Effective Delineation of ECG Signals for Operative Neural Disease Recognition and Classification

It is expected that cardiovascular disease will be the maj or cause of death worldwide. When it comes to heart disease and strokes, CAD (coronary artery disease), also known as “atherosclerotic heart disease,” is by far the most common cause of both. Plaque formation and cholesterol build-up in the interior of the heart's arteries lead to this condition. Chest pain (angina), myocardial ischemia (MI), and in rare cases, infarction, are all symptoms of a heart that is not getting enough blood and oxygen as the disease develops and the arteries narrow. An electrocardiogram (ECG) may have artefacts if they were recorded using poor quality equipment. Baseline drift, interference from power lines, and muscular tremors are all examples of artefacts that people often experience. Misdiagnosis of ischemia can be avoided if you know how to spot artefacts and get rid of them. Some of the many methods for detecting ischemia that have been published all have drawbacks, such as the need for time-consuming and complicated training of classifiers and decision-making algorithms. Because of this, a straightforward, trustworthy, and robust approach is necessary to counteract these drawbacks. Results from applying the suggested technique to a dataset of 10 delineations reveal a sensitivity (SE) of 99.89 and a positive predictivity (+ P) of 99.92. The suggested method makes use of a mathematical formula for robustly detecting such outliers. In order to simplify yet reliably classify myocardial ischemia, this function was developed.