Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases

作者：Zakeri Vahid^*; Akhbardeh Alireza; Alamdari Nasim; Fazel Rezai Reza; Paukkunen Mikko; Tavakolian Kouhyar

来源：IEEE Transactions on Biomedical Engineering, 2017, 64(8): 1786-1792.

DOI：10.1109/TBME.2016.2621037

• Respiratory phase identification
• seismocardiogram (SCG)
• support vector machine (SVM)
• systolic time intervals (STI)

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.

• 出版日期2017-8