This research compares methods for measuring pilot mental workload (MWL) from the electrocardiogram (ECG) signal. ECG-based metrics have been used extensively in MWL research. Heart rate (HR) and heart-rate variability (HRV) exhibit changes in response to varying levels of task demand. Classical methods for HRV analysis examine the ECG signal in the linear time and frequency domains. More contemporary research has advanced the notion that nonlinear elements contribute to cardiac control and ECG signal generation, spawning development of analytical techniques borrowed from the domain of nonlinear dynamics (NLD). Applications of nonlinear HRV analysis are substantial in clinical diagnosis settings; however, such applications are less frequent in MWL research, especially in the aviation domain. Specifically, the relative utility of linear and non-linear HRV analysis methods has not been fully assessed in pilot MWL research.
This thesis contributes to aforementioned research gap by comparing a non-linear HRV method, utilizing transition probability variances (TPV), to classical time and frequency domain methods, focusing the analysis on sensitivity and diagnosticity. ECG data is harvested from a recent study characterizing spatial disorientation (SDO) risk amongst three candidate off-boresight (OBS) helmet-mounted display (HMD) symbologies in a tactically relevant live-flight task. A comparative analysis of methods on this dataset and supplemental workload analysis for the HMD study are presented. Results indicate the TPV method may exhibit higher sensitivity and diagnosticity than classical methods. However, limitations of this analysis warrant further investigation into this question.