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Published on: April 19, 2019
Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and
Duyan Geng1,2, Yue Yin1,2, Zhigang Fu3
1Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China.
Insights
This study introduces a novel J-wave detection algorithm using Ballistocardiogram (BCG) signals for accurate, untethered heart rate monitoring during sleep. The method achieves high accuracy, offering a robust alternative to traditional electrocardiography (ECG) for daily health tracking.
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Signal Processing
Background:
- Traditional electrocardiography (ECG) for heart rate monitoring is restrictive due to electrode requirements.
- Wearable devices offer non-invasive, convenient health monitoring, but Ballistocardiogram (BCG) signal acquisition faces robustness challenges.
- Accurate, untethered heart rate monitoring is crucial for continuous health assessment.
Purpose of the Study:
- To develop an accurate method for detecting heartbeat cycles using BCG signals, specifically for untethered monitoring during sleep.
- To improve the robustness and accuracy of non-invasive heart rate monitoring.
Main Methods:
- An innovative J-wave detection algorithm based on BCG signals was implemented.
- A bi-directional long short-term memory (BiLSTM) model was constructed for J-wave recognition after feature extraction.
- BCG signals from 28 healthy subjects in various sleeping positions were collected and analyzed.
Main Results:
- The J-wave recognition accuracy reached 99.67%, with a heart rate detection deviation rate of only 0.27%.
- The proposed method demonstrated higher accuracy compared to previous wearable device algorithms.
- Bland-Altman plots showed no significant difference between BCG and ECG heart rate results.
Conclusions:
- The developed method enhances the accuracy and generalization of BCG-based heartbeat cycle extraction.
- This approach provides a foundation for reliable, wearable-based, untethered daily health monitoring.
- The study validates the efficacy of the BiLSTM model for robust J-wave recognition in BCG signals.
Background:
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.
Objective:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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