ECG Based Algorithm For Detecting Ventricular Arrhythmia And Atrial Fibrillation

As per the report of statistics sudden cardiac deaths affects about 30 percent of the population all around the world. In most cases these deaths are due to arrhythmias including ventricular tachycardia or ventricular fibrillation and atrial fibrillation. Ventricular arrhythmia is an abnormal ECG rhythm that occurs in the ventricles of the heart and is responsible for 75 to 85 percent of sudden deaths in persons with heart problems unless treated within seconds. Similarly, atrial fibrillation occurs due to abnormal ECG rhythm occurring in the upper chambers of the heart and causes the heart to quiver. These deaths occur due to hypertension and other cardiac related issues. Sudden deaths due to arrhythmia also occur in individuals who do not have high risk profiles. When a person is diagnosed with arrhythmia, patient is left with only few seconds to die. Long term ECG monitoring is the standard criterion for the diagnosis of arrhythmia, which is time consuming. So, detection and analysis of arrhythmia is vital as it has become one of the major causes of cardiac deaths. The project illustrates a simple algorithm for the detection of arrhythmia using a unique set of ECG features. Databases of the ECG signal recordings from the MIT physionet ATM, sampled at 250 Hz were used to evaluate the performance of algorithm. The algorithm presented in this paper provides a computationally efficient solution for the detection of ventricular arrhythmia and atrial fibrillation. This makes it ideal for a wide range of machine diagnosis application in biomedical field.