Detection/exclusion of acute myocardial infarction using neural network analysis of measurements of biochemical markers

An apparatus for early detection of acute myocardial infarction (AMI) in a patient, comprising:

• means for performing timed measurements, at at least two different time instances occurring within 3 hours from admission of said patient, of concentration levels of at least two biochemical markers of AMI in plasma of said patient, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• a neural network structure for generating an indication of AMI based on a first predetermined set of said concentration levels and first empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said previous patients, said neural network structure being trained with said first empirical data to detect marker-patterns of AMI;
• and
• means for predicting the infarct size based on a second predetermined set of said concentration levels and second empirical data comprising concentration levels obtained from previous patients with different infarct sizes and a preclassification of the infarct sizes of said previous patients.

A method for early detection of acute myocardial infarction (AMI) in a patient, comprising the steps of:

• performing timed measurements, at at least two different time instances occurring within 3 hours from admission of said patient, of concentration levels of at least two biochemical markers of AMI in plasma of said patient, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• generating an indication of AMI based on a first predetermined set of said concentration levels and first empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said previous patients by using a neural network structure trained with said first empirical data to detect marker-patterns of AMI;
• predicting the infarct size based on a second predetermined set of said concentration levels and second empirical data comprising concentration levels obtained from previous patients with different infarct sizes and a preclassification of the infarct sizes of said previous patients;
• and
• generating, based on a third predetermined set of said concentration levels and third empirical data comprising concentration levels obtained from previous patients of different states regarding the time after onset of infarction and a preclassification of the times after onset for said previous patients, at least one time estimate representing a time period within which onset of infarction occurred,
• whereby an early detection of AMI, an early infarct size prediction and an early estimate of the time after onset of AMI are obtained.

A method for:

• a) early detection/exclusion of acute myocardial infarction (AMI) in a patient;
• b) predicting the infarct size;
• c) estimating the time after onset of infarction;
• d) assessing/excluding possible minor myocardial damage (MMD), said method comprising the steps of:
• measuring, with a predetermined timing and at at least two different time instances occurring within 3 hours from admission of said patient, concentration levels of at least two biochemical markers of AMI in plasma of said patient, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• generating an indication of AMI/non-diagnosis by making a comparison based on a first predetermined set of said concentration levels and first empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said previous patients by using a neural network structure trained with said first empirical data to detect marker-patterns of AMI;
• predicting the infarct size by making a comparison based on a second predetermined set of said concentration levels andsecond empirical data comprising concentration levels obtained from previous patients with different infarct sizes and a preclassification of the infarct sizes of said previous patients, provided an indication of AMI has been generated;
• generating, by making a comparison based on a third predetermined set of said concentration levels and third empirical data comprising concentration levels obtained from previous patients of different states regarding the time after onset of infarction and a preclassification of the times after onset for said previous patients, an estimate representing a time period within which onset of infarction occurred, provided an indication of AM/has been generated;
• excluding AMI provided said indication of AMI has not been generated for a predetermined period of time;
• and
• assessing/excluding MMD by making a comparison based on a fourth predetermined set of said concentration levels and fourth empirical data comprising concentration levels obtained from previous patients with MMD/without MMD and a preclassification of the presence/absence of MMD in said previous patients, provided AMI has been excluded.

A method for early detection of acute myocardial infarction (AMI) in a patient, comprising the steps of:

• measuring, with a predetermined timing and at at least two different time instances occurring within 3 hours from admission of said patient, concentration levels of least two biochemical markers of AMI in plasma of said patient, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• generating an indication of AMI based on a first predetermined set of said concentration levels and first empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said previous patients by using a neural network structure trained with said first empirical data to detect marker-patterns of AMI;
• and
• generating, based on a third predetermined set of said concentration levels and third empirical data comprising concentration levels obtained from previous patients of different states regarding the time after onset of infarction and a preclassification of the times after onset for said previous patients, an estimate of a time period within which onset of infarction occurred,
• whereby an early detection of AMI and an early estimate of the time after onset of AMI are obtained.

An apparatus for excluding acute myocardial infarction (AMI) in a patient, comprising:

• means for measuring, with a predetermined timing and within 3 hours from admission of said patient, concentration levels of at least two biochemical markers of AMI in plasma of said patient, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• means for generating an indication of AMI based on a first predetermined set of said concentration levels and first empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said previous patients by using a neural network structure trained with said first empirical data to detect marker-patterns of AMI;
• and
• means for excluding AMI as a diagnosis provided said indication of AMI has not been generated for a predetermined period of time.

A method for excluding acute myocardial infarction (AMI) as a diagnosis for a patient, comprising the steps of:

• performing timed measurements, at at least two different time instances occurring within 3 hours from admission of said patient, of concentration levels of at least two biochemical markers of AMI in plasma of said patient, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• generating an indication of non-diagnosis based on a first predetermined set of said concentration levels and first empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said patients, by using a neural network structure trained with said first empirical data to detect marker-patterns of AMI;
• excluding AMI as a diagnosis provided said indication of non-diagnosis has been generated for a predetermined period of time;
• and
• assessing/excluding minor myocardial damage (MMD) based on said concentration levels and fourth empirical data comprising concentration levels obtained from previous patients with MMD/without MMD and a preclassification of the presence/absence of MMD in said previous patients, provided AM/has been excluded as a diagnosis,
• whereby an early exclusion of AMI and an assessment/exclusion of MMD are obtained.

An apparatus for early detection of acute myocardial infarction (AMI) in a patient, comprising:

• means for measuring concentration levels of at least two biochemical markers of AMI in plasma of said patient and for generating signals representing said concentration levels, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• a neural network structure responsive to said signals and trained with empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said previous patients to detect marker-patterns of AMI, said neural network structure comprising at least two neural networks, each one of which is trained to detect AMI within an individual defined time period after onset of symptoms of infarction for generating an individual indication of AMI/non-diagnosis;
• and
• means for generating a conclusive indication of AMI based on said individual indications.

An apparatus for early detection of acute myocardial infarction (AMI) in a patient, comprising:

• means for measuring concentration levels of at least two biochemical markers of AMI in plasma of said patient and for generating signals representing said concentration levels, said at least two biochemical markers having different rates of appearance in said plasma at AMI;
• a first neural network structure responsive to the initial signals and trained with first empirical data comprising concentration levels obtained from previous patients with AMI/non-AMI and a preclassification of the presence/absence of AMI in said previous patients to detect marker-patterns of AMI, said first neural network structure comprising at least two neural networks, each one of which is associated with a separate period of time after onset of symptoms of infarction for generating an individual indication of AMI/non-diagnosis;
• means for generating a first indication of AMI/non-diagnosis based on said individual indications;
• and
• a second neural network structure responsive to said signals and trained with said first empirical data to detect marker-patterns of AMI, said second neural network structure comprising a feedback connection for recognizing temporal patterns of said concentration levels so as to generate a second indication of AMI provided said first indication of AMI was not generated.