… and diagnosis of Cardiac Arrhythmia. This paper proposes a new transfer learning-based
end to end approach to automate the cardiac arrhythmia classification. The proposed …

… , in this work, we propose using transfer learning from the 2dimensional domain. The image
… These learnt feature maps can be transferred to the ECG domain, if the 1-D ECG signal is …

… The transfer learning is a solution … study is to investigate the ability of transfer learning
method for classification ECG signals into normal, AF, other rhythms and noisy classes. Approach: …

… In this work, we used transfer learning to improve convolutional neural networks (CNN)
trained to classify heart rhythm from a short ECG recording. First, we pretrained CNNs on a large …

… The highlight of this section is the proposed model of transfer learning named ‘CardioNet’
for arrhythmia classification using ECG. The experimental details including datasets used, …

… Due to this, we considered cardiac diseases like arrhythmia, myocardial infarction, and …
from scratch, a transfer learning approach is introduced. Transfer learning is the methodology …

… the functioning of the heart and ECG before implementing it on any machine learning, deep
learning, and transfer learning approach [3–5]. For the diagnosis of heart arrhythmia, the …

… Effectiveness of the proposed method In this paper, the proposed method combined transfer
learning and active learning. With a pre-trained model, informative samples were selected …

… (A) The CAE was pre-trained using the Ajou University Medical Center (AUMC) ECG
dataset, and the CAE weights were employed for transfer learning to initialize the ECG rhythm …

… active learning technique based on transfer learning, … the MIT-BIH Arrhythmia Database to
train a deep learning model to … Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) of …