Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation
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 restrict...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024034005 |
| _version_ | 1797259701758459904 |
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| author | Duyan Geng Yue Yin Zhigang Fu Geng Pang Guizhi Xu Yan Geng Alan Wang |
| author_facet | Duyan Geng Yue Yin Zhigang Fu Geng Pang Guizhi Xu Yan Geng Alan Wang |
| author_sort | Duyan Geng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T23:13:37Z |
| format | Article |
| id | doaj.art-4f4d3c5497814eb8a62c06ff80fd41b8 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T23:13:37Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-4f4d3c5497814eb8a62c06ff80fd41b82024-03-17T07:58:01ZengElsevierHeliyon2405-84402024-03-01105e27369Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validationDuyan Geng0Yue Yin1Zhigang Fu2Geng Pang3Guizhi Xu4Yan Geng5Alan Wang6Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China; Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR ChinaHebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China; Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR ChinaPhysical Examination Center of the Fourth Joint Logistics Support Unit of the 983rd Hospital of the Tianjin Chinese People's Liberation Army, Tianjin, 300142, PR ChinaHebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China; Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR ChinaHebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China; Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR ChinaHebei Institute for Drug and Medical Device Control, Shijiazhuang, 050200, PR China; Corresponding author.Centre for Brain Research, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand; Centre for Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New ZealandBackground: 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.http://www.sciencedirect.com/science/article/pii/S2405844024034005BallistocardiogramSleeping positionBidirectional long short-term memory networkHeart rate detection |
| spellingShingle | Duyan Geng Yue Yin Zhigang Fu Geng Pang Guizhi Xu Yan Geng Alan Wang Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation Heliyon Ballistocardiogram Sleeping position Bidirectional long short-term memory network Heart rate detection |
| title | Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation |
| title_full | Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation |
| title_fullStr | Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation |
| title_full_unstemmed | Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation |
| title_short | Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation |
| title_sort | heart rate detection method based on ballistocardiogram signal of wearable device algorithm development and validation |
| topic | Ballistocardiogram Sleeping position Bidirectional long short-term memory network Heart rate detection |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024034005 |
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