Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 8; pp. 1786 - 1792
• Main Authors: Zakeri, Vahid, Akhbardeh, Alireza, Alamdari, Nasim, Fazel-Rezai, Reza, Paukkunen, Mikko, Tavakolian, Kouhyar
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Accelerometry - methods; Adult; Algorithms; Ballistocardiography - methods; Bins; Computer Simulation; Feature extraction; Frequency domain analysis; Heart; Heart diseases; Humans; Male; Models, Biological; Oscillometry - methods; Pattern Recognition, Automated - methods; Phases; Reproducibility of Results; Respiratory Mechanics - physiology; Respiratory phase identification; seismocardiogram (SCG); Sensitivity and Specificity; Sternum; Support Vector Machine; support vector machine (SVM); Support vector machines; systolic time intervals (STI); Testing; Time domain analysis; Training; Vibrations
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2016.2621037

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.