Why the study?
Most existing AF detection methods convert 1D ECG signals into 2D spectrograms to train complex systems, leading to heavy computation and high implementation costs.
Does an end-to-end 1D convolutional neural network improve detection accuracy and reduce network complexity for atrial fibrillation detection from ECG signals compared to existing methods?
Does an end-to-end 1D convolutional neural network improve detection accuracy and reduce network complexity for atrial fibrillation detection from ECG signals compared to existing methods?
A novel 1D convolutional neural network can accurately detect atrial fibrillation from single-lead ECGs with an average F1 score of 78.2% while significantly reducing computational complexity compared to 2D spectrogram-based methods.
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Supports efficient AF detection algorithms; leaves open prospective clinical validation before practice adoption.
Hsieh et al. (2020) studied this question.
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