A Comparison of Two ECG Inter-beat Interval Measurement Methods for HRV-Based MentalWorkload Prediction of Ambulant Users

Abstract

Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Extraction of inter-beat interval series requires processing of the electrocardiogram (ECG) signal. This processing
is critical for high quality RR series extraction and overall HRV measurement. Typically, the Pan-Tomkins peak detection algorithm is used. Recently, however, innovative modulation spectral based heart rate detection methods have been proposed. In this paper, we compare the performance of both algorithms and their effects on HRV measurement for mental workload assessment under physical activity. Experiments were conducted with 45 participants while they performed the NASA Revised Multi- Attribute Task Battery II (MATB-II) under different types and levels of physical activity. We show that modulation spectrum based methods perform better than conventional peak detection methods for mental workload prediction in lower levels of physical activity, particularly in the bike riding condition.