification …
prevalence in today's scenario. Application of Artificial Intelligence (AI) for early identification …