دورية أكاديمية
Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs
| العنوان: | Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs |
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| المؤلفون: | Sbrollini, A., Haar, C.C. ter, Leoni, C., Morettini, M., Burattini, L., Swenne, C.A. |
| المصدر: | Physiological Measurement |
| سنة النشر: | 2023 |
| المجموعة: | Leiden Repository (Leiden University) |
| مصطلحات موضوعية: | acute myocardial ischemia, ambulance ECG, serial electrocardiography, artificial intelligence, neural network, pre-hospital ECG |
| الوصف: | 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | lumc-id: 185380891; https://hdl.handle.net/1887/3722069Test |
| DOI: | 10.1088/1361-6579/ace241 |
| الإتاحة: | https://doi.org/10.1088/1361-6579/ace241Test https://hdl.handle.net/1887/3722069Test |
| رقم الانضمام: | edsbas.39B52B18 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1088/1361-6579/ace241 |
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