Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings
P Zhang, C Ma, Y Sun, G Fan, F Song, Y Feng… - Computers in Biology …, 2021 - Elsevier
Background and objective Atrial fibrillation (AF) is the most common persistent cardiac
arrhythmia in clinical practice, and its accurate screening is of great significance to avoid …
arrhythmia in clinical practice, and its accurate screening is of great significance to avoid …
Automatic modulation classification using DenseNet
S Shaik, S Kirthiga - 2021 5th International Conference on …, 2021 - ieeexplore.ieee.org
Wireless Communication over long distances has revolutionised the way world works,
functions and interacts. Modulation of a signal containing information has made all this …
functions and interacts. Modulation of a signal containing information has made all this …
9 Disease prediction mechanisms on large-scale big data with explainable deep learning models for multi-label classification problems in healthcare
M Ganeshkumar, V Sowmya… - Healthcare Big Data …, 2024 - degruyter.com
Deep learning models have been prominently applied for the automatic detection of various
cardiovascular conditions using ECG signals. The concept of explainable artificial …
cardiovascular conditions using ECG signals. The concept of explainable artificial …
Implementation of Time-Frequency Moments for the Classification of Atrial Fibrillation Sequences Through a Bidirectional Long-Short Term Memory Network
C García-Aquino, D Mújica-Vargas… - … Congress of Telematics …, 2022 - Springer
This article proposes a method to classify atrial fibrillation signals using time-frequency
characteristics through a BiLSTM network. The experiment was performed with the ECG …
characteristics through a BiLSTM network. The experiment was performed with the ECG …
[PDF][PDF] Implementation of Time-Frequency Moments for the Classification of Atrial Fibrillation Sequences Through a Bidirectional Long-Short Term Memory Network
This article proposes a method to classify atrial fibrillation signals using time-frequency
characteristics through a BiLSTM network. The experiment was performed with the ECG …
characteristics through a BiLSTM network. The experiment was performed with the ECG …