Abstract
In this study, we discuss an efficient approach to characterizing chronotypes using Symbolic Aggregate approXimation (SAX) on actigraphy data. Actigraphy, a non-invasive monitoring of human rest/activity cycles, provides valuable insights into sleep-wake behaviors and circadian rhythms. However, the high dimensionality of actigraphy data poses significant challenges in storage, processing, and analysis. To address these challenges, we applied the SAX algorithm to transform continuous time-series actigraphy data into a symbolic representation, enabling dimensionality reduction while preserving essential patterns. We analyzed actigraphy data from the National Health and Nutrition Examination Survey (NHANES) database, covering over 10,000 individuals, and used unsupervised clustering to identify distinct chronotype patterns. The SAX transformation facilitated the application of machine learning techniques, revealing five chronotype clusters characterized by differences in activity onset, resolution, and intensity. Age distribution analysis showed biases towards specific age groups within the clusters, highlighting the relationship between age and chronotype. Key findings include age-related Chronotype variations with younger individuals exhibiting delayed chronotypes with significant differences in sleep onset (SOT) and wake time (WT) compared to older adults, suggesting a phase delay in sleep patterns as age decreases and activity transition dynamics where clusters showed distinct patterns in winding up and winding down periods, providing insights into the dynamics of activity transitions. This study demonstrates the efficiency and effectiveness of SAX in processing large-scale actigraphy data, enabling robust chronotype characterization that can inform personalized healthcare and public health initiatives. Further exploration of SAX integration with other biometric measures could deepen our understanding of human circadian biology and its impact on health and behavior.
Published Version
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