Time-Frequency Domain Modified Vision Transformer Model for Detection of Atrial Fibrillation using Multi-lead ECG Signals
Author:
Affiliation:
1. BITS Pilani,Department of EEE,Hyderabad,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10067865/10067872/10068090.pdf?arnumber=10068090
Reference20 articles.
1. Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier
2. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
3. Time–Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals
4. AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals
5. Attention is all you need;vaswani;Advances in neural information processing systems,2017
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2. Atrial fibrillation detection with signal decomposition and dilated residual neural network;Physiological Measurement;2023-10-01
3. A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG;Diagnostics;2023-07-21
4. Advancing ECG Biometrics Through Vision Transformers: A Confidence-Driven Approach;IEEE Access;2023
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