Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation

• Published in: Heliyon Vol. 10; no. 5; p. e27369
• Main Authors: Geng, Duyan, Yin, Yue, Fu, Zhigang, Pang, Geng, Xu, Guizhi, Geng, Yan, Wang, Alan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.03.2024; Elsevier
• Subjects: algorithms; Ballistocardiogram; Bidirectional long short-term memory network; electrocardiography; health status; heart; heart rate; Heart rate detection; humans; lead; neural networks; sleep; Sleeping position; Sleeping position; Heart rate detection; Ballistocardiogram; Bidirectional long short-term memory network
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e27369

Heart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status. Traditional electrocardiography (ECG) measurement, which is routinely monitored, requires subjects to wear lead electrodes frequently, which undoubtedly places great restrictions on participants' activities during the normal test. At present, the boom of wearable devices has created hope for non-invasive, simple operation and low-cost daily heart rate monitoring, among them, Ballistocardiogram signal (BCG) is an effective heart rate measurement method, but in the actual acquisition process, the robustness of non-invasive vital sign collection is limited. Therefore, it is necessary to develop a method to improve the robustness of heart rate monitoring. Therefore, in view of the problem that the accuracy of untethered monitoring heart rate is not high, we propose a method aimed at detecting the heartbeat cycle based on BCG to accurately obtain the beat-to-beat heart rate in the sleep state. In this study, we implement an innovative J-wave detection algorithm based on BCG signals. By collecting BCG signals recorded by 28 healthy subjects in different sleeping positions, after preprocessing, the data feature set is formed according to the clustering of morphological features in the heartbeat interval. Finally, a J-wave recognition model is constructed based on bi-directional long short-term memory (BiLSTM), and then the number of J-waves in the input sequence is counted to realize real-time detection of heartbeat. The performance of the proposed heartbeat detection scheme is cross-verified, and the proposed method is compared with the previous wearable device algorithm. The accuracy of J wave recognition in BCG signal is 99.67%, and the deviation rate of heart rate detection is only 0.27%, which has higher accuracy than previous wearable device algorithms. To assess consistency between method results and heart rates obtained by the ECG, seven subjects are compared using Bland-Altman plots, which show no significant difference between BCG and ECG results for heartbeat cycles. Compared with other studies, the proposed method is more accurate in J-wave recognition, which improves the accuracy and generalization ability of BCG-based continuous heartbeat cycle extraction, and provides preliminary support for wearable-based untethered daily monitoring.