PMID- 38330822
OWN - NLM
STAT- MEDLINE
DCOM- 20240228
LR  - 20240228
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 170
DP  - 2024 Mar
TI  - Analysis of sympathetic responses to cognitive stress and pain through skin 
      sympathetic nerve activity and electrodermal activity.
PG  - 108070
LID - S0010-4825(24)00154-9 [pii]
LID - 10.1016/j.compbiomed.2024.108070 [doi]
AB  - We explored the non-invasive evaluation of the sympathetic nervous system (SNS) 
      by employing two distinct physiological signals: skin sympathetic nerve activity 
      (SKNA), extracted from electrocardiogram (ECG) signals, and electrodermal 
      activity (EDA), a well-studied marker in the context of the SNS assessment. Our 
      investigation focused on cognitive stress and pain; two conditions closely 
      associated with the SNS. We sought to determine if the information and dynamics 
      of EDA could be derived from the novel SKNA signal. To this end, ECG and EDA 
      signals were recorded simultaneously during three experiments aimed at 
      sympathetic stimulation, Valsalva maneuver (VM), Stroop test, and thermal-grill 
      pain test. We calculated the integral area under the rectified SKNA signal 
      (iSKNA) and decomposed the EDA signal to its phasic component (EDA(phasic)). An 
      average delay of more than 4.6 s was observed in the onset of EDA(phasic) bursts 
      compared to their corresponding iSKNA bursts. After shifting the EDA(phasic) 
      segments by the extent of this delay and smoothing the corresponding iSKNA 
      bursts, our results revealed a strong average correlation coefficient of 
      0.85+/-0.14 between the iSKNA and EDA(phasic) bursts, indicating a noteworthy 
      similarity between the two signals. We also reconstructed the EDA signals with 
      time-varying sympathetic (TVSymp) and modified TVSymp (MTVSymp) methods. Then we 
      extracted the following features from iSKNA, EDA(phasic), TVSymp, and MTVSymp 
      signals: peak amplitude, average amplitude (aSKNA), standard deviation (vSKNA), 
      and the cumulative duration during which the signals had higher amplitudes than a 
      specified threshold (HaSKNA). A strong average correlation of 0.89+/-0.18 was found 
      between vSKNA and subjects' self-rated pain levels during the pain test. Our 
      statistical analysis also included applying Linear Mixed-Effects Models to check 
      if there were significant differences in features across baseline and different 
      levels of SNS stimulation. We then assessed the discriminating power of the 
      features using Area Under the Receiver Operating Characteristic Curve (AUROC) and 
      Fisher's Ratio. Finally, using all the four EDA features, a multi-layer 
      perceptron (MLP) classifier reached the classification accuracies 95.56%, 89.29%, 
      and 67.88% for the VM, Stroop, and thermal-grill pain control and stimulation 
      classes. On the other hand, the highest classification accuracies based on SKNA 
      features were achieved using K-nearest neighbors (KNN) (98.89%), KNN (89.29%), 
      and MLP (95.11%) classifiers for the same experiments. Our comparative analysis 
      showed the feasibility of SKNA as a novel tool for assessing the SNS with 
      accurate classification capability, with a faster onset of amplitude increase in 
      response to SNS activity, compared to EDA.
CI  - Copyright (c) 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.
FAU - Baghestani, Farnoush
AU  - Baghestani F
AD  - Biomedical Engineering Department, University of Connecticut, United States of 
      America.
FAU - Kong, Youngsun
AU  - Kong Y
AD  - Biomedical Engineering Department, University of Connecticut, United States of 
      America.
FAU - D'Angelo, William
AU  - D'Angelo W
AD  - Biomedical Systems Engineering and Evaluation Department, Naval Medical Research 
      Unit Department, San Antonio, TX, United States of America.
FAU - Chon, Ki H
AU  - Chon KH
AD  - Biomedical Engineering Department, University of Connecticut, United States of 
      America. Electronic address: ki.chon@uconn.edu.
LA  - eng
PT  - Journal Article
DEP - 20240201
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
SB  - IM
MH  - Humans
MH  - *Galvanic Skin Response
MH  - *Sympathetic Nervous System/physiology
MH  - Pain
MH  - Electrocardiography/methods
MH  - Cognition
OTO - NOTNLM
OT  - Electrocardiogram (ECG)
OT  - Electrodermal Activity (EDA)
OT  - Machine learning
OT  - Pain
OT  - Physiological markers
OT  - Skin Sympathetic Nerve Activity (SKNA)
OT  - Stroop test
OT  - Sympathetic Nervous System (SNS)
OT  - Valsalva maneuver
COIS- Declaration of competing interest All authors have nothing to declare.
EDAT- 2024/02/09 00:42
MHDA- 2024/02/28 06:44
CRDT- 2024/02/08 18:12
PHST- 2023/10/27 00:00 [received]
PHST- 2023/12/28 00:00 [revised]
PHST- 2024/01/27 00:00 [accepted]
PHST- 2024/02/28 06:44 [medline]
PHST- 2024/02/09 00:42 [pubmed]
PHST- 2024/02/08 18:12 [entrez]
AID - S0010-4825(24)00154-9 [pii]
AID - 10.1016/j.compbiomed.2024.108070 [doi]
PST - ppublish
SO  - Comput Biol Med. 2024 Mar;170:108070. doi: 10.1016/j.compbiomed.2024.108070. Epub 
      2024 Feb 1.