A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis

Abstract

Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical limitations, such as cumbersome hardware, assuming individual bladder shapes being universal, and being sensitive to sensor placement error. Methods: A low-voltage ultrasound system with machine learning has been developed in estimating bladder filling status. A custom-made flexible 1D transducer array has been excited by low-voltage coded pulses with a pulse compression technique for an enhanced signal-to-noise ratio. In order to minimize the negative influence of possible transducer misplacement, a multiple-position training strategy using machine learning has been adopted in this work. Three popular classification methods, KNN, SVM and sparse coding, have been utilized to classify the acquired different volumes ranging from 100 ml to 300 ml into two categories: low volume and high volume. The low-volume category requires no further action while the high-volume category triggers an alarm to alert the child and caregiver. Results: When the sensor placement is ideal, i.e., the position of the practical sensor placement is on spot with the trained position, the precision and recall of the classification using sparse coding are $0.957~\pm ~0.02$ and $0.958~\pm ~0.02$ , respectively. Even if the transducer array is misplaced by up to 4.5 mm away from the ideal location, the proposed system is able to maintain high classification accuracy (precision $\ge 0.75$ and recall $\ge 0.75$ ). Category: Early/Pre-Clinical Research Clinical and Translational Impact: The proposed ultrasound sensor system for nocturnal enuresis is of significant clinical and translational value as it addresses two major issues that limit the wide adoption of similar devices. Firstly, it offers enhanced safety as the entire system has been implemented in the lowvoltage domain. Secondly, the system features ample tolerance to sensor misplacement while maintaining high classification accuracy. These features combined provide a much more user-friendly environment for children and their caregivers than existing devices.