Multi-Harmonic Visualization for Actigraphy-Measured Circadian Rhythm with Evolutionary Learning in People with Dementia

Irregular sleep activity is a well-known symptom of dementia, while the assessment of circadian rhythm activity changes in clinical settings remains challenging. In this study, we develop a multi-harmonic model for the visualization of circadian rhythm from actigraph measurements. By applying an evolutionary learning fitting procedure, we obtain models that provide more detailed information about circadian rhythm activity. The results show that the proposed model is able to reflect the alterations in circadian rhythm that correspond to the severity of cognitive impairment levels and sleep disturbances. The visualization thus shows nuanced changes in circadian activity over time, surpassing the capabilities of standard models. This approach can provide valuable insights for the care of people with dementia, both during the progression of symptoms and at the end of life.