Title: Transforming hearing aids through large-scale electrophysiology and deep learning
Funder: Engineering and Physical Sciences Research Council

Abstract: 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.

Lay abstract: Hearing loss is one of the most widespread and disabling chronic conditions in the world, affecting approximately 500 million people. It impedes communication, leads to social isolation, and has been linked to increased risk of cognitive decline and mortality. In fact, a recent UK-led commission identified hearing loss as the most important modifiable risk factor for dementia, accounting for nearly 10% of overall risk. Given the huge individual, societal, and economic burden of hearing loss, there is an urgent need for effective treatments. For the most common forms of hearing loss, there is little chance of a drug- or surgery-based treatment in the near future; thus, the only hope rests with hearing aids. Unfortunately, current devices often fall short in typical social settings with high sound levels and background noise. Hearing aid manufacturers continue to release new models with superficial changes, while users continue to struggle to understand speech in noisy environments and experience distortions in environmental sounds and music. Ongoing efforts to improve background noise suppression will likely help, but the potential benefits of noise suppression are ultimately limited by the difficulty in distinguishing signal from noise in real-world settings with multiple talkers and its inapplicability to environmental sounds or music. We are taking a fundamentally different approach to hearing aid design based on neuroscience and deep learning that will allow us to identify optimal hearing aid algorithms empirically. Using measurements of neural activity with and without hearing loss, we can learn how a hearing aid should transform any sound -- speech, music, etc. -- to provide the best possible correction of the distortions in neural activity caused by hearing loss. We have spent the past several years developing methodologies for the large-scale, high-resolution neural recordings from the central auditory pathway that are required to provide the necessary measurements of activity and refining our deep learning approach in silico. This EPSRC project will allow us to develop a complete prototype and provide preliminary evidence of the efficacy of our technology. Our prototype will be designed for those who are most likely to benefit from improved hearing aids -- people with mild-to-moderate sensorineural hearing loss. We will carry out the proposed work in two stages. In the first stage, we will use our established animal model of hearing loss to develop a prototype hearing aid algorithm to correct distortions in auditory brain activity. The main objectives for this stage are (1) to learn NormNet and LossNet, deep neural networks (DNNs) that produce accurate predictions of auditory brain activity with normal hearing and hearing loss, and (2) to learn and validate AidNet, a DNN that acts as a hearing aid, transforming sound such that it elicits activity with hearing loss that is as close as possible to normal activity. In the second stage, we will optimize AidNet for human listeners and test its objective and subjective benefit. The main objectives for this stage are (1) to demonstrate that AidNet significantly improves the performance of listeners with hearing loss in a speech recognition task relative to that achieved with a state-of-the-art hearing aid and (2) to demonstrate that AidNet scores significantly higher than a state-of-the-art hearing aid in a head-to-head test of subjective listener preference. We believe that our technology has the potential to develop into life-changing products for millions of people. We have support in place from the UCLH Royal National ENT Hospital and its associated Biomedical Research Centre that will allow us to move forward rapidly with testing of our prototype and we are already well positioned for product development and commercialization through our startup company, Perceptual Technologies Ltd.

Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;5.3 MEDICAL DEVICES
Health categories: EAR;NEUROLOGICAL