{'fraction_non_alphanumeric': 0.017076845806127575, 'fraction_numerical': 0.0045203415369161224, 'mean_word_length': 5.425806451612903, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Human turnover dynamics during sleep: statistical behavior and its modeling.

Turnover is a typical intermittent body movement while asleep. Exploring its behavior may provide insights into the mechanisms and management of sleep. However, little is understood about the dynamic nature of turnover in healthy humans and how it can be modified in disease. Here we present a detailed analysis of turnover signals that are collected by accelerometry from healthy elderly subjects and age-matched patients with neurodegenerative disorders such as Parkinson's disease. In healthy subjects, the time intervals between consecutive turnover events exhibit a well-separated bimodal distribution with one mode at ⩽10 s and the other at ⩾100 s, whereas such bimodality tends to disappear in neurodegenerative patients. The discovery of bimodality and fine temporal structures (⩽10 s) is a contribution that is not revealed by conventional sleep recordings with less time resolution (≈30 s). Moreover, we estimate the scaling exponent of the interval fluctuations, which also shows a clear difference between healthy subjects and patients. We incorporate these experimental results into a computational model of human decision making. A decision is to be made at each simulation step between two choices: to keep on sleeping or to make a turnover, the selection of which is determined dynamically by comparing a pair of random numbers assigned to each choice. This decision is weighted by a single parameter that reflects the depth of sleep. The resulting simulated behavior accurately replicates many aspects of observed turnover patterns, including the appearance or disappearance of bimodality and leads to several predictions, suggesting that the depth parameter may be useful as a quantitative measure for differentiating between normal and pathological sleep. These findings have significant clinical implications and may pave the way for the development of practical sleep assessment technologies.