A framework to detect aortic valve opening (AO) phase with the help of seismocardiogram (SCG) signal is proposed. A small electronic circuit board is designed, which consists of 3-D MEMS based accelerometer, pre-amplifier, and filter. It is interfaced with standard data acquisition system to record SCG signals. The signal is decomposed using a proposed modified variational mode decomposition technique. In the first stage of decomposition, baseline drift is suppressed. Whereas, in the second stage, signal information related to AO instants are extracted. Gaussian derivative filtering is performed on each of the decomposed modes to enhance the systolic profiles. These filtered modes are named as Gaussian derivative filtered modes (GDFMs). The GDFMs with probable AO peaks are selected based on proposed relative GDFM energy (RGE). The signal is reconstructed from the selected GDFMs and it is emphasized using the weights derived from squared RGE. The iteratively extracted maximum slope information is incorporated for systole envelope construction. Finally, peaks are detected using Hilbert transform and cardiac cycle envelope. The robustness of the proposed framework is evaluated using clean and noisy SCG signals from two different databases. For publicly available database (CEBS, Physionet), mean detection error rate 5.2%, sensitivity 97.3%, positive predictivity 97.4%, and detection accuracy 95.1% are found. For our real-time SCG database, the values of these metrics are 6.9%, 96.7%, 96.4%, and 93.4%, respectively. The developed system shows good detection rates even on less number of analyzed beats.

中文翻译：

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一种利用SCG信号检测主动脉瓣开放相位的新方法

提出了一种借助心电图 (SCG) 信号检测主动脉瓣开度 (AO) 相位的框架。设计了一个小型电子电路板，它由基于 3D MEMS 的加速度计、前置放大器和滤波器组成。它与标准数据采集系统接口以记录SCG信号。使用提出的改进的变分模式分解技术来分解信号。在分解的第一阶段，基线漂移被抑制。而在第二阶段，提取与 AO 时刻相关的信号信息。对每个分解模式执行高斯导数滤波以增强收缩曲线。这些滤波模式被称为高斯导数滤波模式 (GDFM)。基于建议的相对 GDFM 能量 (RGE) 选择具有可能 AO 峰值的 GDFM。从选定的 GDFM 重建信号，并使用从平方 RGE 得出的权重对其进行强调。迭代提取的最大斜率信息被纳入收缩包络构造。最后，使用希尔伯特变换和心动周期包络检测峰值。使用来自两个不同数据库的干净和嘈杂的 SCG 信号来评估所提出框架的稳健性。对于公开可用的数据库（CEBS，Physionet），发现平均检测错误率为 5.2%，灵敏度为 97.3%，阳性预测率为 97.4%，检测准确率为 95.1%。对于我们的实时 SCG 数据库，这些指标的值分别为 6.9%、96.7%、96.4% 和 93.4%。开发的系统即使在分析的节拍数量较少时也显示出良好的检测率。