Heart rate variability (HRV) is a non-invasive alternative to analyze the role of the autonomic nervous system (ANS) on heart functioning. Many tools have been developed to analyze collected cardiac data. Among them, the Central Tendency Measure (CTM) is a quantitative method for variability analysis of RR intervals. The values of the CTM must be between 0 and 1 (inclusive) for different radius, which follows the intrinsic characteristics of each time series. Using the conventional CTM, the successive differences of the time series may be calculated, and it can classify and differentiate the disturbances in the ANS involving HRV. This method was extended (e-CTM) to analyze the differences between RR interval time series. In this extension, a new parameter is added, which allows analysis of long time intervals, instead of successive and adjacent RR intervals. The ability of the e-CTM to differentiate the groups of the RR interval time series was verified with 145 RR interval time series divided into three groups: subjects with congestive heart failure, healthy subjects, and nurses during one hour of their workday. Results evidence that the new parameter added differentiates the group with pathology (and subsequent impairment of ANS) and group under stress at work (temporary impairment of ANS). These results suggest that the e-CTM is capable of detection long-term variations in the HRV according to the ANS impairment.