Analyzing seismocardiogram cycles to identify the respiratory phases

Vahid Zakeri*, Alireza Akhbardeh, Nasim Alamdari, Reza Fazel-Rezai, Mikko Paukkunen, Kouhyar Tavakolian

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)

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 languageEnglish
Article number7707393
Pages (from-to)1786-1792
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number8
DOIs
Publication statusPublished - 1 Aug 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Respiratory phase identification
  • Seismocardiogram (SCG)
  • Support vector machine (SVM)
  • Systolic time intervals (STI)

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    Zakeri, V., Akhbardeh, A., Alamdari, N., Fazel-Rezai, R., Paukkunen, M., & Tavakolian, K. (2017). Analyzing seismocardiogram cycles to identify the respiratory phases. IEEE Transactions on Biomedical Engineering, 64(8), 1786-1792. [7707393]. https://doi.org/10.1109/TBME.2016.2621037