| CPC A61B 5/349 (2021.01) [A61B 5/352 (2021.01); A61B 5/364 (2021.01); A61B 5/366 (2021.01); A61B 5/743 (2013.01); G16H 10/60 (2018.01); G16H 20/10 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)] | 18 Claims |
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1. An automated non-invasive method of assessment of cardiac status of an examined pediatric subject utilizing an electrocardiogram (ECG) system including: an electronic unit configured to connect to the examined pediatric subject, a memory unit configured to contain a database of Z-score-based nomograms of a first set of ECG variables from historic data of healthy individuals including pediatric individuals, a computer interface system, an adaptive confirmatory enhancement (ACE) module, and a report generator, the method comprising:
digitally transforming, by the computer interface system of the ECG system, electrical values obtained by the electronic unit from the examined pediatric subject to generate digital ECG values of a second set of ECG variables of the examined pediatric subject;
determining, by the computer interface system of the ECG system, that the digital ECG values of the second set of ECG variables of the examined pediatric subject comprises abnormal R wave voltages in left lateral leads of the examined pediatric subject of the ECG system, wherein the abnormal R wave voltages comprise Z-scores with two standard deviations above a mean Z-score of the first set of ECG variables from the historic data of healthy individuals including the pediatric individuals or Z-scores with two standard deviations below the mean Z-score of the first set of ECG variables from the historic data of healthy individuals including the pediatric individuals;
executing, by the computer interface system of the ECG system, a comparison of the digital ECG values of the second set of ECG variables of the examined pediatric subject with the pediatric individuals in the database in the memory unit;
determining, by the computer interface system of the ECG system, a diagnosis of hypertrophic cardiomyopathy (HCM) of the examined pediatric subject based on the comparison;
periodically integrating, by the ACE module of the ECG system, new ECG data into the database by performing continuous machine learning to create ECG-disease associations and confirm and enhance an accuracy of the diagnosis of HCM of the examined pediatric subject by calculating new Z-scores and new cut-off values to predict a first set of new normal and abnormal ECGs using the continuous machine learning and update the Z-score-based nomograms based on the new Z-scores, the new cut-off values, and the first set of new normal and abnormal ECGs;
generating, by the report generator of the ECG system, an extended ECG report containing: a predictive Z-score for each one of the second set of ECG variables of the examined pediatric subject; and the diagnosis of HCM of the examined pediatric subject based on the updated Z-score-based nomograms;
incorporating, by the ACE module of the ECG system, a second set of new normal and abnormal ECG values of the second set of ECG variables by incorporating the continuous machine learning of undiscovered patterns that are used to discern the second set of new normal and the abnormal ECG values of the second set of ECG variables to further create the ECG-disease associations and enhance the accuracy of the diagnosis of HCM of the examined pediatric subject and further update the Z-score-based nomograms based on the second set of new normal and abnormal ECG values; and
determining, by the ACE module of the ECG system, whether the diagnosis of HCM of the examined pediatric subject is accurate based on the periodic integration of the new ECG data into the database by performing continuous machine learning to create the ECG-disease associations and the incorporation of the second set of new normal and abnormal ECG values of the second set of ECG variables by incorporating the continuous machine learning of the undiscovered patterns that are used to discern the second set of new normal and the abnormal ECG values of the second set of ECG variables to further create the ECG-disease associations,
wherein the examined pediatric subject and the pediatric individuals in the database comprise individuals up to an age of eighteen,
the digital ECG values of the second set of ECG variables comprise T wave axis, R-Taxis deviation, T wave voltage, QRS axis, QRS integral, and T wave integral which are used in determining the diagnosis of HCM of the examined pediatric subject,
the electronic unit connects an ECG machine to the examined pediatric subject, the ECG machine comprises the memory unit, and the computer interface system comprises the ACE module, and
the first set of ECG variables comprise up to 102 ECG variables and the pediatric individuals of the historic data comprise up to 27,085 pediatric individuals.
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