DOI: 10.5061/dryad.6q573n62j
title: Data from: Measurement of stress-induced sympathetic nervous activity using multi-wavelength photoplethysmography

description: The onset of stress triggers sympathetic arousal (SA), which causes
 detectable changes to physiological parameters such as heart rate, blood
 pressure, dilation of the pupils and sweat release. The objective
 quantification of SA has tremendous potential to prevent and manage
 psychological disorders. Photoplethysmography (PPG), a non-invasive method
 to measure skin blood flow changes, has been used to estimate SA
 indirectly. However, the impact of various wavelengths of the PPG signal
 has not been investigated for estimating SA. In this study, we explore the
 feasibility of using various statistical and nonlinear features derived
 from peak-to-peak (AC) values of PPG signals of different wavelengths
 (green, blue, infrared and red) to estimate stress-induced changes in SA
 and compare their performances. The impact of two physical stressors, Cold
 Pressor and Hand Grip, is studied on 32 healthy individuals. The results
 show that the nonlinear features are the most promising in detecting
 stress-induced sympathetic activity. TotalSampEn feature was capable of
 detecting stress-induced changes in SA for all wavelengths, whereas other
 features (Petrosian, AvgSampEn) are significant (AUC≥0.8$) only for IR and
 Red wavelengths. The outcomes of this study can be used to make device
 design decisions as well as develop stress detection algorithms.
description: The data consists of the computed nonlinear, linear and fractal
 statistical features from all subjects. These are saved as CSV files for
 two cohorts, SSNA and MSNA.  The data is sorted into
 MSNA and SSNA as it is being reused for a different publication. But for
 the reader's info, the acronyms stand for muscle (MSNA) and skin
 sympathetic nerve activity (SSNA), respectively. For the analysis, these
 should be combined, as the separation of the features is not relevant to
 this investigation. The code consists of loading data
 from the CSV files and computing the p-values that correspond to Table
 4.
description: <strong>Software:  </strong>
 Users should install the Python environment. The
 “requirements.txt” file includes all required python libraries.
 Download the dataset and you can find the following
 zip file 
 <strong>DATA_AND_CODE.zip</strong> 
  <strong>data</strong> 
 MSNA_app_entropy.csv
 SSNA_app_entropy.csv 
   <strong>data_processing</strong> 
   data_process.py 
 <strong>p_value_dir</strong> 
 AUC_value.csv
 P_value_significance.csv 
  <strong>error_bar_dir</strong> 
 HG.png CP.png 
 <strong>main.py</strong>
 <strong>requirements.txt</strong>
 <strong>Read_me_file.pdf</strong>
 <strong>File
 descriptions:</strong> 
 <strong>data</strong> folder includes all the datasets and
 must be within your working directory for the analysis to work. 
  Example: MSNA_app_entropy.csv = ”MSNA (muscle skin sympathetic
 nerve activity)” _” feature name” “.csv “extension  
 <strong>data_process.py:</strong> this
 file includes p-value and errorbar generator functions with proper
 documentation. 
 <strong>AUC_value.csv</strong>: this file includes the
 significance of the AUC (area under the curve) value for different events
 such as a non-stress event to stress event (AC) (Table 5 in the manuscript
 shows the same value as mentioned in this file).  
 <strong>P_value_significance.csv</strong>: this file includes
 the significance of p_value for different events such as pre-stress to
 cold pressor to recovery (ABC) (Table 4 in the manuscript shows the same
 value as mentioned in this file). 
 <strong>Error_bar_dir.zip</strong>: this file includes an
 errorbar for different features such as Mean, SD, Katz, Petrosian,
 Higuchi, app_entropy, sample_entropy, TotalSampEn, AvgSampEn (Figure 5 to
 7 in the manuscript shows the same value as mentioned in this
 file). 
 <strong>Main.py</strong> generates the result for AUC, p_value
 and error_bar into the p_value_dir and error_bar_dir folder.
 <strong>Requirement.txt:</strong> all required
 python libraries need to be installed. 
 <strong>Read_me_file.pdf</strong> includes all directions for
 users. 
  <strong>Note</strong>: <em>Please cite this
 paper and collaborate with the authors if you are using this
 dataset.</em>