AI Article Synopsis

  • This study introduces a new analytical framework using machine learning to identify dynamic task-based functional connectivity (FC) features as biomarkers for emotional sensitivity in nursing students, utilizing functional Near-Infrared Spectroscopy (fNIRS) technology.
  • Through a sliding window correlation analysis, researchers discovered four recurring connectivity states, leading to findings that nursing students were more affected by emotional stimuli compared to registered nurses, who showed a single task-relevant state.
  • The study highlights that the dynamic FC features were more accurate indicators of emotional sensitivity (81.65%) than traditional heart rate variability measures (71.03%) and suggests potential applications in professional training for nursing regarding emotional sensitivity.

Article Abstract

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

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Source
http://dx.doi.org/10.1109/TNSRE.2021.3078460DOI Listing

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