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 approaches for the detection of heart blocks in ECG signals. Spectral estimations such as the periodogram power spectrum, Blackman-Tukey power spectrum and spectrogram time1requency distribution are employed to analyze ECG variations. Window functions are applied to the spectrums which are Boxcar, Hamming and Bartlett window. Seven subjects are identified: normal, first degree heart block, second degree heart block type /, 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) for both the periodogram and Blackman-Tukey method. The
results revealed that the periodogram power spectrum with Boxcar window can be used to differentiate between
normal and heart block subjects, while the spectogram time-frequency distribution is used to give better
characterization of ECG parameters in term three dimension: time, frequency and power intensity. These
analyses can be used to construct ECG monitoring and analyzing system for heart blocks detection
