The stress response primes the human body to overcome a challenge or threat, and is governed by the sympathetic (SNS) and parasympathetic nervous systems (PNS). However, there is a great need to objectively identify stress, as overactivation of the SNS is dangerous to health. This PhD aimed to produce a heart rate variability (HRV) based measure of stress, to outperform the state-of-the- art and objectively identify levels of stress. Classification Angle (ClassA), a nonlinear framework based on second-order-difference plots of HRV, was developed to achieve this aim; ClassA produces four metrics, which include measures of SNS and PNS dynamics, and was tested on the HRV of ten males and ten females, exposed to periods of rest and mental (arithmetic) stress. The framework was compared to the state-of-the-art temporal, spectral and nonlinear measures. Only the heart rate of the males produced significant differences (p-value < 0.01) between the HRV from the rest and stress epochs. In contrast, two of the ClassA metrics were able to discern stress in both the males and females, with three-dimensional plots of the ClassA metrics spatially separating the HRV from the rest and stress epochs. The ability of the framework to distinguish between levels of stress was then tested on the cardiac signals of 28 day-surgery patients, recorded before and during surgery. Pain was defined as a stressor, and the ClassA framework produced significant differences between the HRV from the patients who experienced extreme pain, and those who did not. Furthermore, the ClassA framework has proved unique in accounting for sex-differences in the stress response, and in analysing only ten seconds of HRV data, whilst the state-of-the-art require five minutes. The development of the ClassA framework therefore achieves the PhD aim, and is the first step in the creation of a tool to objectively identify stress.