Abstract
Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83 ± 0.09 and 0.81 ± 0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.
Highlights
Pain, a vital phenomenon that depends on the dynamic integration of sensory and contextual processes, is one of the top causes of disability, and if untreated, it can lead to undesirable personal and sociological outcomes, such as depression, work absenteeism, and presenteeism, and unnecessary costs to families and caregivers [1], [2]
Our motivation is to investigate the performance of convolutional neural networks (CNN) for the benefit of EEG-based pain detection, which has high potential in clinical research and solutions
The following subsections will demonstrate the classification results of pain detection based on CNN and compare our approach with respect to the state-of-the-art, in both movement stimulation (MS) and video stimulation (VS) phases
Summary
A vital phenomenon that depends on the dynamic integration of sensory and contextual processes, is one of the top causes of disability, and if untreated, it can lead to undesirable personal and sociological outcomes, such as depression, work absenteeism, and presenteeism, and unnecessary costs to families and caregivers [1], [2]. Recent advances in brain imaging technologies have led to neurocomputational models that use neurological signals to classify pain states. These models make use of the alterations in structure and functionalities of the chronic pain patient’s brain. Compared to fMRI, which is widely used for functional recording, the electroencephalography (EEG) to predict brain function abnormalities and extract a brain-based marker of chronic pain is appealing as the system is non-invasive, cost-effective, broadly available, and potentially mobile [8]. The goal of the current study is to detect pain states in chronic pain patients based on neural signatures extracted from EEG signals. Results in this work might be helpful for the diagnosis and classification of chronic pain in a less expensive and more comfortable manner, but might represent a target for novel therapeutic strategies such as neurofeedback [9], or noninvasive brain stimulation techniques [10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
