Detection of Cardiovascular Disease Using ECG Images in Machine Learning and Deep Learning

One of the most prominent tools for detecting cardiovascular problems is the electrocardiogram (ECG). The electrocardiogram (ECG or EKG) is a diagnostic tool that is used to routinely assess the electrical and muscular functions of the heart. Even though it is a comparatively simple test to perform, the interpretation of the ECG charts requires considerable amounts of training. Till recently, the majority of ECG records were kept on paper. Thus, manually examining and re-examining the ECG paper records often can be a time-consuming and daunting process. If we digitize such paper ECG records, we can perform automated diagnosis and analysis. The main goal of this project is to use machine learning to convert ECG paper records into a 1-D signal. This can be achieved by extracting the P, QRS, and T waves that exist in ECG signals to demonstrate the electrical activity of the heart using various techniques. The techniques include splitting the original ECG report into 13 Leads, extracting and converting into the signal, smoothing, converting them to binary images using threshold and scaling. Post-feature-extraction, dimension reduction techniques like Principal Component Analysis are applied to understand the data. Multiple classifiers like k-nearest neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), and Voting Based Ensemble Classifier are implemented, and based on the acceptable criteria on the accuracy, precision, recall, f1-score, and support, the model will be finalized. This final model will aid in the diagnosing of cardiac diseases, to detect whether a patient has/had Myocardial Infarction, Abnormal Heartbeat, or the patient is hale and healthy by inferring the ECG reports