An official website of the United States government
Self-similarities at different time scales embedded within a self-organizing neural manifold are well recognized. In this study, we hypothesize that the Hurst fractal dimension (HFD) of the scalp electroencephalographic (EEG) signal reveals statistical differences between chronic pain and opioid use. We test this hypothesis by using EEG resting state signals acquired from a total of 23 human subjects: 14 with chronic pain, 9 with chronic pain taking opioid medications, 5 with chronic pain and not taking opioid medications, and 9 healthy controls. Using the multifractal analysis algorithm, the HFD for full spectrum EEG and EEG frequency band time series was computed for all groups. Our results indicate the HFD varies spatially and temporally across all groups and is of lower magnitude in patients not taking opioids as compared to those taking opioids and healthy controls. A global decrease in HFD was observed with changes in gamma and beta power in the chronic pain group compared to controls and when paired to subject handedness and sex. Our results show the loss of complexity representative of brain wide dysfunction and reduced neural processing can be used as an EEG biomarker for chronic pain and subsequent opioid use.
more »
« less
Comparison of autonomic signals between healthy subjects and chronic low back pain patients at rest and during noxious stimulation
Chronic pain is a major cause of disability worldwide. While acute pain may serve as a protective function, chronic pain and the associated changes in neural processing negatively impact function and quality of life. This neural plasticity may include changes to the autonomic nervous system (ANS) potentially detectable as changes in various physiological signals. Our aim is to evaluate differences in the physiological signals reflecting ANS changes, by comparing healthy subjects and patients with chronic low back pain during standardized pain stimuli. We extracted several features from photoplethysmography (PPG), electrodermal activity (EDA), and respiration, both at rest and during a repeated pinprick test. We found significant group differences in some PPG parameters at rest and in response to the repeated pinprick test. Chronic pain patients had consistently higher basal sympathetic activity, as well as a blunted autonomic response when subjected to nociceptive stimuli.
more »
« less
- Award ID(s):
- 1838796
- PAR ID:
- 10534294
- Publisher / Repository:
- Patron Editore S.r.l.
- Date Published:
- Journal Name:
- National congress of bioengineering Proceedings
- ISSN:
- 2724-2129
- ISBN:
- 9788855580113
- Format(s):
- Medium: X
- Location:
- Italy
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications.more » « less
-
null (Ed.)OBJECTIVES/GOALS: Spinal cord stimulation (SCS) is an intervention for patients with chronic back pain. Technological advances have led to renewed optimism in the field, but mechanisms of action in the brain remain poorly understood. We hypothesize that SCS outcomes will be associated with changes in neural oscillations. METHODS/STUDY POPULATION: The goal of our team project is to test patients who receive SCS at 3 times points: baseline, at day 7 during the trial period, and day 180 after a permanent system has been implanted. At each time point participants will complete 10 minutes of eyes closed, resting electroencephalography (EEG). EEG will be collected with the ActiveTwo system, a 128-electrode cap, and a 256 channel AD box from BioSemi. Traditional machine learning methods such as support vector machine and more complex models including deep learning will be used to generate interpretable features within resting EEG signals. RESULTS/ANTICIPATED RESULTS: Through machine learning, we anticipate that SCS will have a significant effect on resting alpha and beta power in sensorimotor cortex. DISCUSSION/SIGNIFICANCE OF IMPACT: This collaborative project will further the application of machine learning in cognitive neuroscience and allow us to better understand how therapies for chronic pain alter resting brain activity.more » « less
-
Pain, especially chronic pain, is a complicated and subjective experience, threatening global healthcare as one of the most severe health problems. Traditionally, pain is assessed by Visual Analog Scale to indicate the pain intensity by the patient’s self-report, causing them to become biased by various psychosocial factors. In this study, we performed two distinct labeling methods to assess the pressure pain in Quantitative Sensory Testing and to differentiate healthy controls and chronic low back pain patients: time period labels and percentage timestamp labels. Physiological signals such as blood volume pulse and galvanic skin response were collected. The time period labeling method was to segment via fixed time windows. The percentage timestamp labeling method was to select the timestamp labels based on the percentage of the threshold or the tolerance time. Both methods demonstrate different advantages when visualizing the information of different pain states and different participant groups.more » « less
-
Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
