Key result
Adaptive threshold ECG wavelet analysis achieves up to ~94% accuracy for classifying MI.
Why the study?
Does a multiresolution wavelet analysis with adaptive thresholding accurately classify anterior and inferior myocardial infarction from normal ECGs?
Does a multiresolution wavelet analysis with adaptive thresholding accurately classify anterior and inferior myocardial infarction from normal ECGs?
A multiresolution wavelet analysis with adaptive thresholding provides high accuracy for classifying inferior and anterior myocardial infarction from ECG signals.
In this paper, classification of anterior and inferior myocardial infarction from normal cases is done using the changes happening in ECG waves. Depth of Q peak and elevation in ST segment is taken in consideration for classification purpose. A multiresolution approach along with an adaptive thresholding is used to extract these ECG features. Classification of inferior myocardial infarction (IMI) and anterior myocardial infarction (AMI) is done using a simple adaptive threshold (SAT) method. The sensitivity, specificity and accuracy is 93.22%, 94.28% and 93.61% respectively in case of IMI and in AMI cases its 83.33%, 88.57%, 86.15% respectively. Classification is also done using artificial neural network, but its performance is comparatively low. This may be due to inadequacy of data available. The PTB diagnostic ECG database is used for evaluation of the methods.
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Remya et al. (2016) studied Myocardial Infarction. Simple adaptive threshold (SAT) method using multiresolution wavelet analysis vs. Artificial neural network was evaluated on Classification accuracy, sensitivity, and specificity for inferior and anterior myocardial infarction. A simple adaptive threshold method using multiresolution wavelet analysis of ECG achieved 93.61% accuracy for inferior myocardial infarction and 86.15% for anterior myocardial infarction.
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