ECG-Free Assessment of Cardiac Valve Events Using Seismocardiography
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915061678735360 |
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| author | Rahman, Mohammad Muntasir Mann, Aysha Taebi, Amirtaha |
| author_facet | Rahman, Mohammad Muntasir Mann, Aysha Taebi, Amirtaha |
| contents | Seismocardiogram (SCG) signals can play a crucial role in remote cardiac monitoring, capturing important events such as aortic valve opening (AO) and mitral valve closure (MC). However, existing SCG methods for detecting AO and MC typically rely on electrocardiogram (ECG) data. In this study, we propose an innovative approach to identify AO and MC events in SCG signals without the need for ECG information. Our method utilized a template bank, which consists of signal templates extracted from SCG waveforms of 5 healthy subjects. These templates represent characteristic features of a heart cycle. When analyzing new, unseen SCG signals from another group of 6 healthy subjects, we employ these templates to accurately detect cardiac cycles and subsequently pinpoint AO and MC events. Our results demonstrate the effectiveness of the proposed template bank approach in achieving ECG-independent AO and MC detection, laying the groundwork for more convenient remote cardiovascular assessment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_09513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ECG-Free Assessment of Cardiac Valve Events Using Seismocardiography Rahman, Mohammad Muntasir Mann, Aysha Taebi, Amirtaha Medical Physics Seismocardiogram (SCG) signals can play a crucial role in remote cardiac monitoring, capturing important events such as aortic valve opening (AO) and mitral valve closure (MC). However, existing SCG methods for detecting AO and MC typically rely on electrocardiogram (ECG) data. In this study, we propose an innovative approach to identify AO and MC events in SCG signals without the need for ECG information. Our method utilized a template bank, which consists of signal templates extracted from SCG waveforms of 5 healthy subjects. These templates represent characteristic features of a heart cycle. When analyzing new, unseen SCG signals from another group of 6 healthy subjects, we employ these templates to accurately detect cardiac cycles and subsequently pinpoint AO and MC events. Our results demonstrate the effectiveness of the proposed template bank approach in achieving ECG-independent AO and MC detection, laying the groundwork for more convenient remote cardiovascular assessment. |
| title | ECG-Free Assessment of Cardiac Valve Events Using Seismocardiography |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2408.09513 |