Intelligent Real Time Data Mining of Depth of Anaesthesia

Qin Wei, Dai-Wei Huang, Cheng-Wei Lu, Quan Liu, Shou-Zen Fan, and Jiann-Shing Shieh


Electroencephalograph, sample entropy, multi-scale entropy, depth of anaesthesia


Diagnosis of anaesthesia plays an important role for treatment and drug usage in operating theatre and intensive care unit. Various methods such as spectral edge frequency, δ power and bispectral spectrum analysis have been used widely and integrated into devices. Entropy as a classical method has been applied as an index in measuring complexity of physiological signals in the two decades as well. In this paper, sample entropy and multi-scale entropy are employed to detect state of conscious or depth of anaesthesia at real time, after offline analysis of DOA has been done through approximate entropy and sample entropy. And a system including online and offline analysis on electroencephalograph signals is developed to help anaesthesiologists in measuring status of patients in the operating theaters.

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