RBHHM: A novel remote cardiac cycle detection model based on heartbeat harmonics. (September 2022)

• Record Type: Journal Article
• Authors: Ji, Shanling; Zhang, Zhisheng; Xia, Zhijie; Wen, Haiying; Zhu, Jianxiong; Zhao, Kunkun
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Cardiac cycle -- Bimodal Gaussian distribution -- Heartbeat harmonics -- Remote heartbeat detection -- K-means -- RNNs; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103936
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We proposed a heartbeat harmonics based remote cardiac cycle detection model. The correlation between the non-contact cardiac radar and ECG is investigated. A heartbeat intervals detection method using remote radar is researched. The time series classification maethod of remote cardiac cycle is studied. The accuracy of proposed detection methods is verified using a clinical dataset. Abstract: Cardiac cycle detection methods based on radar sensing have made marked progress in the last decade. In this study, a novel harmonic distribution based non-contact cardiac cycle detection method is innovatively proposed to extract heartbeat intervals and rhythm sequences from radar signals. Initially, we exploit the bimodal Gaussian distribution model of the frequency domain and the Shannon energy to separate heartbeat harmonics from radar signals, which preserve instantaneous variation features of signal components. Furthermore, we cluster harmonics into three types of waves based on K-means. The peak positions of the three waves are near locations of the R-peak, T-peak, and T-wave end in the electrocardiograph (ECG) respectively. Thus, the detected peak locations of three waves can be directly used to estimate heartbeat-to-beat intervals. Additionally, we compared three recurrent neural networks (RNNs) using the clustered waves to segmentation sequences of "R-T-R" and "Q-R-S-T-Q". Specific subsets are classified from the sequences to prove their association with cardiac systolic and diastolic variation. Finally, verification is performed using a clinical dataset. The suitable centre frequency range to use the proposed model is about 3–15 Hz. The performance of the cardiac cycle detection method achieves a mean relative error of 5.34% ± 3.57% for BBIs detection and an accuracy of 95.88% for heartbeat rhythm segmentation. The experimental results show that our radar-based heartbeat harmonic model (RBHHM) based method has better accuracy than other state-of-the-art cardiac cycle detection methods.