Frozen language model helps ecg zero-shot learning
The electrocardiogram (ECG) is one of the most commonly used non-invasive, convenient
medical monitoring tools that assist in the clinical diagnosis of heart diseases. Recently …
medical monitoring tools that assist in the clinical diagnosis of heart diseases. Recently …
Etp: Learning transferable ecg representations via ecg-text pre-training
In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical,
non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have …
non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have …
[HTML][HTML] ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features
Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart
arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed …
arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed …
Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method
J Qin, Y Xu, ZLC Liu, Z Lu, X Zhang - arXiv preprint arXiv:2306.16950, 2023 - arxiv.org
Feature alignment is the primary means of fusing multimodal data. We propose a feature
alignment method that fully fuses multimodal information, which alternately shifts and …
alignment method that fully fuses multimodal information, which alternately shifts and …
SF-ECG: Source-free intersubject domain adaptation for electrocardiography-based arrhythmia classification
Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in
cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly …
cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly …
Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets
ECG classification or heartbeat classification is an extremely valuable tool in cardiology.
Deep learning-based techniques for the analysis of ECG signals assist human experts in the …
Deep learning-based techniques for the analysis of ECG signals assist human experts in the …
Model Interpretation Considering Both Time and Frequency Axes Given Time Series Data
W Lee, G Kim, J Yu, Y Kim - Applied Sciences, 2022 - mdpi.com
Recently, deep learning-based models have emerged in the medical domain. Although
those models achieve high performance, it is difficult to directly apply them in practice …
those models achieve high performance, it is difficult to directly apply them in practice …
A digital twin enabled wearable device for customized healthcare
Z Zhu, RY Zhong - Digital Twin, 2022 - digitaltwin1.org
Background: The traditional healthcare process centers on the hospital rather than the
individual patient. The demand for continuous monitoring is increasing with the increasing …
individual patient. The demand for continuous monitoring is increasing with the increasing …
Zoom and Shift are All You Need
J Qin - arXiv preprint arXiv:2406.08866, 2024 - arxiv.org
Feature alignment serves as the primary mechanism for fusing multimodal data. We put forth
a feature alignment approach that achieves full integration of multimodal information. This is …
a feature alignment approach that achieves full integration of multimodal information. This is …
Multimodal Deep Learning for Enhanced Arrhythmia Detection Using ECG Time Series and Image Data
Arrhythmia detection has become a more and more important issue now. The traditional
heart rate detection method uses 12 lead electrocardiograms for heart rate detection …
heart rate detection method uses 12 lead electrocardiograms for heart rate detection …