Youssef Mohamed Ali
Omar Ahmed Diaa El-Din
Ahmed Raouf Aly
Andrew Gamal Fahmy
Supervised by: Dr. Diaa Salama &
Eng. Mahmoud Heidar
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.
ECG analysis is useful for determining heart health. As a result, cardiovascular disorders require the identification and classification of ECG signals. Not only is early prevention crucial, but so is rapid discovery and treatment. The classification of related ECG signals is extremely important in modern medicine, the electrocardiogram (ECG) is one of the four major regular examination items in medicine. ECG is the safest and most effective approach for diagnosing cardiovascular diseases. ECG measurement has become more convenient and faster as a result of advances in electronic information technology, which offers numerous benefits. ECG automated classification requires a large amount of data. Machine learning and deep learning networks have made significant progress in the recent years, not only in image processing, voice recognition, and a variety of other domains. It has also been widely used to assist in the diagnosis of cardiac illness using ECG signals. Also deep learning has been applied successfully for the classification of the arrhythmia from ECG signals.
According to the World Health Statistics 2019 report, heart diseases, humanity’s top killer, were the leading cause of death in the last two decades, accounting for 16 percent of all deaths. This type of sickness has such a significant impact on life expectancy. To diagnose cardiovascular disorders they’re certain diagnostic procedures, such as ECG, ultrasonic cardiogram (UGC), chest X-ray, and cardiac Magnetic Resonance Imaging (MRI), are currently widely employed. The ECG, in particular, plays a key role among these tests given to its low cost and ease of use. With the advancement of Artificial Intelligence (AI) technology, several machine learning approaches are being applied in the detection of ECG signal features, with the goal of resolving issues such as enormous volumes of ECG signal feature data and a heavy manual detection load
1.3 Problem Statement
1- Heart diseases are the top killer of humanity, it was the primary cause of death worldwide in the past two decades.
2- Cardiovascular disorders require the identification and classification of ECG signals. Not only is early prevention important, but so is rapid discovery and treatment.
3- There are massive amount of ECG signal feature data that may cause problems during the classification of ECG signals