6 research outputs found
GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data
The stress detection problem is receiving great attention in related research
communities. This is due to its essential part in behavioral studies for many
serious health problems and physical illnesses. There are different methods and
algorithms for stress detection using different physiological signals. Previous
studies have already shown that Galvanic Skin Response (GSR), also known as
Electrodermal Activity (EDA), is one of the leading indicators for stress.
However, the GSR signal itself is not trivial to analyze. Different features
are extracted from GSR signals to detect stress in people like the number of
peaks, max peak amplitude, etc. In this paper, we are proposing an open-source
tool for GSR analysis, which uses deep learning algorithms alongside
statistical algorithms to extract GSR features for stress detection. Then we
use different machine learning algorithms and Wearable Stress and Affect
Detection (WESAD) dataset to evaluate our results. The results show that we are
capable of detecting stress with the accuracy of 92 percent using 10-fold
cross-validation and using the features extracted from our tool.Comment: 6 pages and 5 figures. Link to the github of the tool:
https://github.com/HealthSciTech/pyED
Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS
The world has been affected by COVID-19 coronavirus. At the time of this
study, the number of infected people in the United States is the highest
globally (7.9 million infections). Within the infected population, patients
diagnosed with acute respiratory distress syndrome (ARDS) are in more
life-threatening circumstances, resulting in severe respiratory system failure.
Various studies have investigated the infections to COVID-19 and ARDS by
monitoring laboratory metrics and symptoms. Unfortunately, these methods are
merely limited to clinical settings, and symptom-based methods are shown to be
ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to
early-detect different respiratory diseases in ubiquitous health monitoring. We
posit that such biomarkers are informative in identifying ARDS patients
infected with COVID-19. In this study, we investigate the behavior of COVID-19
on ARDS patients by utilizing simple vital signs. We analyze the long-term
daily logs of blood pressure and heart rate associated with 70 ARDS patients
admitted to five University of California academic health centers (containing
42506 samples for each vital sign) to distinguish subjects with COVID-19
positive and negative test results. In addition to the statistical analysis, we
develop a deep neural network model to extract features from the longitudinal
data. Using only the first eight days of the data, our deep learning model is
able to achieve 78.79% accuracy to classify the vital signs of ARDS patients
infected with COVID-19 versus other ARDS diagnosed patients
Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings
Daily monitoring of stress is a critical component of maintaining optimal
physical and mental health. Physiological signals and contextual information
have recently emerged as promising indicators for detecting instances of
heightened stress. Nonetheless, developing a real-time monitoring system that
utilizes both physiological and contextual data to anticipate stress levels in
everyday settings while also gathering stress labels from participants
represents a significant challenge. We present a monitoring system that
objectively tracks daily stress levels by utilizing both physiological and
contextual data in a daily-life environment. Additionally, we have integrated a
smart labeling approach to optimize the ecological momentary assessment (EMA)
collection, which is required for building machine learning models for stress
detection. We propose a three-tier Internet-of-Things-based system architecture
to address the challenges. We utilized a cross-validation technique to
accurately estimate the performance of our stress models. We achieved the
F1-score of 70\% with a Random Forest classifier using both PPG and contextual
data, which is considered an acceptable score in models built for everyday
settings. Whereas using PPG data alone, the highest F1-score achieved is
approximately 56\%, emphasizing the significance of incorporating both PPG and
contextual data in stress detection tasks
Pain assessment tool with electrodermal activity for postoperative patients: Method validation study
Background: Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects.Objective: The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients.Methods: The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models.Results: Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier.Conclusions: We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities.</p
Recommended from our members
pyEDA: An Open-Source and Versatile Feature Extraction Python Toolkit for Electrodermal Activity
Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), measures changes in perspiration by detecting the changes in electrical conductivity of skin.The changes in perspiration is one of the examples of physiological response to a stimulus such as stress, emotion, pain, etc.
Previous studies have already shown that EDA is one of the leading indicators for a stimulus.
However, the EDA signal itself is not trivial to analyze.
To detect different stimuli in human subjects, variety of features are extracted from EDA signals such as the number of peaks, max peak amplitude, to name a few, showing the prevalence of this signal in bio-medical as well as ubiquitous and wearable computing research.
In this paper, we present an open-source Python toolkit for EDA signal preprocessing and statistical and automatic feature extraction.
To the best of our knowledge, this is the first effort for developing a versatile and generic tool to extract any number of automatic features from EDA signals.
Our online toolkit is evaluated using different machine learning algorithms applied to Wearable Stress and Affect Detection (WESAD) dataset which is publicly available.
Our results show that our proposed pipeline outperforms the state of the art accuracy using either statistical or automatic extracted features on a same dataset. Based on our results, in all of our four machine learning algorithms, we achieve a higher validation accuracy using automatic features compared with statistical features