The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc may contain useful information about the nature of disease affecting the heart. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the heart rate variability signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Analysis of heart rate variability (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. The HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially nonstationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper deals with the analysis of eight types of cardiac abnormalities and presents the ranges of linear and nonlinear parameters calculated for them with a confidence level of more than 90%.