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JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases.
IEEE Transactions on Bio-medical Engineering 2017 August
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.
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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