A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning

Virtual Reality for Pain Management During High-Resolution Manometry: A Randomized Clinical Trial

OBJECTIVE

High-resolution esophageal manometry (HRM) is the gold standard for the diagnosis of esophageal motility disorders. HRM is typically performed in the office with local anesthesia only, and many patients find it unpleasant and painful. The aim of this study was to examine the effects of the use of a virtual reality (VR) headset on pain and anxiety outcomes in patients with dysphagia undergoing HRM.

METHODS

Patients with dysphagia were prospectively recruited and randomized to undergo HRM with and without VR distraction. Data collected included the State-Trait Anxiety Inventory-6 (STAI-6), the Short-Form McGill Pain Questionnaire, heart rate, and galvanic skin response (GSR) tracings.

RESULTS

Forty subjects completed the study, including 20 subjects in the intervention arm and 20 in the control arm. There was evidence of a significant positive effect of VR on calmness (p = 0.0095) STAI-6 rating, as well as on physiologic measures of pain with significantly decreased GSR rise time (p = 0.0137) and average rate of change of conductance change (p = 0.0035).

CONCLUSION

The use of VR during HRM catheter insertion increased calmness compared to control. Change of skin conductance was also reduced in the VR group, suggesting decreased physiologic pain. This study supports the consideration of the use of VR as a distraction tool to improve patient comfort during HRM.

LEVEL OF EVIDENCE

2 Laryngoscope, 134:1118-1126, 2024.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting

Objective. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance and could be used in clinical settings in which patients cannot self-report pain, such as during surgery or when in a coma. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings while salvaging as much useful information as possible.Approach. In this study, we collected EDA data from 70 subjects while they were undergoing surgery in the operating room. We then built a fully automated artifact removal framework to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data. This automated framework consisted of first utilizing three unsupervised machine learning methods for anomaly detection, and then customizing the threshold to separate artifact for each data instance by taking advantage of the statistical properties of the artifact in that data instance. We also created simulated surgical data by introducing artifacts into cleaned surgical data and measured the performance of all three methods in removing it.Main results. Our method achieved the highest overall accuracy and precision and lowest overall error on simulated data. One of the other methods prioritized high sensitivity while sacrificing specificity and precision, while the other had low sensitivity, high error, and left behind several artifacts. These results were qualitatively similar between the simulated data instances and operating room data instances.Significance. Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery, which is the first step to enable clinical integration of EDA as part of standard monitoring.

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity Signals: A Preliminary Study

Automatic motion artifact (MA) removal in electrodermal activity (EDA) signals is a major challenge because of the aperiodic and irregular characteristics of EDA. Given the lack of a suitable MA removal algorithm, a substantial amount of EDA data is typically discarded, especially during ambulatory monitoring. Current methods for MA removal in EDA are feasible when data are corrupted with low magnitude artifacts. In this study, we propose a more data-driven deep convolutional autoencoder (DCAE) for automated motion artifact removal in EDA signals. The DCAE was trained using several publicly available datasets. We used both Gaussian white noise (GWN) and real-life induced MA data records collected in a laboratory setting to corrupt the clean EDA signals. We compared the performance of our DCAE network with three state-of-the-art methods using the performance metrics the signal-to-noise ratio (SNR) improvement (SNR_imp), and the mean squared error (MSE). The proposed DCAE provided significantly higher SNR_impand lower MSE compared to three other methods for both synthetically and real-life induced MA. While the work is preliminary, this work illustrates a promising approach which can potentially be used to remove many different types of MA.