RQA-based identification of emotions from electrocargiogram signals for emotion regulation in children with autism...

Children with Autism Spectrum Disorder (ASD) have difficulties in expressing and regulating their emotions resulting in meltdowns and outbursts that make it difficult for parents, medical practitioners and caretakers. This research aims to recognize the unexpressed positive and negative emotional states of children with ASD using electrocardiogram (ECG) signals. Emotional ECG data is obtained from 25 children with ASD using a personalized emotion elicitation protocol, catered to the emotional need of the child. Emotional data was also obtained from 25 typically developed children using a generic protocol. The ECG data was pre-processed and features were extracted using Recurrent Quantification Analysis (RQA) algorithms. The influence of the various features on emotion recognition is analysed. Classification results indicate 97.9% and 87.9% respectively in identifying the positive and negative emotional states in children with ASD. The better correlation of RQA based ECG features with emotions for children with ASD paves way for RQA and similar nonlinear methods to be explored further for better identification of emotional states.