Key result
An automatic technique exploiting cross-correlation between ECG signals detected myocardial infarction with an accuracy of 91.5%, specificity of 86.5%, and sensitivity of 95.6%.
Why the study?
Detecting heart problems from ECG signals conventionally requires an expert to interpret them, motivating an automatic detection technique for myocardial infarction.
Does an automatic technique exploiting cross-correlation between ECG signals accurately detect myocardial infarction?
Does an automatic technique exploiting cross-correlation between ECG signals accurately detect myocardial infarction?
A simple automatic technique using cross-correlation of ECG signals can detect myocardial infarction with 91.5% accuracy, potentially reducing the need for expert interpretation.
Electrocardiograph (ECG) signals are used to monitor heart activities, which is a non-invasive technique. To detect a problem in the heart, an expert is required to read these signals and diagnose a disease. In this paper, a simple and fast automatic technique is proposed to know if a person is going through a Myocardial Infarction. The proposed technique exploits the cross-correlation between the ECG signals and does not require an expert to read ECG signals. Data acquired from an online resource contains baseline wanders and noise. To remove this noise discrete-wavelet-transform and moving average filters are used. A threshold of 0.54 is chosen for the classification of the patient as a normal or going through myocardial infarction. If the cross-correlation is above 0.54, the patient is classified as normal and vice versa. Our simulation results show that the accuracy, specificity, and sensitivity of the proposed technique are respectively 91.5%, 86.5%, and 95.6%.
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Ghafoor et al. (2020) studied Myocardial Infarction. Cross-correlation between ECG signals was evaluated on Detection of Myocardial Infarction. An automatic technique exploiting cross-correlation between ECG signals detected myocardial infarction with an accuracy of 91.5%, specificity of 86.5%, and sensitivity of 95.6%.
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