A Faster R CNN-Based Real-Time QRS Detector

Accurate QRS location keeps challenging in dynamic electrocardiograms (ECGs). This study addressed this issue and developed a novel faster R convolutional neural network (CNN) model-based real-time QRS detection algorithm. Firstly, ECGs were segmented into 10-s length episodes, and each episode was transformed into a 2-D image with a pixel size of 200 X200 (VOC2007 format). Labelled QRS location information was used to generate the QRS bounding boxes. A faster R CNN model was constructed. Candidates of QRS bounding boxes were extracted by the region proposal networks (RPN). Then, the boxes with small probabilities were excluded according to the rules of probability distribution and QRS location relationship. Finally, locations of QRS complexes were determined based on the geometric features and threshold rule. The proposed algorithm was trained on the MIT/BIH arrhythmia database and verified on the 24-h wearable ECGs. Five-fold cross validation on 24-h wearable ECG recordings from 20 subjects generated a sensitivity of 98.76%, a positive predictivity of 98.52% and an accuracy of 97.32% compared to the manual annotations. In addition, the cost time of the new algorithm for processing a 10-s ECG episode was less than 20 ms under the experiments of CPU i7-2600 3.40 GHz, 8 GB RAM, tesla M60 GPU and 16 GB graphics memory.