Dynamic-time-warping analysis of feature-vector reliability for cognitive stimulation therapy assessment

Abstract

Cognitive stimulation therapy (CST) can help people with mental illness improve their health condition. In particular, CST provides an alternative treatment for people with mild to moderate dementia. Signal processing and pattern recognition methods are promising tools for automated assessment of the effectiveness of CST in treating individuals with dementia. This paper applies the dynamic time-warping for investigating the reliability of photoplethysmography-derived features extracted by the largest Lyapunov exponents and spectral distortion for CST evaluation.