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The electrocardiogram (ECG) is a non-invasive test that records the electrical activity of the heart and is important in the investigation of cardiac abnormalities. Each portion of the ECG waveform carries various types of information for the cardiologists analyzing patient's heart condition. ECG interpretation at the present time remains dependent manually in time domain. It is difficult for the cardiologists to make a correct diagnosis of cardiac disorder. A computerized interpretation of ECG is needed in order to make the diagnosis more efficient. This paper discusses the use of digital signal processing approach for the detection of heart blocks in ECG signals. Signal analysis techniques such as the periodogram power spectrum and spectrogram time-frequency analysis are employed to analyze ECG variations. Seven subjects are identified: normal, first degree heart block, second degree heart block type I, second degree heart block type II, Third degree heart block, right bundle branch block and left bundle branch block. Analysis results revealed that normal ECG subject is able to maintain higher peak frequency range (8 Hz), while heart block subjects revealed a significant low peak frequency range (< 4 Hz). The results revealed that the periodogram power spectrum can be used to differentiate between normal and heart block subjects, while the spectogram time-frequency analysis is used to give better characterization of ECG parameters. These analyses can be used to construct ECG monitoring and analyzing system for heart blocks detection.