  Precise estimation of fiducial points of a seismocardiogram (SCG) signal is a
challenging problem for its clinical usage. Delineation techniques proposed in
the existing literature do not estimate all the clinically significant points
of an SCG signal, simultaneously. The aim of this research work is to propose a
delineation framework to identify IM, AO, IC, AC, pAC and MO fiducial points
with the help of a PPG signal. The proposed delineation method processes a
wavelet-based scalographic PPG and an envelope construction scheme is proposed
to estimate the prominent peaks of the PPG signal. A set of amplitude histogram
based decision rules is developed for estimation of SCG diastole phases, namely
AC, pAC and MO. Subsequently, the systolic phases, IM, AO and IC are detected
by applying diastole masking on SCG and decision rules. Experimental results on
real-time SCG signals acquired from our designed data acquisition-circuitry and
their analysis show the effectiveness of the proposed scheme. Additionally,
these estimated parameters are analyzed to show the discrimination between
normal breathing and breathlessness conditions.
