A novel R-peak detection method based on Long Short-Term Memory (LSTM) networks outperformed traditional rule-based algorithms, with the greatest advantage observed in noisy ECG signals.
An LSTM-based R-peak detection algorithm demonstrates superior performance over traditional rule-based methods, especially in the presence of noise.
Detecting QRS complexes or R-peaks from the electrocardiogram (ECG) is the basis for heart rate determination and heart rate variability analysis. Over the years, multiple different methods have been proposed as solutions to this problem. Vast majority of the proposed methods are traditional rule based algorithms that are vulnerable to noise. We propose a new R-peak detection method that is based on the Long Short-Term Memory (LSTM) network. LSTM networks excel at temporal modelling tasks that include long-term dependencies, making it suitable for ECG analysis. Additionally, we propose data generator for creating noisy ECG data that is used to train the robust R-peak detector. Our initial testing shows that the proposed method outperforms traditional algorithms while the greatest competitive edge is achieved with the noisy ECG signals.
Laitala et al. (Sun,) conducted a other in ECG R-peak detection. LSTM network for R-peak detection vs. Traditional rule-based algorithms was evaluated on R-peak detection performance. A novel R-peak detection method based on Long Short-Term Memory (LSTM) networks outperformed traditional rule-based algorithms, with the greatest advantage observed in noisy ECG signals.