Hybrid RNN-ANN Based Deep Physiological Network for Pain Recognition

Quantitative assessment of pain is vital progress in treatment choosing and distress relief for patients. However, previous approaches based on self-report fail to provide objec- tive and accurate assessments. For impartial pain classification based on physiological signals, a number of methods have been introduced using elaborately designed handcrafted features. In this study, we enriched the methods of physiological-signal- based pain classification by introducing deep Recurrent Neural Network (RNN) based hybrid classifiers which combines auto- extracted features with human-experience enabled handcrafted features. A bidirectional Long Short-Term Memory network (biLSTM) was applied on time series of pre-processed signals to automatically learn temporal dynamic characteristics from them. The handcrafted features were extracted to fuse with RNN-generated features. Finely selected features from biLSTM layer output and handcrafted features trained an Artificial Neural Network (ANN) to classify the pain intensity. The hand- crafted features enhance the RNN classification performance by complementing RNN-generated features. With our accuracy reaching 83.3%, comparison results on an open dataset with other methods show that the proposed algorithm outperforms all of the previous researches with higher classification accuracy. Therefore, this research is a good demonstration of introducing hybrid features for pain assessment.