RQA-based identifcation of emotions from electrocargiogram signals for emotion regulation in children with autism spectrum disorder

Children with Autism Spectrum Disorder (ASD) have difculties in expressing and regulating their emotions resulting
in meltdowns and outbursts that make it difcult 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
Quantifcation Analysis (RQA) algorithms. The infuence of the various features on emotion recognition is analysed.
Classifcation 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 identifcation of emotional states.