Blood leakage detection during dialysis therapy based on fog computing with array photocell sensors and heteroassociative memory model

Healthcare Technology Letters

article

Jian-Xing Wu¹  Ping-Tzan Huang²  Chia-Hung Lin³  Chien-Ming Li⁵ 

[1] California NanoSystems Institute at UCLA;Department of Electrical Engineering, National Tsing Hua University;Department of Electrical Engineering, Kao-Yuan University;Department of Electrical Engineering, National Chin-Yi University of Technology;Division of Infectious Diseases, Department of Medicine, Chi Mei Medical Center

关键词: blood;    patient treatment;    distributed processing;    photoelectric cells;    telemedicine;    wireless sensor networks;    patient monitoring;    bio-optics;    blood leakage detection;    dialysis therapy;    fog computing;    array photocell sensors;    heteroassociative memory model;    blood loss;    healthcare givers;    patients;    adult blood volume;    morbidities;    mortality;    HAM model;    virtual alarm unit;    electricity changes;    end-sensing units;    remote monitor devices;    cloud computing;    wavelength 500 nm to 700 nm;   

DOI  :  10.1049/htl.2017.0091

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Blood leakage and blood loss are serious life-threatening complications occurring during dialysis therapy. These events have been of concerns to both healthcare givers and patients. More than 40% of adult blood volume can be lost in just a few minutes, resulting in morbidities and mortality. The authors intend to propose the design of a warning tool for the detection of blood leakage/blood loss during dialysis therapy based on fog computing with an array of photocell sensors and heteroassociative memory (HAM) model. Photocell sensors are arranged in an array on a flexible substrate to detect blood leakage via the resistance changes with illumination in the visible spectrum of 500–700 nm. The HAM model is implemented to design a virtual alarm unit using electricity changes in an embedded system. The proposed warning tool can indicate the risk level in both end-sensing units and remote monitor devices via a wireless network and fog/cloud computing. The animal experimental results (pig blood) will demonstrate the feasibility.

【 授权许可】

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