Why the study?
ECG has short monitoring cycles and difficulty detecting burst AF, whereas photoplethysmography is suitable for long-term monitoring but needs effective methods for AF detection.
Does a hybrid CNN-LSTM deep learning model using time-frequency analysis of PPG signals accurately identify atrial fibrillation?
Does a hybrid CNN-LSTM deep learning model using time-frequency analysis of PPG signals accurately identify atrial fibrillation?
A novel deep learning approach using time-frequency analysis of PPG signals demonstrates high accuracy (>98%) for detecting atrial fibrillation, potentially enabling better AF screening via wearable devices.
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May enable wearable AF screening; hypothesis-generating pending prospective clinical validation.
Peng et al. (2020) studied this question.
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