Omar Ahmed Diaa El-Din
Andrew Gamal Fahmy
Ahmed Raouf Aly
Dr. Diaa Salama Abd Elminaam
Cardiovascular Disease (CVD) is a substantial health burden and cause of death worldwide. The electrocardiogram (ECG) is an electro-physiological signal that contains a wide range of valuable information about the electrical activity of the heart. Classification of the electrocardiogram (ECG) signals plays a crucial role in clinical diagnosis of heart disease. Analyzing the Electrocardiogram (ECG) is the current method of disease detection. Unfortunately, finding professionals to examine a big volume of ECG data takes up excessive medical time and money. As a result, machine learning-based methods for recognising ECG characteristics have steadily gained traction. These traditional methods have some limitations, such as the need for manual feature recognition, complex models, and a long learning time. Our goal is to solve these problems and predict certain heart diseases by classifying ECG signals using machine learning.
• Our goal is to reduce the death percentage of heart diseases, Early recognition of heart diseases will help us to reduce it.
• Detect and classify cardiac arrhythmias of patients from the ECG signal.
• Utilizing machine learning and improve optimization algorithms in ECG classification.
• Perform better results than simillar systems.
Our proposed methods aim to recognize and classify ECG signals. The methods used will help in effectively evaluating heart health easily. The methods are:
• Collect an ECG dataset and start data preprocessing to eliminate any inconsistent or duplicate data.
• Extract features that help to reduce the time and computation for the classification.
• work on several machine learning and deep learning approaches to reach better performance.
• Use metaheuristic optimization algorithms>2. Left bundle branch block
3. Right bundle branch block
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Documents and Presentations
You will find here the documents and presentation for our proposal.
You will find here the documents and presentation for our SRS.
You will find here the documents and presentation for our SDD.
You will find here the documents and presentation for our Thesis