Key result
The proposed lightweight heartbeat-aware convolutional neural network achieved 100% sensitivity and over 99.9% true positive rate for R-peak detection in both wearable and routine ECG datasets.
Why the study?
A method was needed for R-peak detection in wearable device ECG data to enable accurate estimation of physiological parameters such as heart rate and heart rate variability.
Does a heartbeat-aware convolutional neural network improve R-peak detection accuracy and real-time performance in wearable device ECG data compared to traditional algorithms?
Does a heartbeat-aware convolutional neural network improve R-peak detection accuracy and real-time performance in wearable device ECG data compared to traditional algorithms?
The proposed heartbeat-aware convolutional neural network achieves highly accurate and real-time R-peak detection in noisy wearable ECG data, outperforming traditional rule-based algorithms.
May enable accurate wearable HR/HRV estimation; leaves open prospective clinical validation before adoption.
OBJECTIVE: To develop a method for R-peak detection of ECG data from wearable devices to allow accurate estimation of the physiological parameters including heart rate and heart rate variability. METHODS: A fully convolutional neural network was applied to predict the R-peak heatmap of ECG data and locate the R-peak positions. The heartbeat-aware (HA) module was introduced to enable the model to learn to predict the heartbeat number and R-peak heatmap simultaneously, thereby improving the capability of the model for extraction of the global context. The R-R interval estimated by the predicted heartbeat number was adopted to calculate the minimum horizontal distance for peak positioning. To achieve real-time R-peak detection on mobile devices, the deep separable convolution was adopted to reduce the number of parameters and the computational complexity of the model. RESULTS: The proposed model was trained only with ECG data from wearable devices. At a tolerance window interval of 150 ms, the proposed method achieved R peak detection sensitivities of 100% for both wearable device ECG dataset and a public dataset (i.e. LUDB), and the true positivity rates exceeded 99.9%. As for the ECG signal of a 10 s duration, the CPU time of the proposed method for R-peak detection was about 23.2 ms. CONCLUSION: The proposed method has good performance for R-peak detection of both wearable device ECG data and routine ECG data and also allows real-time R-peak detection of the ECG data.
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Tan et al. (2022) studied ECG R-peak detection (n=1,256). Heartbeat-aware convolutional neural network (LHA-UNet) vs. Other R-peak detection algorithms (Pan-tompkins, Hamilton, Martinez, Lourenço) was evaluated on R-peak detection sensitivity at 150 ms tolerance. The proposed lightweight heartbeat-aware convolutional neural network achieved 100% sensitivity and over 99.9% true positive rate for R-peak detection in both wearable and routine ECG datasets.
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