… Knowledge distillation was utilized in this paper to propose a method for bridging the gap …
-lead ECG signals and the arrhythmia classification model with single-lead ECG signals by …

… This study proposes a deep learning model, trained using knowledge distillation, based on
photoplethysmographic (PPG) and electrocardiogram (ECG) signals to estimate systolic and …

… Therefore, the denoising of the ECG signal is very important for the subsequent accurate …
This paper explores the application of knowledge distillation to the ECG denoising task and …

… knowledge distillation (DKD) method, which purposefully transfers specific knowledge in
knowledge distillation … affect the characteristics and quality of the ECG signals. Most automatic …

… classification effect, so it is particularly important to denoise the ECG reasonably. Currently,
… in filtering ECG signals [7]. Therefore, this paper focuses on filtering ECG signals using …

… [7] used LSTM based temporal model approach for sleep staging on explicitly extracted
132 HRV features from ECG signal and achieved 77% accuracy. N. Sridhar et al. …

… It can automatically represent complex feature of ECG signals via hierarchical non-linear
fitting. Hence the model performance can be largely improved. Acharya UR [10] proposed a …

… The advancement of the Internet of Things (IoT) makes real-time capturing of ECG signals …
knowledge distillation method to further improve the performance of LRH-Net. While distilling …

… ECG signal X i to a fixed-size representation (in x r e s n e t 1 d 101 [42] D = 2048 ). Because
ECG signals are time-series signals… propose a new knowledge distillation objective that can …

… We propose a new ECG balanced self-supervised knowledge distillation framework BSKDECG
to transfer valuable knowledge in multi-lead ECG signals to the student model based on …