An Octave Convolution Neural Network-based QRS Detector

Detection of QRS complex in electrocardiogram (ECG) is the most critical and basic step for automated cardiac diagnosis. Many automated QRS complex detection methods have been proposed, and several state-of-art approaches show acceptable detection accuracy. However, current methods cannot generalize to out-of-distribution data, especially inter-patient and low-quality wearable ECGs.In our work, a 20-layer convolutional neural network (CNN) using octave convolution was proposed to detect QRS complex in unknown and noisy ECGs. Octave convolution allows inter-frequency communication between high- and low-frequency bands in latent space. All extracted features are sent to fully connected classifier for element-wise QRS complex detection. Intra- and inter-independent databases testing was conducted to evaluate methods’ generalization capacity and noise immunity.Our experimental evaluations show that our method results in significant F1 gains on multi independent databases. The proposed method results in average 24.23% improvement comparing with P&T algorithm and 2.16% with SENet, which is the first-place-method in China Physiological signal Challenge (CPSC) 2019. Notably, the proposed method shows superior stability to SENet on the whole databases, especially on databases from out-of-distribution sources of the training set.