Abstract
Recently, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of biological dynamics. Portable noninvasive medical devices are crucial to capture individual characteristics of biological dynamics. The wearable noninvasive medical devices and the analysis/management of related digital medical data will revolutionize the management and treatment of diseases, subsequently resulting in the establishment of a new healthcare system. One of the key features that can be extracted from the data obtained by wearable noninvasive medical device is the complexity of physiological signals, which can be represented by entropy of biological dynamics contained in the physiological signals measured by these continuous monitoring medical devices. Thus, in this chapter I present the major concepts of entropy that are commonly used to measure the complexity of biological dynamics. The concepts include Shannon entropy, Kolmogorov entropy, Renyi entropy, approximate entropy, sample entropy, and multiscale entropy. I also demonstrate an example of using entropy for the complexity of glucose dynamics.
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Zhang, X. (2017). Entropy for the Complexity of Physiological Signal Dynamics. In: Shen, B. (eds) Healthcare and Big Data Management. Advances in Experimental Medicine and Biology, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-10-6041-0_3
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DOI: https://doi.org/10.1007/978-981-10-6041-0_3
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