A Novel Method for Aortic Valve Opening Phase Detection Using SCG Signal

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.