Blood glucose prediction for diabetes therapy using a recurrent artificial neural network

William Sandham; Dimitra Nikoletou; David Hamilton; Ken Paterson; Alan Japp; Catriona McGregor

Blood glucose prediction for diabetes therapy using a recurrent artificial neural network

William Sandham
, Dimitra Nikoletou
, David Hamilton
, Ken Paterson
, Alan Japp
, Catriona McGregor

Research output: Contribution to conference › Paper › peer-review

Abstract

Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin dependent diabetics). This predicted BGL may then be used in a computerised management system to achieve short-term normoglycaemia. This paper investigates the use of a recurrent artificial neural network for predicting BGL, and presents preliminary results for two insulin dependent diabetic females.

Original language	English
Publication status	Published - 1998
Event	EUSIPCO-98: Ninth European Signal Processing Conference - Rhodes, Greece Duration: 8 Sept 1998 → 11 Sept 1998

Conference

Conference	EUSIPCO-98: Ninth European Signal Processing Conference
Period	8/09/98 → 11/09/98

Bibliographical note

Note: This paper was published in Theodoridis, Sergios,
(ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054

Organising Body: European Association for Signal Processing

Keywords

Computer science and informatics

Blood glucose prediction for diabetes therapy using a recurrent artificial neural network
Sandham, W., Nikoletou, D., Hamilton, D., Paterson, K., Japp, A. & McGregor, C., 1998, This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing. Theodoridis, S. (ed.). Patras, Greece
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Cite this

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title = "Blood glucose prediction for diabetes therapy using a recurrent artificial neural network",

abstract = "Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin dependent diabetics). This predicted BGL may then be used in a computerised management system to achieve short-term normoglycaemia. This paper investigates the use of a recurrent artificial neural network for predicting BGL, and presents preliminary results for two insulin dependent diabetic females.",

keywords = "Computer science and informatics",

author = "William Sandham and Dimitra Nikoletou and David Hamilton and Ken Paterson and Alan Japp and Catriona McGregor",

note = "Note: This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing; EUSIPCO-98: Ninth European Signal Processing Conference ; Conference date: 08-09-1998 Through 11-09-1998",

year = "1998",

language = "English",

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TY - CONF

T1 - Blood glucose prediction for diabetes therapy using a recurrent artificial neural network

AU - Sandham, William

AU - Nikoletou, Dimitra

AU - Hamilton, David

AU - Paterson, Ken

AU - Japp, Alan

AU - McGregor, Catriona

N1 - Note: This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing

PY - 1998

Y1 - 1998

N2 - Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin dependent diabetics). This predicted BGL may then be used in a computerised management system to achieve short-term normoglycaemia. This paper investigates the use of a recurrent artificial neural network for predicting BGL, and presents preliminary results for two insulin dependent diabetic females.

AB - Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin dependent diabetics). This predicted BGL may then be used in a computerised management system to achieve short-term normoglycaemia. This paper investigates the use of a recurrent artificial neural network for predicting BGL, and presents preliminary results for two insulin dependent diabetic females.

KW - Computer science and informatics

M3 - Paper

T2 - EUSIPCO-98: Ninth European Signal Processing Conference

Y2 - 8 September 1998 through 11 September 1998

ER -

Blood glucose prediction for diabetes therapy using a recurrent artificial neural network

Abstract

Conference

Bibliographical note

Keywords

Fingerprint

Research output

Blood glucose prediction for diabetes therapy using a recurrent artificial neural network

Cite this