Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals
Abstract We developed a deep learning–based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R–R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84049-0 |
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| author | Seiichi Morokuma Tadashi Saitoh Masatomo Kanegae Naoyuki Motomura Subaru Ikeda Kyuichi Niizeki |
| author_facet | Seiichi Morokuma Tadashi Saitoh Masatomo Kanegae Naoyuki Motomura Subaru Ikeda Kyuichi Niizeki |
| author_sort | Seiichi Morokuma |
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| description | Abstract We developed a deep learning–based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R–R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network to train the model to minimize the loss function of the mean squared error between the predicted ECG (pECG) and genuine ECG signals. Using a dataset acquired with polyvinylidene fluoride and ECG sensors in different recumbent positions from 18 participants, we generated pECG signals from preprocessed BCG signals using the learned model and evaluated the RRI estimation performance by comparing the predicted RRI with the reference RRI obtained from the ECG signal using a leave-one-subject-out cross-validation scheme. A mean absolute error (MAE) of 0.034 s was achieved for the beat-to-beat interval accuracy. To further test the generalization ability of the learned model trained with a short-term-recorded dataset, we collected long-term overnight recordings of BCG signals from 12 different participants and performed validation. The beat-to-beat interval correlation between BCG and ECG signals was 0.82 ± 0.06 with an average MAE of 0.046 s, showing practical performance for long-term measurement of RRIs. These results suggest that the proposed approach can be used for continuous heart rate monitoring in a home environment. |
| format | Article |
| id | doaj-art-ab3e0db4908944deb0df6c6f43d06c16 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ab3e0db4908944deb0df6c6f43d06c162025-01-12T12:15:02ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-84049-0Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervalsSeiichi Morokuma0Tadashi Saitoh1Masatomo Kanegae2Naoyuki Motomura3Subaru Ikeda4Kyuichi Niizeki5Department of Health Sciences, Graduate School of Medical Sciences, Kyushu UniversityDepartment of Applied Chemistry, Chemical Engineering, and Biochemical Engineering, Graduate School of Science and Engineering, Yamagata UniversityHealth Sensing Co. LtdHealth Sensing Co. LtdDepartment of Health Sciences, Graduate School of Medical Sciences, Kyushu UniversityDepartment of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (emeritus)Abstract We developed a deep learning–based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R–R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network to train the model to minimize the loss function of the mean squared error between the predicted ECG (pECG) and genuine ECG signals. Using a dataset acquired with polyvinylidene fluoride and ECG sensors in different recumbent positions from 18 participants, we generated pECG signals from preprocessed BCG signals using the learned model and evaluated the RRI estimation performance by comparing the predicted RRI with the reference RRI obtained from the ECG signal using a leave-one-subject-out cross-validation scheme. A mean absolute error (MAE) of 0.034 s was achieved for the beat-to-beat interval accuracy. To further test the generalization ability of the learned model trained with a short-term-recorded dataset, we collected long-term overnight recordings of BCG signals from 12 different participants and performed validation. The beat-to-beat interval correlation between BCG and ECG signals was 0.82 ± 0.06 with an average MAE of 0.046 s, showing practical performance for long-term measurement of RRIs. These results suggest that the proposed approach can be used for continuous heart rate monitoring in a home environment.https://doi.org/10.1038/s41598-024-84049-0BallistocardiogramBidirectional long short-term memory networkInterbeat interval detection |
| spellingShingle | Seiichi Morokuma Tadashi Saitoh Masatomo Kanegae Naoyuki Motomura Subaru Ikeda Kyuichi Niizeki Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals Scientific Reports Ballistocardiogram Bidirectional long short-term memory network Interbeat interval detection |
| title | Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals |
| title_full | Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals |
| title_fullStr | Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals |
| title_full_unstemmed | Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals |
| title_short | Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals |
| title_sort | prediction of ecg signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals |
| topic | Ballistocardiogram Bidirectional long short-term memory network Interbeat interval detection |
| url | https://doi.org/10.1038/s41598-024-84049-0 |
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