Blood Glucose Prediction Algorithms for Hypoglycemic and/or Hyperglycemic Alerts
Continuous glucose monitoring (CGM) sensors able to monitor blood glucose concentration continuously (i.e. with a reading every 1-5 min) for several days (up to 7 consecutive days), entered clinical research. The availability of continuous glucose monitoring (CGM) sensors allows development of new strategies for the treatment of diabetes. CGM sensors are of two types, noninvasive (NI-CGM) or minimally-invasive (MI-CGM). Irrespective of the type, CGM sensors can become smart by providing them with algorithms able to generate alerts, say, 20–30 min ahead of time, when glucose concentration is predicted to exceed the normal range thresholds (70-180 mg/dL). Such alerts would allow diabetes patients to take precautionary measures to prevent hypo/hyperglycemia. In this paper we review blood glucose prediction algorithms such as first-order autoregressive( AR(1) ), Kalman Filtering and Feed Forward Neural Network. All these algorithms have demonstrated that blood glucose can be predicted ahead in time.
Keywords: Continuous Glucose Monitoring, Auto regressive, Kalman filtering, Feed Forward Neural Network.
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ABOUT THE AUTHORS
C. Gireesh
C. Gireesh is working as Assistant Professor at Vasavi College of Engineering, Hyderabad, India. He received M.Tech. Degree in Computer Science and Engineering from Sri Venkateswara Univesity Tirupathi, Andhra Pradesh. His research interests are Machine Learning, Soft Computing, Image Processing, Bio-Medicine,Bio-Informatics.
V. Punna Rao
V. Punna Rao is working as Assistant professor at Vasavi College of Engineering, Hyderabad, India. He received M.Tech. Degree in Media and Sound Engineering from IIT, Kharagpur. He did his Bachelor’s degree in Computer Science and Engineering, AMIETE from Institution of electronics and telecommunication engineers (IETE),New Delhi. His research interests are Image Processing, Speech and Video Processing, Core Networks.
C. Gireesh
C. Gireesh is working as Assistant Professor at Vasavi College of Engineering, Hyderabad, India. He received M.Tech. Degree in Computer Science and Engineering from Sri Venkateswara Univesity Tirupathi, Andhra Pradesh. His research interests are Machine Learning, Soft Computing, Image Processing, Bio-Medicine,Bio-Informatics.
V. Punna Rao
V. Punna Rao is working as Assistant professor at Vasavi College of Engineering, Hyderabad, India. He received M.Tech. Degree in Media and Sound Engineering from IIT, Kharagpur. He did his Bachelor’s degree in Computer Science and Engineering, AMIETE from Institution of electronics and telecommunication engineers (IETE),New Delhi. His research interests are Image Processing, Speech and Video Processing, Core Networks.
