{"title":"利用可穿戴超声波和机器学习算法为夜尿症儿童设计的排尿前报警系统","authors":"Jun Wang;Zeyang Dai;Xiao Liu","doi":"10.1109/JTEHM.2024.3457593","DOIUrl":null,"url":null,"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 \n<inline-formula> <tex-math>$0.957~\\pm ~0.02$ </tex-math></inline-formula>\n and \n<inline-formula> <tex-math>$0.958~\\pm ~0.02$ </tex-math></inline-formula>\n, 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 \n<inline-formula> <tex-math>$\\ge 0.75$ </tex-math></inline-formula>\n and recall \n<inline-formula> <tex-math>$\\ge 0.75$ </tex-math></inline-formula>\n). 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.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"643-658"},"PeriodicalIF":3.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10671589","citationCount":"0","resultStr":"{\"title\":\"A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis\",\"authors\":\"Jun Wang;Zeyang Dai;Xiao Liu\",\"doi\":\"10.1109/JTEHM.2024.3457593\",\"DOIUrl\":null,\"url\":null,\"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 \\n<inline-formula> <tex-math>$0.957~\\\\pm ~0.02$ </tex-math></inline-formula>\\n and \\n<inline-formula> <tex-math>$0.958~\\\\pm ~0.02$ </tex-math></inline-formula>\\n, 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 \\n<inline-formula> <tex-math>$\\\\ge 0.75$ </tex-math></inline-formula>\\n and recall \\n<inline-formula> <tex-math>$\\\\ge 0.75$ </tex-math></inline-formula>\\n). 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.\",\"PeriodicalId\":54255,\"journal\":{\"name\":\"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm\",\"volume\":\"12 \",\"pages\":\"643-658\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10671589\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10671589/\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10671589/","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis
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.
期刊介绍:
The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.