Electrocardiogram evaluation using z-score based standards

An electrocardiogram (ECG) system comprising:

• an electronic unit configured to connect an ECG machine to an examined subject, the ECG machine comprises a memory unit, and a computer interface system comprises an adaptive confirmatory enhancement (ACE) module, machine learning algorithms and an oversAIght module;
• the 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;
• a computer interface system configured to:
• digitally transform electrical values obtained by the electronic unit from the examined subject to generate digital ECG values of a second set of ECG variables of the examined subject;
• determine that the digital ECG values of the second set of ECG variables of the examined subject are abnormal based on a binary classification model, a probabilistic model, and a transformer model, wherein the abnormal digital ECG values 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 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;
• determine a disease diagnosis of the examined subject based on a determination that the digital ECG values of the second set of ECG variables of the examined subject are abnormal;
• and
• determine whether the disease of the examined subject is accurate based on periodic integration of new ECG data into the database by performing continuous machine learning to create ECG-disease associations and incorporation of 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.

A method, comprising:

• digitally transforming, by a computer interface system of an ECG system, electrical values obtained by an electronic unit from an examined subject to generate digital ECG values of a set of ECG variables of an examined subject;
• determining that the digital ECG values of the set of ECG variables of the examined subject are abnormal based on a binary classification model, a probabilistic model, and a transformer model, wherein the abnormal digital ECG values comprise Z-scores with two standard deviations above a mean Z-score of a set of ECG variables from a historic data of healthy individuals or Z-scores with two standard deviations below the mean Z-score of the set of ECG variables from the historic data of healthy individuals;
• determining, by the computer interface system of the ECG system, a disease diagnosis of the examined subject based on a determination that the digital ECG values of the set of ECG variables of the examined subject are abnormal;
• and
• determining, by an adaptive confirmatory enhancement (ACE) module of the ECG system, whether the disease diagnosis of the examined subject is accurate based on periodic integration of new ECG data into a database by performing continuous machine learning to create ECG-disease associations and incorporation of another set of new normal and abnormal ECG values of the another set of ECG variables by incorporating the continuous machine learning of undiscovered patterns that are used to discern the another set of new normal and the abnormal ECG values of the second set of ECG variables to further create the ECG-disease associations.