Detection of ECG Wave Components for the Prediction of Acute Coronary Syndrome - Brief Survey
Keywords:
Pre-processing, Segmentation, Feature Extraction, Training, TestingAbstract
An ACS (Acute Coronary Syndrome) is a term used to define the heart diseases like Heart attacks, Myocardial infarction, and Unstable Angina. The study described the Electrocardiogram is an important tool for measuring human health and disease detection. Electrocardiogram (ECG) signal consist of Components like waves, intervals and segments studied on the basis of time duration and size. PAN and TOMPKINS give the concept of QRS detection in the decade of eighty. Further several researchers developed various algorithms to detect QRS on the basis of derivative, wavelet transforms and other techniques. In this research we survey the progressive methods of detection of electrocardiogram wave components for the prediction of acute coronary syndrome by introducing electrocardiogram signal preprocessing, heartbeat segmentation, feature extraction and learning algorithms used. Additionally we depict some databases which is used for evaluation indicated by The AAMI standards were introduced by AAMI and are described in American National Standard Institute (ANSI/AAMI EC57:1998/(R) 2008) [16] for analyzing and describing the performance effect of cardiac rhythm and ST-segment evaluation algorithms. Sometimes monitoring and analyzing heartbeat ECG records are necessary. Most of the time there is a possibility of inaccuracy in ECG record analysis. This research becomes the alternative. It can provide essential information to doctors to carry out their diagnoses on patients.
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