ECG feature extraction using MFCC and KNN classification achieved 84% accuracy, 85% sensitivity, and 84% specificity in identifying myocardial infarction from normal signals.
Does MFCC feature extraction and KNN classification accurately identify myocardial infarction from ECG signals?
MFCC feature extraction combined with KNN classification shows promising accuracy (84%) in detecting myocardial infarction from ECG signals.
Feature extraction of electrocardiogram (ECG) signal is one of the essential steps to diagnose various cardiovascular disease (CVD). The signal is generated by the hearts electrical activity and able to reveal the abnormal activity of the heart. An accurate feature extraction method is important to produce better identification of ECG signal. ECG feature extraction using Mel Frequency Cepstrum Coefficient (MFCC), Discrete Wavelet transformation and KNN using euclidean distance as the classifier is proposed in this study. The model and testing of the proposed system were performed on the two types of data, normal and myocardial infarction (MI) labeled as abnormal, obtained from PTB-DB database. Total data used were 100 data, with 50 data for each condition. K-fold cross validation also applied to achieve a generalized result. According to the experimental, 13 features that obtained from MFCC shows good result. The accuracy, sensitivity and specificity were achieved 84%, 85% and 84% respectively.
Yusuf et al. (Sun,) conducted a other in Myocardial infarction (n=100). MFCC feature extraction and KNN classification was evaluated on Classification accuracy, sensitivity, and specificity. ECG feature extraction using MFCC and KNN classification achieved 84% accuracy, 85% sensitivity, and 84% specificity in identifying myocardial infarction from normal signals.
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