A Signal Processing Approach to Post-ACS Patients Risk Stratification Using ECG
Date Issued
2012
Date
2012
Author(s)
Wu, Chung-Hao
Abstract
Acute coronary syndrome (ACS), caused by rupture of an atherosclerotic plaque and partial or complete thrombosis of the infarct-related artery, has been on the first three places of the top ten causes of death in Taiwan for many years. It is of concern whether these patients have grave prognosis under current medical treatment. Traditionally, TIMI score remained the most popular method for healthcare professionals, which uses information like age, aspirin use, cardiac biomarker as scores to evaluated clinical outcome after ACS. Electrocardiogram (ECG) is also a widely-used instrument for patients with cardiovascular disease. Recently, research focus on whether we could identify high risk patients through ECG reorganization due to it is quick, noninvasive, and easy to use.
An ECG analysis system, including preprocessing, beat detection, feature extraction, and using machine learning to make prediction, is proposed. Each part in this system is enhanced by new algorithms or techniques. The design concept of this system is to provide real-time ECG analysis, so a real-time ensemble empirical mode decomposition (EEMD) is proposed. Furthermore, using machine learning to make prediction achieves better stratification outcomes than that made by using inspection or statistics. Machine learning can find the information hidden in features, even if the used features are affected by multiple medical factors, while users have to use much more sophisticated features when they make prognosis by inspection or multivariate statistical analysis. On the other words, traditionally users extract specific information by themselves, while machine learning does it automatically. A set of new features are proposed and proved work via artificial neural network. Finally, we also give simple decision steps so that experts can easily adopt even if they do not use machine learning.
Subjects
ACS prognosis
ECG
Empirical Mode Decomposition
Artificial Neural Network
SDGs
Type
thesis
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