Your browser doesn't support javascript.
loading
Blood leakage detection during dialysis therapy based on fog computing with array photocell sensors and heteroassociative memory model.
Wu, Jian-Xing; Huang, Ping-Tzan; Lin, Chia-Hung; Li, Chien-Ming.
Afiliação
  • Wu JX; Niche Biomedical LLC, California NanoSystems Institute at UCLA, Los Angeles 90095, CA, USA.
  • Huang PT; Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.
  • Lin CH; Department of Electrical Engineering, Kao-Yuan University, Kaohsiung City, 82151, Taiwan.
  • Li CM; Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung City, 41170, Taiwan.
Healthc Technol Lett ; 5(1): 38-44, 2018 Feb.
Article em En | MEDLINE | ID: mdl-29515815
ABSTRACT
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.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article