Toward development of PreVoid alerting system for nocturnal enuresis patients: A fuzzy-based approach for determining the level of liquid encased in urinary bladder
作者：
Highlights：
• The manuscript discusses a machine-learning based technique for determining the level of liquid encased in urinary bladder.
• The proposed method is trained over each individual’s voiding-filling pattern of the bladder, and therefore is independent of gender, BMI and level of obesity of a patient.
• The method assumes the maximum capacity of the bladder is the amount of urine it contains when one feels the urge to void, regardless of the size of bladder.
• Four extracted features obtained via echoed-back pulses of ultrasound are fed to a novel fuzzy error correcting output classifier to see which proportion of bladder is filled.
摘要
•The manuscript discusses a machine-learning based technique for determining the level of liquid encased in urinary bladder.•The proposed method is trained over each individual’s voiding-filling pattern of the bladder, and therefore is independent of gender, BMI and level of obesity of a patient.•The method assumes the maximum capacity of the bladder is the amount of urine it contains when one feels the urge to void, regardless of the size of bladder.•Four extracted features obtained via echoed-back pulses of ultrasound are fed to a novel fuzzy error correcting output classifier to see which proportion of bladder is filled.
论文关键词：Bladder non-destructive testing (NDT),Fuzzy classifier,Error correcting output codes,Nocturnal enuresis,Intelligent systems,Discriminant feature analysis,Medical wearable sensors
论文评审过程：Received 25 February 2019, Revised 20 December 2019, Accepted 17 February 2020, Available online 22 February 2020, Version of Record 3 June 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101819
