Heartbeat-aware convolutional neural network for R-peak detection of wearable device ECG data.
10.12122/j.issn.1673-4254.2022.03.09
- Author:
Hui Xin TAN
1
;
Jie Wei LAI
1
;
Zuo WANG
1
;
Lei JI
2
;
Yi Hang ZHANG
3
;
Jin Liang WANG
3
;
Yu Zhang SONG
4
;
Wei YANG
1
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
2. Information Department, Medical Security Center, Chinese PLA General Hospital, Beijing 100853, China.
3. CardioCloud Medical Technology (Beijing) Co. Ltd, Beijing 100084, China.
4. University of California, Riverside, Riverside 92521, USA.
- Publication Type:Journal Article
- Keywords:
R-peak detection;
convolutional neural network;
heartbeat-aware;
wearable device ECG data
- MeSH:
Algorithms;
Electrocardiography;
Heart Rate;
Neural Networks, Computer;
Signal Processing, Computer-Assisted;
Wearable Electronic Devices
- From:
Journal of Southern Medical University
2022;42(3):375-383
- CountryChina
- Language:Chinese
-
Abstract:
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.
