Transforming hearing aids through large-scale electrophysiology and deep learning

通过大规模电生理学和深度学习改变助听器

• 批准号: EP/W004275/1
• 负责人: Nicholas Lesica
• 金额: $ 107.1万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW004275%2F1
• 关键词: Transforming; hearing; aids; through; large

Hearing loss affects approximately 500 million people worldwide (11 million in the UK), making it the fourth leading cause of years lived with disability (third in the UK). The resulting burden imposes enormous personal and societal consequences. By impeding communication, hearing loss leads to social isolation and associated decreases in quality of life and wellbeing. It has also been identified as the leading modifiable risk factor for incident dementia and imposes a substantial economic burden, with estimated costs of more than £30 billion per year in the UK.As the impact of hearing loss continues to grow, the need for improved treatments is becoming increasingly urgent. In most cases, the only treatment available is a hearing aid. Unfortunately, many people with hearing aids don't actually use them, partly because current devices, which are little more than simple amplifiers, often provide little benefit in social settings with high sound levels and background noise. Thus, there is a huge unmet clinical need with around three million people in the UK living with an untreated, disabling hearing loss. The common complaint of those with hearing loss, "I can hear you, but I can't understand you", is echoed by hearing aid users and non-users alike. Inasmuch as the purpose of a hearing aid is to facilitate communication and reduce social isolation, devices that do not enable the perception of speech in typical social settings are fundamentally inadequate. The idea that hearing loss can be corrected by amplification alone is overly simplistic; while hearing loss does decrease sensitivity, it also causes a number of other problems that dramatically distort the information that the ear sends to the brain. To improve performance, the next generation of hearing aids must incorporate more complex sound transformations that correct these distortions. This is, unfortunately, much easier said than done. In fact, engineers have been attempting to hand-design hearing aids with this goal in mind for decades with little success. Fortunately, recent advances in experimental and computational technologies have created an opportunity for a fundamentally different approach. The key difficulty in improving hearing aids lies in the fact that there are an infinite number of ways to potentially transform sounds and we do not understand the fundamentals of hearing loss well enough to infer which transformations will be most effective. However, modern machine learning techniques will allow us to bypass this gap in our understanding; given a large enough database of sounds and the neural activity that they elicit with normal hearing and hearing impairment, deep learning can be used to identify the sound transformations that best correct distorted activity and restore perception as close to normal as possible.The required database of neural activity does not yet exist, but we have spent the past few years developing the recording technology required to collect it. This capability is unique; there are no other research groups in the world that can make these recordings. We have already demonstrated the feasibility of solving the machine learning problem in silico. We are now proposing to collect the large-scale database of neural activity required to fully develop a working prototype of a new hearing aid algorithm based on deep neural networks and to demonstrate its efficacy for people with hearing loss.

听力损失影响着全球约5亿人（英国有1100万人），使其成为导致残疾的第四大原因（英国第三大）。由此产生的负担造成了巨大的个人和社会后果。通过阻碍沟通，听力损失导致社会孤立以及生活质量和福祉的相关下降。它也被认为是痴呆症发病的主要可改变风险因素，并造成了巨大的经济负担，在英国每年估计花费超过300亿英镑。随着听力损失的影响持续增长，对改善治疗的需求变得越来越迫切。在大多数情况下，唯一可用的治疗方法是助听器。不幸的是，许多有助听器的人实际上并不使用它们，部分原因是目前的设备，比简单的放大器多一点，在高声级和背景噪音的社会环境中通常没有什么好处。因此，有一个巨大的未满足的临床需求，在英国约有300万人患有未经治疗的致残性听力损失。听力损失患者的常见抱怨，“我能听到你，但我听不懂你”，助听器使用者和非使用者都有同感。由于助听器的目的是促进沟通和减少社会隔离，因此在典型的社会环境中不能感知言语的设备从根本上是不够的。听力损失可以通过放大来纠正的想法过于简单;虽然听力损失确实会降低灵敏度，但它也会导致许多其他问题，这些问题会严重扭曲耳朵发送给大脑的信息。为了提高性能，下一代助听器必须包含更复杂的声音转换，以纠正这些失真。不幸的是，这说起来容易做起来难。事实上，几十年来，工程师们一直试图以这个目标来手工设计助听器，但收效甚微。幸运的是，实验和计算技术的最新进展为一种根本不同的方法创造了机会。改善助听器的关键困难在于，有无数种方法可以潜在地转换声音，而我们对听力损失的基本原理并不了解，无法推断哪些转换最有效。然而，现代机器学习技术将使我们能够绕过我们理解中的这一差距;给定足够大的声音数据库以及它们在正常听力和听力障碍情况下引起的神经活动，深度学习可以用于识别最好地纠正扭曲活动并尽可能接近正常恢复感知的声音变换。所需的神经活动数据库尚不存在，但我们在过去几年里一直在开发收集它所需的录音技术。这种能力是独一无二的;世界上没有其他研究小组可以制作这些录音。我们已经证明了在计算机中解决机器学习问题的可行性。我们现在建议收集大规模的神经活动数据库，以充分开发基于深度神经网络的新型助听器算法的工作原型，并证明其对听力损失患者的有效性。