Analyzing seismocardiogram cycles to identify the respiratory phases
V Zakeri, A Akhbardeh, N Alamdari… - IEEE Transactions …, 2016 - ieeexplore.ieee.org
IEEE Transactions on Biomedical Engineering, 2016•ieeexplore.ieee.org
Goal: the objective of this study was to develop a method to identify respiratory phases (ie,
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 …
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 …
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.
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