Non-invasive method and system for characterizing cardiovascular systems for all-cause mortality and sudden cardiac death risk

A system for quantifying risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, the system 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:
• retrieve an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• process, via space-time analysis comprising a phase space transformation, the retrieved electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the retrieved electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, and wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia;
• wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death.

A non-transitory computer readable medium having instructions, wherein execution of the instructions, by a processor, cause the processor to:

• obtain an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• process, via space-time analysis comprising a phase space transformation, the electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the retrieved electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, and wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia;
• and
• wherein the one or more parameters parameter, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death.

A method for quantifying risk of cardiac morbidity or death, the method comprising:

• obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• processing, by the one or more processors, via space-time analysis, the obtained electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the obtained electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the generated space-time domain data set is divided into a plurality of regions from a center of mass of a space-time density structure associated with the generated space-time domain data set.

A method for quantifying risk of cardiac morbidity or death, the method comprising:

• obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• processing, by the one or more processors, via space-time analysis, the obtained electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the obtained electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the learning algorithm comprises a genetic algorithm.

A method for quantifying risk of cardiac morbidity or death, the method comprising:

• obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• processing, by the one or more processors, via space-time analysis, the obtained electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the obtained electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, ad wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the generated space-time domain data set comprises an N-dimensional data set.

A method for quantifying risk of cardiac morbidity or death, the method comprising:

• obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• processing, by the one or more processors, via space-time analysis, the obtained electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the obtained electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the generated space-time domain data set comprises a subspace data set.

A method for quantifying risk of cardiac morbidity or death, the method comprising:

• obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• processing, by the one or more processors, via space-time analysis, the obtained electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the obtained electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, and wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the extracted metrics comprise one or more parameters selected from the group consisting of one or more CSF trajectory parameters, one or more quasi-periodic subspace parameters, one or more chaotic subspace parameters, one or more low-energy component subspace parameters, one or more high energy component subspace parameters, one or more fractional derivative parameters associated with a low-energy component subspace, and one or more fractional derivative parameters associated with a high-energy component subspace.

A method for quantifying risk of cardiac morbidity or death, the method comprising:

• obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• processing, by the one or more processors, via space-time analysis, the obtained electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the obtained electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, and wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death;
• and
• generating, for display, by the one or more processors, a three dimensional phase space plot of the generated space-time domain data.

A method for quantifying risk of cardiac morbidity or death, the method comprising:

• obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• processing, by the one or more processors, via space-time analysis, the obtained electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the obtained electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the at least one heart beat cycle corresponds to a vector sum electrical activation pathway through the heart.

A system for quantifying risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, the system 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:
• retrieve an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• process, via space-time analysis, the retrieved electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the retrieved electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the learning algorithm comprises a genetic algorithm.

A system for quantifying risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, the system 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:
• retrieve an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• process, via space-time analysis, the retrieved electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the retrieved electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death,
• wherein the generated space-time domain data set comprises an N-dimensional data set.

A system for quantifying risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, the system 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:
• retrieve an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• process, via space-time analysis, the retrieved electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the retrieved electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death,
• wherein the generated space-time domain data set comprises a subspace data set.

A system for quantifying risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, the system 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:
• retrieve an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• process, via space-time analysis, the retrieved electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the retrieved electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death, and
• wherein the extracted metrics comprise one or more parameters selected from the group consisting of one or more CSF trajectory parameters, one or more quasi-periodic subspace parameters, one or more chaotic subspace parameters, one or more low-energy component subspace parameters, one or more high energy component subspace parameters, one or more fractional derivative parameters associated with a low-energy component subspace, and one or more fractional derivative parameters associated with a high-energy component subspace.

A system for quantifying risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, the system 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:
• retrieve an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle;
• and
• process, via space-time analysis, the retrieved electrophysiological data set to extract metrics from a generated space-time domain data set generated from the space-time analysis of the retrieved electrophysiological data set, wherein the generated space-time domain data set is divided into a plurality of regions and metrics associated with each of the plurality of regions are extracted, wherein the extracted metrics are used, via a learning algorithm, to generate one or more parameters that characterize a risk selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for sudden cardiac death, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia, wherein the one or more parameters, or values associated therewith, are presented for clinical utility in predicting risk of cardiac morbidity or death;
• and
• generate, for display, a three dimensional phase space plot of the generated space-time domain data.