One of the most important causes of death in human societies is heart attack, which is affected by many factors. Therefore, there is a growing need to deal with it. Ventricular Tachycardia and VentricularFibrillation are two major factors of a heart attack that can lead to serious damages and even death, due to insufficiency of blood and oxygen to the brain. The only effective treatment to deal with these arrhythmias and prevent heart attack, is the application of electric shocks Defibrillation to the heart in the shortest time, which is external Defibrillation in most cases. Automated External Defibrillatorcan detect these arrhythmias promptly and accurately, and by employing an electric shock to patient’s heart, it can help the patient to survive. So far, several algorithms are proposed to identify Ventricular Tachycardia and VentricularFibrillation in both time domain and frequency domain. In this thesis, a new algorithm in the time domain is designed to detect these arrhythmias in real time. For detecting these arrhythmias, first we collected features such as heart rate, pulse width, rhythm of the waves and the average amplitude from the ECG. After feature extraction, according to the defined thresholds and mathematical relationships we categorize arrhythmias as VF or no VF. To evaluate performance the proposed algorithm, data from Medical databases such as MIT-BIH Database, MIT-BIH Malignant Database and CU Database was used. The results show that the proposed algorithm has sensitivity of 94.73% and specificity of 99.24%. The proposed algorithm has been successfully implemented on ARM microprocessor and it’s performance was evaluated in hardware. In addition, a point-to-point Receiver Operating Characteristic Curve was plotted and compared with other existing algorithms. This algorithm is very efficient to be applied in real-time ECG monitor system. Keywords: Automated External Defibrillator, Electrocardiogram (ECG), Real Time Detection, Ventricular Fibrillation, Ventricular Tachycardia