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Deep Learning Techniques on Sparsely Sampled Multichannel Data—Identify Deterioration in ICU Patients

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Precision Medicine Powered by pHealth and Connected Health (ICBHI 2017)

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The focus of this paper is to recognize periods of time deviating from the norm using sparsely sampled multichannel signals. The case in question being the ICU, our domain of interest is patient deterioration. In many cases the recording and analyzing of frequently sampled streaming data that can carry more information is not always an option, while at the same time the availability for data recorded at large time intervals is a common occurrence. To address this issue, we examine whether Deep-learning methods can provide efficient results regarding the recognition of different states during the hospitalization, by utilizing hourly multichannel physiological recordings.

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This work leading this research has been partially supported by the E.C. funded program AEGLE under H2020 Grant Agreement No: 644906.

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Correspondence to A. Chytas .

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Chytas, A., Vaporidi, K., Surlatzis, Y., Georgopoulos, D., Maglaveras, N., Chouvarda, I. (2018). Deep Learning Techniques on Sparsely Sampled Multichannel Data—Identify Deterioration in ICU Patients. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore.

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  • Print ISBN: 978-981-10-7418-9

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