Analyzing seismocardiogram cycles to identify the respiratory phases

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

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

Organisaatiot

  • Heart Force Medical Inc.
  • University of British Columbia
  • Johns Hopkins University
  • University of North Dakota

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Artikkeli7707393
Sivut1786-1792
Sivumäärä7
JulkaisuIEEE Transactions on Biomedical Engineering
Vuosikerta64
Numero8
TilaJulkaistu - 1 elokuuta 2017
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 15861892