PAIN RECOGNITION WITH PHYSIOLOGICAL SIGNALS USING MULTI-LEVEL CONTEXT INFORMATION

Abstract

In the medical field, automatic pain detection is crucial. Previous research has shown that physiological signal characteristics are used preferentially for traditional models by automated pain identification algorithms. These techniques work well, however they mostly depend on medical knowledge to extract physiological signal features. Regardless of medical background, this work proposes a deep learning strategy based on physiological signals that play the roles of both feature extraction and classification. We suggest including multidimensional contextual information for every physiological signal that distinguishes between pain and absence of discomfort. Based on Part A of the BioVid Heat Pain database and the Emopain 2021 dataset, our experimental findings demonstrate that multi-level context information performs more substantially than uni-level context information. Our experimental findings for pain detection tasks include Pain 0 and Pain 1, Pain 0 and Pain 2, Pain 0 and Pain 3, and Pain 0 and Pain 4 for Part A of the BioVid Heat Pain database. In a Leave-One-Subject-Out cross validation analysis, the classification task between Pain 0 and Pain 4 yields average accuracy of 84.8 B1 13.3% for 87 patients and 87.8 B1 11.4% for 67 individuals. The suggested approach makes use of deep learning's superior performance over traditional techniques while handling physiological inputs. The author of the proposal used multilevel or two level feature selection algorithms, such as CNN + BI-LSTM. In the extension work, we added three levels of feature optimization by combining CNN + BI-LSTM + BI GRU. In this way, BI-STM will select the CNN optimized features, and BI-GRU will select the BI-LSTM optimized features. Three level feature optimization and selection contributes to increased accuracy.