Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs

Physiological Measurement, vol. 44, pp. 084003

Abstract

Abstract Objectives . Acute myocardial ischemia in the setting of acute coronary syndrome (ACS) may lead to myocardial infarction. Therefore, timely decisions, already in the pre-hospital phase, are crucial to preserving cardiac function as much as possible. Serial electrocardiography, a comparison of the acute electrocardiogram with a previously recorded (reference) ECG of the same patient, aids in identifying ischemia-induced electrocardiographic changes by correcting for interindividual ECG variability. Recently, the combination of deep learning and serial electrocardiography provided promising results in detecting emerging cardiac diseases; thus, the aim of our current study is the application of our novel Advanced Repeated Structuring and Learning Procedure (AdvRS&LP), specifically designed for acute myocardial ischemia detection in the pre-hospital phase by using serial ECG features. Approach . Data belong to the SUBTRACT study, which includes 1425 ECG pairs, 194 (14%) ACS patients, and 1035 (73%) controls. Each ECG pair was characterized by 28 serial features that, with sex and age, constituted the inputs of the AdvRS&LP, an automatic constructive procedure for creating supervised neural networks (NN). We created 100 NNs to compensate for statistical fluctuations due to random data divisions of a limited dataset. We compared the performance of the obtained NNs to a logistic regression (LR) procedure and the Glasgow program (Uni-G) in terms of area-under-the-curve (AUC) of the receiver-operating-characteristic curve, sensitivity (SE), and specificity (SP). Main Results . NNs (median AUC = 83%, median SE = 77%, and median SP = 89%) presented a statistically ( P value lower than 0.05) higher testing performance than those presented by LR (median AUC = 80%, median SE = 67%, and median SP = 81%) and by the Uni-G algorithm (median SE = 72% and median SP = 82%). Significance . In conclusion, the positive results underscore the value of serial ECG comparison in ischemia detection, and NNs created by AdvRS&LP seem to be reliable tools in terms of generalization and clinical applicability.

Authors 5

1. Agnese Sbrollini

   Marche Polytechnic University

   Affiliation as printed

   Università Politecnica delle Marche

   Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Ancona, 60121, ITALY

2. C. Cato ter Haar Aachen Cardiology Department

   Leiden University Medical Center · Amsterdam University Medical Centers

   Affiliation as printed

   Amsterdam University Medical Center

   Leiden University Medical Center

   Cardiology Department, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, Zuid-Holland, 2300 RC, NETHERLANDS

3. Micaela Morettini

   Marche Polytechnic University

   Affiliation as printed

   Università Politecnica delle Marche

   Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Ancona, 60121, ITALY

4. Laura Burattini

   Marche Polytechnic University

   Affiliation as printed

   Università Politecnica delle Marche

   Department of Information Engineering, Universita Politecnica delle Marche, via Brecce Bianche, Ancona, 60121, ITALY

5. Cees A. Swenne corresponding Aachen Cardiology Department

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center

   Cardiology Department, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, Zuid-Holland, 2300 RC, NETHERLANDS