Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment

Background: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive technique for studying brain hemodynamics. Since brain hemodynamics also involves components from the heart rate (HR), it is possible to extract the HR signal from the fNIRS (EHR). By removing global systemic changes from th...

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Main Authors: Mahya Mirbagheri, Naser Hakimi, Elias Ebrahimzadeh, S. Kamaledin Setarehdan
Format: Article
Language:English
Published: Elsevier 2020-01-01
Series:Informatics in Medicine Unlocked
Online Access:http://www.sciencedirect.com/science/article/pii/S2352914819303478
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spelling doaj-995cf7459e814887954d97f5b45fb9452020-11-25T02:39:36ZengElsevierInformatics in Medicine Unlocked2352-91482020-01-0118Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessmentMahya Mirbagheri0Naser Hakimi1Elias Ebrahimzadeh2S. Kamaledin Setarehdan3Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IranControl and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IranControl and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, CanadaControl and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Corresponding author. North Kargar st., Tehran, 1439957131, Iran.Background: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive technique for studying brain hemodynamics. Since brain hemodynamics also involves components from the heart rate (HR), it is possible to extract the HR signal from the fNIRS (EHR). By removing global systemic changes from the EHR, we hypothesized that the EHR includes physiological information beyond HR, which is the functional response of the brain. Method: To evaluate this hypothesis, the mental states of 10 healthy subjects were assessed under a stressful condition induced using the Montreal Imaging Stress Task (MIST). EHR and Reference HR (RHR) signals were then extracted from fNIRS and ECG, respectively. Next, a method based on Independent Component Analysis (ICA) was applied in order to discriminate the independent sources in the EHR signal. Thereafter, the least correlated components with RHR were reconstructed, which is the so-called target signal of EHR¯. Results: Similarity measurement of EHR¯ with fNIRS signals revealed a significant correlation between them. To show the effectiveness of the existing information in EHR¯ signals, they were employed for mental stress assessment by using the SVM classifier, leading to an accuracy of 97.3 ± 0.9%, which was 13%, 30.8% and 1.8% greater than those of the fNIRS, RHR, and EHR signals, respectively. Conclusions: We showed not only that the EHR¯ signal is a brain-related response, but also that it contains useful information for mental stress level measurement. Moreover, it is concluded that the EHR signal includes information from both brain and heart, which is hence useful for applications in which brain and heart functions are altered. Keywords: Functional near infrared spectroscopy, Heart rate, Stress assessment, Brain hemodynamic response, Independent component analysishttp://www.sciencedirect.com/science/article/pii/S2352914819303478
collection DOAJ
language English
format Article
sources DOAJ
author Mahya Mirbagheri
Naser Hakimi
Elias Ebrahimzadeh
S. Kamaledin Setarehdan
spellingShingle Mahya Mirbagheri
Naser Hakimi
Elias Ebrahimzadeh
S. Kamaledin Setarehdan
Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment
Informatics in Medicine Unlocked
author_facet Mahya Mirbagheri
Naser Hakimi
Elias Ebrahimzadeh
S. Kamaledin Setarehdan
author_sort Mahya Mirbagheri
title Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment
title_short Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment
title_full Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment
title_fullStr Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment
title_full_unstemmed Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment
title_sort quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment
publisher Elsevier
series Informatics in Medicine Unlocked
issn 2352-9148
publishDate 2020-01-01
description Background: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive technique for studying brain hemodynamics. Since brain hemodynamics also involves components from the heart rate (HR), it is possible to extract the HR signal from the fNIRS (EHR). By removing global systemic changes from the EHR, we hypothesized that the EHR includes physiological information beyond HR, which is the functional response of the brain. Method: To evaluate this hypothesis, the mental states of 10 healthy subjects were assessed under a stressful condition induced using the Montreal Imaging Stress Task (MIST). EHR and Reference HR (RHR) signals were then extracted from fNIRS and ECG, respectively. Next, a method based on Independent Component Analysis (ICA) was applied in order to discriminate the independent sources in the EHR signal. Thereafter, the least correlated components with RHR were reconstructed, which is the so-called target signal of EHR¯. Results: Similarity measurement of EHR¯ with fNIRS signals revealed a significant correlation between them. To show the effectiveness of the existing information in EHR¯ signals, they were employed for mental stress assessment by using the SVM classifier, leading to an accuracy of 97.3 ± 0.9%, which was 13%, 30.8% and 1.8% greater than those of the fNIRS, RHR, and EHR signals, respectively. Conclusions: We showed not only that the EHR¯ signal is a brain-related response, but also that it contains useful information for mental stress level measurement. Moreover, it is concluded that the EHR signal includes information from both brain and heart, which is hence useful for applications in which brain and heart functions are altered. Keywords: Functional near infrared spectroscopy, Heart rate, Stress assessment, Brain hemodynamic response, Independent component analysis
url http://www.sciencedirect.com/science/article/pii/S2352914819303478
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