Zakeri Vahid, Akhbardeh Alireza, Alamdari Nasim, Fazel-Rezai Reza, Paukkunen Mikko, Tavakolian Kouhyar
IEEE Trans Biomed Eng. 2017 Aug;64(8):1786-1792. doi: 10.1109/TBME.2016.2621037. Epub 2016 Oct 26.
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.
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).
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.
the proposed method was an efficient, reliable, and accurate approach to identify the respiratory phases of SCG cycles.
The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.
本研究的目的是开发一种方法，以识别心震图（SCG）周期的呼吸阶段（即吸气或呼气）。通过将加速度计放置在胸骨上来获取SCG信号，以捕捉心脏振动。
收集了19名健康受试者的SCG信号，进行预处理、分割和标记。为了提取最重要的特征，将每个SCG周期在时域和频域中划分为等大小的区间，并将每个区间的平均值定义为一个特征。采用支持向量机进行特征选择和识别。基于总准确率选择特征。识别在两种情况下进行：留一受试者法（LOSO）和受试者特定法（SS）。
时域特征表现更好。具有较高准确率的时域特征包括与主动脉瓣开放、主动脉瓣关闭以及心动周期长度相关的特征点。LOSO和SS情况下的平均总识别准确率分别为88.1%和95.4%。
所提出的方法是一种有效、可靠且准确的识别SCG周期呼吸阶段的方法。
本研究获得的结果可用于加强对临床有价值信息（如收缩期时间间隔）的提取。
