Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury

Abstract

Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.

An Exploratory EEG Analysis on the Effects of Virtual Reality in People with Neuropathic Pain Following Spinal Cord Injury

“…The FD of EEG signals have repeatedly been shown to be of a lower value for people suffering from brain disorders compared to healthy individuals [ 45 ]. Anderson and colleagues (2021) found able-bodied participants to have higher FD compared to SCI participants with neuropathic pain and used FD as a diagnostic marker for NP [ 45 ]. Foss and colleagues (2006) were able to differentiate between different pain states from FD values.…”

The Role of Machine Learning in Predicting the Onset and Progression of Neuropathic Pain After Spinal Cord Injury: A Literature Review

“…Anderson et al sought to find non-linear, nonoscillatory features in the form of Higuchi's Fractal Dimension (HFD) [10]. Data preprocessing was done according to the Vuckovic et al protocol, except for feature extraction which followed Higuchi's protocol [6,10]. Linear SVM was used to classify based on the HFD features [10].…”

“…Data preprocessing was done according to the Vuckovic et al protocol, except for feature extraction which followed Higuchi's protocol [6,10]. Linear SVM was used to classify based on the HFD features [10]. This yielded that classification markers are transferable from diagnostic classification to prognostic classification; markers for chronic pain can be used to predict future pain [10].…”

“…Linear SVM was used to classify based on the HFD features [10]. This yielded that classification markers are transferable from diagnostic classification to prognostic classification; markers for chronic pain can be used to predict future pain [10]. The classification based on this assumption yielded a mean accuracy ≥ 85% in distinguishing between PDP and PNP [10].…”

“…NP is highly debilitating and severely impacts quality of life [3]. Currently, there are no treatments for NP and pain management options have limited effectiveness [10]. Determining prognostic outcomes in advance can greatly improve pain management options [5].…”