Introduction: Significant features of the ECG signal include the P wave, the QRS complex (A combination of the Q wave, R wave and S wave, the __ampersandsignldquo;QRS complex__ampersandsignrdquo; represents ventricular depolarization.) and the T wave. This paper focuses on the identification of the P wave and T wave Because of their low amplitude; it is difficult to identify the position of the P and T waves. Objective: Present an algorithm for the detection of QRS (A combination of the Q wave, R wave and S wave), T and P waves of ECG. The extraction will require auxiliary investigations in many methodological aspects. The key gain of this method of detection is that the long-term ECG signal takes less time Methods: Feature Extraction is performed for each subject to shape distinctive, customized signatures. Preprocessing removes or suppresses noise from the raw ECG signal. The elimination of baseline wandering and noise reduction is one of the most common issues. Discrete Wavelet Transform is employed to remove the noise. High-frequency components of the ECG signals reduce as lower information is removed from the original signal. When the lower info is eliminated, the signal is clearer and the noise disappears when noise is defined by components of high frequency picked up in the transmission pathways. Results: The QRS complex was detected which is based on the maximum slope threshold. The ECG data files were used to test this QRS detection method. Based on the information of the identified QRS complexes, the P waves and the T waves can also be detected. From these waves, we are identified amplitude and intervals of ECG data files Conclusion: In this thesis, present an algorithm for the detection of QRS, T and P waves of ECG. The extraction will require auxiliary investigations in many methodological aspects. The key gain of this method of detection is that the long-term ECG signal takes less time.