Predicting Vasovagal Reactions to Needles from Facial Action Units

Author:

Rudokaite Judita12ORCID,Ertugrul Itir Onal3,Ong Sharon1,Janssen Mart P.2ORCID,Huis in ‘t Veld Elisabeth12ORCID

Affiliation:

1. Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, 5037 AB Tilburg, The Netherlands

2. Donor Studies, Department of Donor Medicine Research, Sanquin Research, Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands

3. Department of Information and Computing Sciences, Utrecht University, Heidelberglaan 8, 3584 CS Utrecht, The Netherlands

Abstract

Background: Merely the sight of needles can cause extreme emotional and physical (vasovagal) reactions (VVRs). However, needle fear and VVRs are not easy to measure nor prevent as they are automatic and difficult to self-report. This study aims to investigate whether a blood donors’ unconscious facial microexpressions in the waiting room, prior to actual blood donation, can be used to predict who will experience a VVR later, during the donation. Methods: The presence and intensity of 17 facial action units were extracted from video recordings of 227 blood donors and were used to classify low and high VVR levels using machine-learning algorithms. We included three groups of blood donors as follows: (1) a control group, who had never experienced a VVR in the past (n = 81); (2) a ‘sensitive’ group, who experienced a VVR at their last donation (n = 51); and (3) new donors, who are at increased risk of experiencing a VVR (n = 95). Results: The model performed very well, with an F1 (=the weighted average of precision and recall) score of 0.82. The most predictive feature was the intensity of facial action units in the eye regions. Conclusions: To our knowledge, this study is the first to demonstrate that it is possible to predict who will experience a vasovagal response during blood donation through facial microexpression analyses prior to donation.

Funder

ZonMW Veni project “FAINT”

Stichting Sanquin Bloedvoorziening

Publisher

MDPI AG

Subject

General Medicine

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