Method and system for generating synthetic time domain signals to build a classifier

A system for generating synthetic time domain signals to build a classifier, the system comprising:

• a memory storing instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive a parent dataset of a plurality of samples of a time domain signal of interest comprising a combination of a class data and an non-class data;
• identify a plurality of subsets, from the parent dataset, corresponding to a plurality of morphological features identified for the time domain signal of interest, wherein each subset among the plurality of subset comprises p samples, wherein the plurality of morphological features comprise a Peak Sample (Ps), a Peak Amplitude (Pa), a Trough Sample (Ts), a Trough Amplitude (Ta), a Notch Sample (Ns), a Notch Amplitude (Na), a Dip Sample (Ds), a Dip Amplitude (Da), and distance between left and right samples corresponding to the 25%, 50%, 75% of the (Pa) defining distances d1, d2, d3, and wherein the plurality of morphological features define a template for the time domain signal of interest;
• process each of the plurality of subsets corresponding to each of the plurality of morphological features to generate a plurality of sets of observational values with each of the plurality of sets of observational values comprising p actual values corresponding to each of the plurality of morphological features;
• fit a gaussian kernel density estimate (KDE) to each of the plurality of sets of observational values;
• generate N-point simulated data for each of the plurality of morphological features by generating N random samples from the gaussian KDE fitted to each of the plurality of sets of observational values;
• and
• construct a plurality of synthetic time domain signals for the time domain signal of interest from the N-point simulated data for each of the plurality of morphological features in accordance to the template, wherein the constructing of the plurality of synthetic time domain signals comprising:
• determining a plurality of sequences derived from the generated N-point simulated data for each of the plurality of morphological features, wherein a plurality of elements in each of the plurality of sequences comprising Ts, Ps, Ds, Ns, r1 r2, r3, q1, q2 and q3, wherein values of q1, q2, q3, r1, r2, r3 are derived based on a preselected values of the distances d1, d2, d3 defined for the template, and wherein position of each of the plurality of elements within each of the plurality of sequences is based on a set of conditions defined by a predefined set of morphological features among the plurality of morphological features;
• generating from the plurality of sequences, a plurality of time domain signals corresponding to the time domain signal of interest by performing a spline fitting on each of the plurality of sequences, wherein the spline fitting utilizes piecewise linear regression to obtain parameters of lines connecting two successive elements among the plurality of elements of the each of the plurality of sequences;
• smoothening each of the plurality of time domain signals using a smoothening technique to generate a plurality of smoothened time domain signals;
• and
• applying a peak smoothening technique on each of the smoothened plurality of time domain signals to construct the plurality of synthetic time domain signals for the time domain signal of interest.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes a method for:

• receiving a parent dataset of a plurality of samples of a time domain signal of interest comprising a combination of a class data and a non-class data,
• identifying a plurality of subsets, from the parent dataset, corresponding to a plurality of morphological features identified for the time domain signal of interest, wherein each subset among the plurality of subsets comprises p samples, further wherein the plurality of morphological features comprise a Peak Sample (Ps), a Peak Amplitude (Pa), a Trough Sample (Ts), a Trough Amplitude (Ta), a Notch Sample (Ns), a Notch Amplitude (Na), a Dip Sample (Ds), a Dip Amplitude (Da), and distance between left and right samples corresponding to the 25%, 50%, 75% of the (Pa) defining distances d1, d2, d3, and wherein the plurality of morphological features define a template for the time domain signal of interest;
• processing each of the plurality of subsets corresponding to each of the plurality of morphological features to generate a plurality of sets of observational values with each of the plurality of sets of observational values comprising p actual values corresponding to each of the plurality of morphological features;
• fitting a gaussian kernel density estimate (KDE) to each of the plurality of sets of observational values;
• generating N-point simulated data for each of the plurality of morphological features by generating N random samples from the gaussian KDE fitted to each of the plurality of sets of observational values;
• and
• constructing a plurality of synthetic time domain signals for the time domain signal of interest from the N-point simulated data for each of the plurality of morphological features in accordance to the template, wherein the constructing of the plurality of synthetic time domain signals comprising:
• determining a plurality of sequences derived from the generated N-point simulated data for each of the plurality of morphological features, wherein a plurality of elements in each of the plurality of sequences comprising Ts, Ps, Ds, Ns, r1 r2, r3, q1, q2 and q3, wherein values of q1, q2, q3, r1, r2, r3 are derived based on a preselected values of the distances d1, d2, d3 defined for the template, and wherein position of each of the plurality of elements within each of the plurality of sequences is based on a set of conditions defined by a predefined set of morphological features among the plurality of morphological features;
• generating from the plurality of sequences, a plurality of time domain signals corresponding to the time domain signal of interest by performing a spline fitting on each of the plurality of sequences, wherein the spline fitting utilizes piecewise linear regression to obtain parameters of lines connecting two successive elements among the plurality of elements of the each of the plurality of sequences;
• smoothening each of the plurality of time domain signals using a smoothening technique to generate a plurality of smoothened time domain signals;
• and
• applying a peak smoothening technique on each of the smoothened plurality of time domain signals to construct the plurality of synthetic time domain signals for the time domain signal of interest.