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A Position Paper on Predicting the Onset of Nocturnal Enuresis Using Advanced Machine Learning

  • Paul Fergus
  • Abir Hussain
  • Dhiya Al-Jumeily
  • Naeem Radi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9226)

Abstract

Bed-wetting during normal sleep in children and young people has a significant impact on the child and their parents. The condition is known as nocturnal enuresis and its underlying cause has been subject to different explanatory factors that include, neurological, urological, sleep, genetic and psychosocial influences. Several clinical and technological interventions for managing nocturnal enuresis exist that include the clinician’s opinions, pharmacology interventions, and alarm systems. However, most have failed to produce any convincing results. Clinical information is often subjective and often inaccurate, the use of desmopressin and tricyclic antidepressants only report between 20 % and 40 % success, and alarms only a 50 % success fate. This position paper posits an alternative research idea concerned with the early detection of impending involuntary bladder release. The proposed framework is a measurement and prediction system that processes moisture and bladder volume data from sensors fitted into undergarments that are used by patients suffering with nocturnal enuresis. The proposed framework represents a level of sophistication in nocturnal enuresis treatment not previously considered.

Keywords

Nocturnal enuresis Bedwetting Machine learning Classification Neural networks Sensors 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Paul Fergus
    • 1
  • Abir Hussain
    • 1
  • Dhiya Al-Jumeily
    • 1
  • Naeem Radi
    • 2
  1. 1.Liverpool John Moores UniversityLiverpoolUK
  2. 2.Al-Khawarizmi International CollegeAbu DhabiUAE

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