SHB: Type I (EXP): Algorithms for Unsupervised and Online Learning of Hierarchy of Features for Tuning Cochlear Implants for the Hearing Impaired

SHB：I 型（EXP）：用于调整听力障碍者人工耳蜗的特征层次结构的无监督和在线学习算法

• 批准号: 1231620
• 负责人: Bonny Banerjee
• 金额: $ 29.82万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1231620&HistoricalAwards=false
• 关键词: SHB; Type; EXP; Algorithms; Unsupervised

Since noteworthy events happen only occasionally in any data, it is imperative for smart sensors to learn the norms in data so that authorities can be alerted and appropriate action can be taken at the occurrence of an abnormal or noteworthy event. The aim of this project is to develop algorithms that can learn the norm in terms of a hierarchy of meaningful features from data in an unsupervised and online manner. The application testbed is the problem of automatically tuning cochlear implants (CIs) of patients with severe-to-profound hearing loss by continuously monitoring their speech output. The working hypothesis is that deficiencies in hearing for people with significant hearing loss are reflected in their speech production. This project will develop and use unsupervised, online, and biologically plausible machine learning algorithms to learn feature hierarchies from the speech output data of severely-to-profoundly hearing-impaired patients. The learned feature hierarchy from the speech of a patient will be compared to those learned from the speech of a comparable normal hearing population. Deficiencies in the patient's hearing will be ascertained by identifying the missing or distorted features. Algorithms will be developed to map this information into the signal processing strategies used in CIs to enhance the audibility of speech.The proposed project promises transformative changes to three major interdisciplinary fields: machine learning and artificial intelligence, healthcare, and sensors. It will transform the traditional ways in which the clinical needs of patients are met. For example, the results of this project will provide doctors with evidence-based practices that will better address the specific needs of individual patients by monitoring each patient around the clock at minimal effort and cost.Hearing loss is the most common birth defect in the U.S. with slightly over 15,000 new pediatric cases each year and societal losses amounting to $4.6 billion over a lifetime. A proven technology for CI tuning would make a significant difference to the lives of over 1.2 million CI candidates in the U.S. and many more around the world, thereby leading to substantial health and economic benefits to society. Other than CI tuning, the proposed algorithms will be applicable to a variety of monitoring applications within healthcare, such as blood pressure, cerebrospinal fluid pressure, intracavitary pressure of the bladder, etc., and beyond healthcare, such as web, machine health, traffic, etc. Continuous monitoring with wearable and implantable body sensors will increase early detection of emergency conditions and diseases in at-risk patients and also provide a wide range of healthcare services for people with various degrees of cognitive and physical disabilities. Not only the elderly and chronically ill, but also the families in which both parents have to work will benefit from these systems to provide high-quality care services for their babies and children. Finally, the proposed project will integrate diversity by promoting teaching, learning, and interdisciplinary research among underrepresented groups.

由于值得注意的事件在任何数据中只是偶尔发生，因此智能传感器必须了解数据中的规范，以便在发生异常或值得注意的事件时向当局发出警报并采取适当的行动。该项目的目的是开发算法，能够以无监督和在线的方式从数据中的有意义特征的层次结构中学习规范。该应用测试平台的问题是通过持续监测重度至极重度听力损失患者的语音输出来自动调整人工耳蜗（CI）。有效的假设是，严重听力损失者的听力缺陷反映在他们的言语表达中。该项目将开发和使用无监督、在线且生物学上合理的机器学习算法，从重度至极重度听力障碍患者的语音输出数据中学习特征层次结构。从患者的语音中学习到的特征层次结构将与从可比较的正常听力人群的语音中学到的特征层次结构进行比较。通过识别缺失或扭曲的特征来确定患者的听力缺陷。我们将开发算法，将这些信息映射到 CI 中使用的信号处理策略中，以提高语音的可听度。拟议的项目有望为三个主要跨学科领域带来变革：机器学习和人工智能、医疗保健和传感器。它将改变满足患者临床需求的传统方式。例如，该项目的结果将为医生提供循证实践，通过以最小的努力和成本全天候监测每位患者，更好地满足个别患者的特定需求。 听力损失是美国最常见的出生缺陷。每年新增儿科病例略多于 15,000 例，一生造成的社会损失高达 46 亿美元。一项经过验证的 CI 调整技术将为美国和世界各地超过 120 万 CI 考生的生活带来重大改变，从而为社会带来巨大的健康和经济效益。除了 CI 调整之外，所提出的算法将适用于医疗保健领域的各种监测应用，例如血压、脑脊液压力、膀胱腔内压力等，以及医疗保健之外的各种监测应用，例如网络、机器健康、交通等。使用可穿戴和植入式身体传感器进行持续监测将有助于对高危患者的紧急情况和疾病进行早期发现，并为患有不同程度认知和身体残疾的人提供广泛的医疗服务。不仅老年人和慢性病患者，父母双方都有工作的家庭也将受益于这些系统，为他们的婴儿和儿童提供高质量的护理服务。最后，拟议的项目将通过促进代表性不足群体的教学、学习和跨学科研究来整合多样性。