Key result
An EEG-based classification strategy using support vector machines achieved accuracies of 83 ± 5% and 62 ± 6% for distinguishing three and five levels of pain, respectively.
Why the study?
Can a novel EEG processing and classification strategy accurately distinguish three or five levels of pain in healthy subjects?
Can a novel EEG processing and classification strategy accurately distinguish three or five levels of pain in healthy subjects?
A novel EEG processing and classification strategy using SVM can distinguish three levels of pain with 83% accuracy and five levels with 62% accuracy.
EEG-based pain classification feasible in healthy subjects; hypothesis-generating, requires clinical validation before adoption.
Research studies have tried to extract pain-related features from electroencephalogram(EEG) signals for quantitative measuring of pain. In this study, we go one step further to measure three/five levels of pain by proposing efficient EEG processing steps in conjunction with a new classification strategy. 24 healthy subjects voluntarily performed the cold pressor test while their EEGs were recorded. First, the EEGs were decomposed by independent component analysis and the artifact sources were removed. Among the remained sources, pain-related sources, were chosen according to an adopted information criterion. Next, the EEGs were reconstructed by projecting back the selected sources. Then, grand average brain maps of train subjects were estimated for each pain level over the Alpha(8-12 Hz) and Delta(0.5-4 Hz) bands. By tracing the brain maps' changes over different pain levels, the structure of the proposed decision tree was formed. To enrich the feature set, we also extracted other EEG features. For each decision node, a specific subset of features was selected by sequential forward selection method. Considering k-nearest neighbor(KNN) as the decision marker,the classification accuracies for the three and five pain levels was determined 80 ± 5 and 60 ± 5 percent, respectively while by choosing support vector machine(SVM), the results improved up to 83 ± 5 and 62 ± 6 percent,respectively.
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Nezam et al. (2018) studied Healthy subjects (experimentally induced pain) (n=24). EEG signal feature classification strategy was evaluated on Classification accuracy for three and five pain levels. An EEG-based classification strategy using support vector machines achieved accuracies of 83 ± 5% and 62 ± 6% for distinguishing three and five levels of pain, respectively.
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