{'fraction_non_alphanumeric': 0.021632937892533146, 'fraction_numerical': 0.00628053035589672, 'mean_word_length': 5.2894736842105265, '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}}
Age-Related Differences in Complexity During Handgrip Control Using Multiscale Entropy

Changes in motor behavior during aging might be induced by complexity fluctuations in the neuromuscular system. Most previous studies have been performed based on single-scale entropies. In this paper, multiscale fuzzy entropy (MSFuzzyEn) was applied to characterize the changes in the complexity of simulated electromyogram (EMG) signals with the increasing motor unit number and signal-to-noise ratio. Age-related differences in multiscale complexity during handgrip control were also investigated. Ten young and 10 older adults were instructed to produce constant forces at 25%, 50%, and 75% of their maximal grip force with their dominant hands. The grip force and EMG signals of four forearm muscles were recorded simultaneously and analyzed using MSFuzzyEn. The simulation tests revealed that, as the time scale increased, the interference of noise in the EMG signals decreased. At time scale 1, the complexities of the force and EMG signals exhibited opposite changes with aging. When the time scale increased, we observed a loss in complexity with aging in both the force and EMG signals. These results confirmed the merits of MSFuzzyEn in noise abatement, and implied that entropy at relatively larger time scales might better characterize EMG signals. Further studies should extend the application of multiscale entropy in pathologies.