Search

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.

Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis applications. However, annotation of these peaks is not a trivial task and complicated in some subjects. In this paper, an automated method is proposed to annotate these peaks. The high-frequency accelerations obtained from the same accelerometer, used to record SCG with, were used to facilitate the annotation of the SCG. Algorithms were developed for detection of isovolumic moment (IM) and aortic valve closure (AC) points of SCG. Four different envelope calculation methods were used: cardiac sound characteristic waveform (CSCW), Shannon, absolute, and Hilbert. The algorithms were evaluated based on a dataset including 18 subjects undergoing lower body negative pressure and were further tested with another dataset, which included 67 subjects. These datasets had been previously manually annotated. The algorithm based on CSCW envelope calculation produced the highest detection accuracy for both IM and AC. The overall CSCW algorithm detection accuracy for the test dataset was 98.7% and 99.1% for the IM and AC points, respectively.