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.
目的: 实现可穿戴式心电信号的R峰检测，为准确估计心率、心率变异性等生理参数提供基础。
方法: 采用全卷积网络预测R峰热图，对热图进行峰值定位获得R峰位置。引入心拍感知模块，联合心拍数量预测任务和R峰热图预测任务进行学习，提高卷积网络对全局上下文信息的提取能力。心拍感知模块预测的心拍数量还可估计R-R间期，用作峰值定位的峰间最小水平距离。为满足移动端的实时应用，采用深度可分离卷积减小模型的参数量和计算量。
结果: 实验仅使用可穿戴式心电数据训练模型。测试中定位误差容忍度设置为150 ms时，本文方法在可穿戴式心电数据集和公开数据集LUDB上的R峰检测灵敏度均高达100%，真阳率均超过99.9%；对于时长10 s的ECG信号，R峰检测CPU耗时约为23.2 ms。
结论: 本文方法对可穿戴式和常规心电信号的R峰检测均可达到良好效果，且满足R峰检测的实时性需求。
Keywords: R-peak detection; convolutional neural network; heartbeat-aware; wearable device ECG data.