Bidirectional Hetero-Associative Memory Network With Flexible Sensors and Cloud Computing for Blood Leakage Detection in Intravenous and Dialysis Therapy

Infiltration and blood leakage are serious life-threatening complications during intravenous administration and dialysis therapy. Serious infiltrations, such as drips and drugs, may cause inflection, sepsis, and extent of tissue damage. Blood leakage and blood loss are serious complications, as it takes only a few minutes to lose over 40% of adult blood volume, potentially resulting in mortality. Therefore, we propose integrating the flexible sensors and bidirectional hetero-associative memory (BHAM) network into a cloud computing-based warning tool for the detection of infiltration and blood leakage. The flexible sensors are arranged in two array patterns for liquid leakage detection and are fabricated on a soft substrate via a screen-printing technique. The BHAM network constructs a virtual alarm unit in an embedded system or tablet PC. This early warning unit can be employed to identify risk levels via a wireless network and cloud computing, and then indicates warning information on a remote monitor device or a mobile device. The proposed detection model has been validated experimentally, and the experimental results demonstrate its feasibility.

[1]  Hans-Dietrich Polaschegg,et al.  Venous needle dislodgement: the pitfalls of venous pressure measurement and possible alternatives, a review. , 2010, Journal of renal care.

[2]  Chia-Hung Lin,et al.  Sprott chaos synchronisation classifier for diabetic foot peripheral vascular occlusive disease estimation , 2012 .

[3]  Wansheng Tang,et al.  Learning Associative Memories by Error Backpropagation , 2011, IEEE Transactions on Neural Networks.

[4]  Tracie Wilt Major,et al.  Decreasing IV Infiltrates in the Pediatric Patient--System-Based Improvement Project. , 2016, Pediatric nursing.

[5]  Chien-Ming Li,et al.  Assistive technology using integrated flexible sensor and virtual alarm unit for blood leakage detection during dialysis therapy. , 2016, Healthcare technology letters.

[6]  Sylvain Chartier,et al.  2009 Special Issue: A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties , 2009 .

[7]  B Fabian.  Intravenous complication: infiltration. , 2000, Journal of intravenous nursing : the official publication of the Intravenous Nurses Society.

[8]  Timothy A. Davis,et al.  Direct methods for sparse linear systems , 2006, Fundamentals of algorithms.

[9]  Yao-Feng Chang,et al.  “Cut‐and‐Paste” Manufacture of Multiparametric Epidermal Sensor Systems , 2015, Advanced materials.

[10]  Ming-Jui Wu,et al.  Detection of venous needle dislodgement during haemodialysis using fractional order shape index ratio and fuzzy colour relation analysis. , 2015, Healthcare technology letters.

[11]  Ho-Chiao Chuang,et al.  The Development of a Blood Leakage Monitoring System for the Applications in Hemodialysis Therapy , 2015, IEEE Sensors Journal.

[12]  Sam Emaminejad,et al.  Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis , 2016, Nature.

[13]  Billie Axley,et al.  Venous needle dislodgement in patients on hemodialysis. , 2012, Nephrology nursing journal : journal of the American Nephrology Nurses' Association.

[14]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[15]  Mounir Boukadoum,et al.  A bidirectional heteroassociative memory for binary and grey-level patterns , 2006, IEEE Transactions on Neural Networks.

[16]  G. S. Sathiya devi.  Blood leakage monitoring system in Hemodialysis therapy , 2016 .

[17]  Chien-Ming Li,et al.  Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model With Cloud Computing for Blood Leakage Detection During Hemodialysis , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[18]  Stephen A. Ritz,et al.  Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .

[19]  H C Schouten,et al.  Clinical practices concerning central venous catheters in haematological patients. , 2010, European journal of oncology nursing : the official journal of European Oncology Nursing Society.

[20]  布拉德利·乔恩·摩尔.  Method and device for monitoring loss of body fluid and dislodgment of medical instrument from body , 2004 .

[21]  Whei-Min Lin,et al.  Topology-adaptive noise-immune fault detection system using an associative memory network , 2006 .

[22]  Antonio Brogi,et al.  QoS-Aware Deployment of IoT Applications Through the Fog , 2017, IEEE Internet of Things Journal.

[23]  Cornelio Yáñez-Márquez,et al.  Bidirectional associative memories: Different approaches , 2013, CSUR.

[24]  L. Vallozzi,et al.  Wireless Communication for Firefighters Using Dual-Polarized Textile Antennas Integrated in Their Garment , 2010, IEEE Transactions on Antennas and Propagation.

[25]  Suranga Nanayakkara,et al.  BWard: An optical approach for reliable in-situ early blood leakage detection at catheter extraction points , 2015, 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[26]  Karolj Skala,et al.  Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing , 2015, Open J. Cloud Comput..