Identification of Neuropathic Pain Severity based on Linear and Non-Linear EEG Features

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2021; pp. 169 - 173
• Main Authors: Zolezzi, Daniela M., Maria Alonso-Valerdi, Luz, Naal-Ruiz, Norberto E., Ibarra-Zarate, David I.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Broadband communication; Clinical trials; Cognitive processes; Data models; Electroencephalography; Entropy; Eye; Humans; Neuralgia - diagnosis; Neuralgia - drug therapy; Spinal Cord Injuries; Support vector machines
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630101

The lack of an integral characterization of chronic neuropathic pain (NP) has led to pharmacotherapy mismanagement and has hindered advances in clinical trials. In this study, we attempted to identify chronic NP by fusing psychometric (based on the Brief Inventory of Pain - BIP), and both linear and non-linear electroencephalographic (EEG) features. For this purpose, 35 chronic NP patients were recruited voluntarily. All the volunteers answered the BIP; and additionally, 22 EEG channels positioned in accordance with the 10/20 international system were registered for 10 minutes at resting state: 5 minutes with eyes open and 5 minutes with eyes closed. EEG Signals were sampled at 250 Hz within a bandwidth between 0.1 and 100 Hz. As linear features, absolute band power was obtained per clinical frequency band: delta (0.1~4 Hz), theta (4~8 Hz), alpha (8~12 Hz), beta (12~30 Hz) and gamma (30~100 Hz); considering five regions: prefrontal, frontal, central, parietal and occipital. As non-linear features, approximate entropy was calculated per channel and per clinical frequency band with addition of the broadband (0.1~100 Hz). Resulting feature vectors were grouped in line with the BIP outcome. Three groups were considered: low, moderate, and high pain. Finally, BIP-EEG patterns were classified in those three classes, achieving 96% accuracy. This result improves a previous work of a SVM classifier that used exclusively linear EEG features and showed an accuracy between 87% and 90% per class to predict central NP after spinal cord injury.