QRS detection method based on fully convolutional networks for capacitive electrocardiogram

Abstract

A capacitive electrocardiogram (cECG) signal is considered a promising alternative to a conventional contact electrocardiogram (ECG) signal because the cECG signal can serve the same purpose as the contact ECG signal but can be measured during daily life without causing a subject to feel uncomfortable. However, the cECG signal has a limitation in that detection of QRS complexes, which is a fundamental step to analyze heart condition, is not easy. That is because the cECG signal is sensitive to noise, especially motion noise. This paper proposes a method to detect QRS complexes in cECG signals degraded by motion noise. The proposed method is based on fully convolutional networks (FCNs) and mainly consists of three parts: the generation of ground-truth data, the FCN model, and postprocessing. A labeling process for generating the ground-truth data is proposed. Then, an FCN model that is suitable for cECG signals is proposed. The proposed FCN model uses filters of a large size to achieve a large receptive field, unlike the common FCN models used in image processing. The receptive field is sufficiently large to involve information about adjacent QRS complexes, such as the time interval between the QRS complexes and its variability. By considering the information, the proposed FCN model can reliably classify QRS complexes even in cECG signals degraded by motion noise. Additionally, postprocessing, which consists of an accumulation step and a non-maximum suppression step, is proposed to complement the proposed FCN model. In experiments with real data, the proposed method showed an average sensitivity of 96.94%, positive predictive value of 99.13%, and F1 score of 98.02%. These results demonstrate that the proposed method overcomes the limitation of a cECG signal and helps the cECG signal be widely utilized for medical or healthcare applications.