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Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity

Tang, T.B. and Chong, J.S. and Kiguchi, M. and Funane, T. and Lu, C.-K. (2021) Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29 . pp. 894-904.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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. © 2001-2011 IEEE.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Biomarkers; Decision making; Decision trees; Graph theory; Infrared devices; K-means clustering; Learning systems; Near infrared spectroscopy; Nursing; Supervised learning, Discriminating power; Functional connectivity; Functional near-infrared spectroscopy (fnirs); Heart rate variability; K-means clustering techniques; Occurrence probability; Professional training; Supervised machine learning, Students, biological marker, adult; algorithm; anxiety; Article; bootstrapping; conformational transition; correlation analysis; data analysis software; decision making; electrocardiogram; emotion; false discovery rate; female; functional connectivity; functional near-infrared spectroscopy; heart rate; heart rate variability; human; human experiment; k fold cross validation; machine learning; mathematical model; normal human; nursing student; optical density; parasympathetic tone; photoelectric plethysmography; physiological stress; posttraumatic stress disorder; questionnaire; random forest; reaction time; receiver operating characteristic; sensitivity and specificity; training; brain; near infrared spectroscopy; nuclear magnetic resonance imaging, Brain; Humans; Magnetic Resonance Imaging; Spectroscopy, Near-Infrared
ID Code:23761
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 10:01
Last Modified:19 Aug 2021 10:01

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