The role of Basal HRV assessed through wavelet transform in the prediction of anxiety and affect levels: a case study

Abstract

The present paper is a designed case study to understand the potential role of heart rate variability (HRV) to predict different levels of anxiety and affect in a non-clinical sample by Wavelet Transform Tools. Trait anxiety was evaluated through the Spielberger’s State-Trait Anxiety Inventory. Positive and negative affect scores were measured through the Positive (PA) and Negative (NA) Affect Schedule. Electrocardiogram (ECG) was recorded during 4 min in basal conditions. The ECG data was analyzed using Wavelet Transform Daubechies order 4 as kernel. Our aim is investigate whether HRV, assessed by the wavelet transform decomposition in 8 levels of frequency, would be able to characterize trait anxiety (TA), PA and NA characteristics. Correlation analysis were conducted between each psychological parameter (TA, PA and NA) and the values of frequency levels. The results showed a weak but relevant tendency between frequency level and individual trait or affective score. Thus, the present study suggests that resting HRV is efficient to predict anxiety trait and affective trait and state. Beyond, the results points to the need of introducing different stimulations or tasks capable of modulating HRV and evidencing its association with distinct psychophysiological patterns.