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This document presents a novel R-peak detection method for ECG signals using Long Short-Term Memory (LSTM) networks, which outperforms traditional algorithms, particularly in noisy environments. The study introduces a data generator for creating augmented noisy ECG data to train the LSTM model, enhancing its robustness against various artifacts. The proposed method demonstrates superior performance in detecting R-peaks compared to existing techniques, making it suitable for real-world applications in heart

Author

ebrosa001

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EN