Improved Artifact Mitigation From ECG Signal For Effective Ischemia Detection and Classification

The majority of people who experience a heart attack have CAD (coronary artery disease), the most prevalent form of which is myocardial ischemia (MI). When cholesterol and plaque narrow the lumen of the heart's arteries, ischemia occurs and the heart is deprived of the oxygen and nutrients it needs to function effectively. Angina, myocardial ischemia, and even infarction can result from this condition. The Electrocardiogram (ECG) is the gold standard for the non-invasive diagnosis of ischemia. An electrocardiogram (ECG) is a recording of the heart's electrical activity over time. In the electrocardiogram (ECG), this appears as abnormally high or low ST segments. Artifacts are introduced into the ECG during the recording process. Baseline drift, power line interference, and muscular tremors are all examples of common artefacts. Ischemia misdiagnosis can be avoided primarily via awareness of artefacts and their removal. Although many approaches for ischemia detection have been published, many of them have drawbacks, such as the need for time-consuming and complicated calculations to reach a conclusion or the need to train a classifier. Therefore, these drawbacks necessitate an approach that is easy to use, trustworthy, and robust. Newer methods of signal processing, such as multi-resolution analysis and the wavelet transform, have proven to be superior for ECG analysis, pre-processing, and delineation. We pre-processed the ECG recordings at the ECG landmarks using the wavelet transform toolkit in MATLAB code. When compared to conventional approaches, the proposed strategy performs admirably.