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Supervised learning classifiers for electrical impedance-based bladder state detection
Dunne, Eoghan; Santorelli, Adam; McGinley, Brian; Leader, Geraldine; O’Halloran, Martin; Porter, Emily
Urinary Incontinence affects over 200 million people worldwide, severely impacting the quality of life of individuals. Bladder state detection technology has the potential to improve the lives of people with urinary incontinence by alerting the user before voiding occurs. To this end, the objective of this study is to investigate the feasibility of using supervised machine learning classifiers to determine the bladder state of ‘full’ or ‘not full’ from electrical impedance measurements. Electrical impedance data was obtained from computational models and a realistic experimental pelvic phantom. Multiple datasets with increasing complexity were formed for varying noise levels in simulation. 10-Fold testing was performed on each dataset to classify ‘full’ and ‘not full’ bladder states, including phantom measurement data. Support vector machines and k-Nearest-Neighbours classifiers were compared in terms of accuracy, sensitivity, and specificity. The minimum and maximum accuracies across all datasets were 73.16% and 100%, respectively. Factors that contributed most to misclassification were the noise level and bladder volumes near the threshold of ‘full’ or ‘not full’. This paper represents the first study to use machine learning for bladder state detection with electrical impedance measurements. The results show promise for impedance-based bladder state detection to support those living with urinary incontinence. This research was supported by funding from the European Research Council under the European Union’s Horizon 2020 Programme/ ERC Grant Agreement BioElecPro n. 637780 and the charity RESPECT and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728.
Keyword(s): Bladder; Electrical and electronic engineering; Urinary Incontinence; Bladder state detection
Publication Date:
2018
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Contributor(s): Horizon 2020; European Research Council; FP7 People: Marie-Curie Actions; RESPECT
Institution: NUI Galway
Publisher(s): Nature Publishing Group
File Format(s): application/pdf
First Indexed: 2018-04-13 06:20:13 Last Updated: 2018-04-13 06:20:13