Key result
The ECG-Convolution-Vision Transformer Network (ECVT-Net) achieved an accuracy of 98.88% for inter-patient congestive heart failure detection from ECGs.
Why the study?
Existing machine learning methods for automatic CHF detection focus on intra-patient schemes and lack performance evaluation under noise, which does not meet clinical application needs.
Does the ECVT-Net accurately detect congestive heart failure from ECGs in an inter-patient scheme?
Comparison
ECVT-Net model vs noise-added conditions
Design
Machine learning model development and validation study
Authors
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May support automated ECG screening for CHF; leaves open prospective validation before clinical adoption.
Does the ECVT-Net accurately detect congestive heart failure from ECGs in an inter-patient scheme?
The ECVT-Net algorithm provides a highly accurate (98.88%) and noise-robust method for automated detection of congestive heart failure from ECGs.
Liu et al. (2022) studied Congestive heart failure. ECG-Convolution-Vision Transformer Network (ECVT-Net) was evaluated on Accuracy for inter-patient scheme. The ECG-Convolution-Vision Transformer Network (ECVT-Net) achieved an accuracy of 98.88% for inter-patient congestive heart failure detection from ECGs.
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