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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.14376 |
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| _version_ | 1866909433800425472 |
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| author | Yi, Ruhan Popescu, Mihail Keller, James M. Scott, Grant Despins, Laurel Heise, David Skubic, Marjorie |
| author_facet | Yi, Ruhan Popescu, Mihail Keller, James M. Scott, Grant Despins, Laurel Heise, David Skubic, Marjorie |
| contents | Longitudinal monitoring of heart rate (HR) and heart rate variability (HRV) can aid in tracking cardiovascular diseases (CVDs), sleep quality, sleep disorders, and reflect autonomic nervous system activity, stress levels, and overall well-being. These metrics are valuable in both clinical and everyday settings. In this paper, we present a transformer network aimed primarily at detecting the precise timing of heart beats from predicted electrocardiogram (ECG), derived from input Ballistocardiogram (BCG). We compared the performance of segment and subject models across three datasets: a lab dataset with 46 young subjects, an elder dataset with 28 elderly adults, and a combined dataset. The segment model demonstrated superior performance, with correlation coefficients of 0.97 for HR and mean heart beat interval (MHBI) when compared to ground truth. This non-invasive method offers significant potential for long-term, in-home HR and HRV monitoring, aiding in the early indication and prevention of cardiovascular issues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Heartbeat Detection from Ballistocardiogram using Transformer Network Yi, Ruhan Popescu, Mihail Keller, James M. Scott, Grant Despins, Laurel Heise, David Skubic, Marjorie Signal Processing Longitudinal monitoring of heart rate (HR) and heart rate variability (HRV) can aid in tracking cardiovascular diseases (CVDs), sleep quality, sleep disorders, and reflect autonomic nervous system activity, stress levels, and overall well-being. These metrics are valuable in both clinical and everyday settings. In this paper, we present a transformer network aimed primarily at detecting the precise timing of heart beats from predicted electrocardiogram (ECG), derived from input Ballistocardiogram (BCG). We compared the performance of segment and subject models across three datasets: a lab dataset with 46 young subjects, an elder dataset with 28 elderly adults, and a combined dataset. The segment model demonstrated superior performance, with correlation coefficients of 0.97 for HR and mean heart beat interval (MHBI) when compared to ground truth. This non-invasive method offers significant potential for long-term, in-home HR and HRV monitoring, aiding in the early indication and prevention of cardiovascular issues. |
| title | Heartbeat Detection from Ballistocardiogram using Transformer Network |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.14376 |