SYSTEM FOR CHARACTERIZING CARDIOVASCULAR SYSTEMS FROM SINGLE CHANNEL DATA
Systems to identify and risk stratify disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and other toxic agents, pathological substrates, conduction delays and defects, and ejection fraction using single channel biological data obtained from the subject. A modified Matching Pursuit (MP) algorithm may be used to find a noiseless model of the data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to characterize the cardiac system. In another system, space-time domain is divided into a number of regions (which is largely determined by the signal length), the density of the signal is computed in each region and input to a learning algorithm to associate them to the desired cardiac dysfunction indicator target.
1 . A system for detecting a cardiac dysfunction using a computing device, comprising:
one or more processors; and
a memory having instructions stored thereon, wherein execution of the instructions by the one or more processors, cause the one or more processors to:
receive only one-dimensional electrophysiological data;
construct multi-dimensional electrophysiological data from the only one-dimensional electrophysiological data;
define a phase space system from the multi-dimensional electrophysiological data;
detect the cardiac dysfunction from the phase space system; and
provide an indicator of the cardiac dysfunction.
2 . The system of claim 1 , wherein the instructions to construct multi-dimensional electrophysiological data further cause the one or more processors to:
denoise the one-dimensional electrophysiological data to generate denoised one-dimensional data;
remove a baseline of the denoised one-dimensional electrophysiological data; and
take fractional derivatives of the denoised one-dimensional electrophysiological data to construct the multi-dimensional electrophysiological data.
3 . The system of claim 1 , wherein the one-dimensional electrophysiological data comprises at least one cardiac cycle.
4 . The system of claim 3 , wherein the cardiac cycle corresponds to a vector sum of electrical activation of the heart.
5 . The system of claim 4 , wherein the instructions to detect the cardiac dysfunction from the phase space system further cause the one or more processors to:
use variables from an N dimensional space-time information to determine feature vectors; and
correlate the feature vectors with attributes that are associated with the cardiac dysfunction.
6 . The system of claim 5 , wherein the instructions to correlate use one or more machine learning algorithms.
7 . The system of claim 6 , wherein the one or more machine learning algorithms includes a genetic algorithm mediated map generation or artificial neural network.
8 . The system of claim 5 , further comprising instructions to cause the one or more processors to:
divide the N dimensional space-time information into a predetermined number of regions;
determine a density of a time series of each region; and
use the density of each region in the step of correlating.
9 . A system for detecting a cardiac dysfunction using a computing device, comprising:
one or more processors; and
a memory having instructions stored thereon, wherein execution of the instructions by the one or more processors, cause the one or more processors to:
receive only one-dimensional electrophysiological data;
construct multi-dimensional electrophysiological data from the only one-dimensional electrophysiological data;
define a phase space system from the multi-dimensional electrophysiological data;
determine a baseline measurement of a subject's physiological condition from the phase space system;
determine a first risk score associated with the baseline measurement of the subject's physiological condition;
apply a therapy to the subject and subsequently receiving only second one-dimensional electrophysiological data;
construct second multi-dimensional electrophysiological data from the only second one-dimensional electrophysiological data;
define a second phase space system from the second multi-dimensional electrophysiological data;
determine a second measurement of the subject's physiological condition from the second phase space system after the therapy is applied;
determine a second risk score associated with the second measurement of the subject's physiological condition; and
compare the first risk score and the second risk score to determine a utility of patient therapies.
10 . The system of claim 9 , wherein the cardiac dysfunction is at least one of disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and toxic agents, pathological substrates, conduction delays and defects, and ejection fraction.
11 . The system of claim 9 , wherein the instructions to provide an indicator of the cardiac dysfunction further include instructions to generate a 3-D phase space plot containing the indicator.
12 . The system of claim 11 , wherein the indicator is a complex subharmonic frequency (CSF) trajectory that is associated with the cardiac dysfunction.
13 . The system of claim 1 , further comprising instructions that cause the one or more processors to receive the one-dimensional electrophysiological data from one of a one-dimensional recorder, an implantable telemeter, a smartphone, a smart handheld consumer device, a smart watch, a perfusion sensor, and clothing embedded with biometrics sensors.
14 . A system for detecting a cardiac dysfunction, comprising:
one or more processors; and
a memory having instructions stored thereon, wherein execution of the instructions by the one or more processors, cause the one or more processors to:
receive only one-dimensional electrophysiological data;
construct multi-dimensional electrophysiological data from the only one-dimensional electrophysiological data;
define a model from the multi-dimensional electrophysiological data;
detect the cardiac dysfunction from the model; and
provide an indicator of the cardiac dysfunction.
15 . The system of claim 14 , wherein the instructions to construct multi-dimensional electrophysiological data further cause the one or more processors to:
denoise the one-dimensional electrophysiological data to generate denoised one-dimensional data;
remove a baseline of the denoised one-dimensional electrophysiological data; and
take fractional derivatives of the denoised one-dimensional electrophysiological data to construct the multi-dimensional electrophysiological data.
16 . The system of claim 14 , wherein the one-dimensional electrophysiological data comprises at least one cardiac cycle.
17 . The system of claim 14 , wherein the model is created using a modified MP algorithm.
18 . The system of claim 17 , wherein the model is linked to space-time densities.
19 . The system of claim 14 , wherein the cardiac dysfunction is at least one of disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and toxic agents, pathological substrates, conduction delays and defects, and ejection fraction.
20 . The system of claim 14 , wherein the cardiac dysfunction is detected using at least one machine learning algorithm.