A Kalman Filtering Framework for Physiological Detection of Anxiety-Related Arousal in Children With Autism Spectrum Disorder


Objective: Anxiety is related to physiological changes that can be noninvasively measured using cheap and wearable sensors. These changes offer an objective and language-free measure of arousal related to anxiety, which can complement treatment programs for clinical populations who have problem with introspection, communication, and emotion recognition. This motivates the development of automatic methods for detection of hysteria-related arousal using physiology signals. While several supervised learning ways are proposed for this purpose, these methods need regular assortment and updating of coaching knowledge and are, thus, not suitable for clinical populations, where obtaining labelled knowledge might be challenging due to impairments in communication and introspection. During this context, the objective of this paper is to develop an unsupervised and real-time arousal detection algorithm. Ways: We tend to propose a learning framework based on the Kalman filtering theory for detection of physiological arousal based mostly on cardiac activity. The performance of the system was evaluated on data obtained from a sample of kids with autism spectrum disorder. Results: The results indicate that the system will detect anxiety-connected arousal in these kids with sensitivity and specificity of 99% and 92%, respectively. Conclusion and significance: Our results show that the proposed technique can detect physiological arousal associated with anxiety with high accuracy, providing support for technical feasibility of augmenting anxiety treatments with automatic detection techniques. This approach can ultimately cause a lot of effective anxiety treatment for a bigger and more diverse population.

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