An investigation into the effect of coding scheme choice for sample entropy analysis of communications data is presented. Semantic content and speaker encoding of transmissions are explored. The results suggest a scheme with a greater number of categories will more likely pass surrogate testing, whether through finer distinctions of semantic categorization or through the additions of a speaker-content interaction. The results also suggest the way semantic categories are formed affects how well the scheme permits discrimination between workload conditions with a known effect.
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