| CPC A61B 5/341 (2021.01) [A61B 5/271 (2021.01); A61B 5/352 (2021.01); A61B 5/355 (2021.01); A61B 5/725 (2013.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); A61B 2560/02 (2013.01)] | 10 Claims |
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1. A method for early detection of a presence of coronary heart disease (CHD) and/or cardiac arrhythmia (HRD) in a patient to be screened, the method comprising:
(i) non-invasive recording of ECG signals at the heart of the patient in the resting state;
(ii) filtering processing of the recorded ECG signals;
(iii) converting the filtered ECG signals into orthogonalized measured values based on vectorcardiography; and
(iv) inputting the orthogonalized measured values into a system based on artificial intelligence, in which already known findings data of reference patients is stored, wherein a diagnosis of CHD and/or HRD is made for the screened patient by comparing the entered orthogonalized measured values with the findings data of the reference patients within the AI system;
wherein the AI system is trained prior to step (iv),
wherein the AI system comprises at least one neural network,
wherein, for training the AI system, a number of specific learning values is input therein, the number of specific learning values being between 10 and 30 or the number of specific learning values being 20,
wherein the specific learning values are determined by the following sequence of steps:
(v) providing measured values of a set (M) of patients with a known finding, wherein these measured values are orthogonalized based on vectorcardiography;
(vi) providing a plurality of time series parameters and at least one statistical method;
(vii) forming a 3D matrix, wherein the orthogonalized measured values of the set of patients define the rows, the time series parameters define the columns and the at least one statistical method defines the depth of this matrix;
(viii) classifying all pairs of values of the 3D matrix according to the principle of the “Area-under-Curve” (AUC) calculation;
(ix) selecting a pair of values from the set in step (viii) with the highest AUC value;
(x) checking another pair of values from the set in step (viii), and selecting this pair of values, if a limit value for a correlation with the value pair of step (ix) is smaller than 1.65/√N, where N=number of the data points or parameter statistics (patients) in step (vi);
(xi) repeating step (x) for another pair of values from the set in step (viii), and selecting this pair of values if a limit value for a correlation with the previously selected value pairs is in each case smaller than 1.65/√N; and
(xii) repeating the steps (ix) to (xi) until a predetermined number of value pairs is reached, which are then defined as specific learning values for training the AI system.
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