Abstract
Goal: the objective of this study was to develop a method to identify respiratory phases (i.e., inhale or exhale) of seismocardiogram (SCG) cycles. An SCG signal is obtained by placing an accelerometer on the sternum to capture cardiac vibrations. Methods: SCGs from 19 healthy subjects were collected, preprocessed, segmented, and labeled. To extract the most important features, each SCG cycle was divided to equal-sized bins in time and frequency domains, and the average value of each bin was defined as a feature. Support vector machines was employed for feature selection and identification. The features were selected based on the total accuracy. The identification was performed in two scenarios: leave-one-subject-out (LOSO), and subject-specific (SS). Results: time-domain features resulted in better performance. The time-domain features that had higher accuracies included the characteristic points correlated with aortic-valve opening, aortic-valve closure, and the length of cardiac cycle. The average total identification accuracies were 88.1% and 95.4% for LOSO and SS scenarios, respectively. Conclusion: the proposed method was an efficient, reliable, and accurate approach to identify the respiratory phases of SCG cycles. Significance: The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.
Original language | English |
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Article number | 7707393 |
Pages (from-to) | 1786-1792 |
Number of pages | 7 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 64 |
Issue number | 8 |
DOIs | |
Publication status | Published - 1 Aug 2017 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Respiratory phase identification
- Seismocardiogram (SCG)
- Support vector machine (SVM)
- Systolic time intervals (STI)