National Science Foundation Grant Will Benefit Severely Hearing-impaired Individuals

A grant from the National Science Foundation (NSF) to Dr. Bonny Banerjee, assistant professor of electrical and computer engineering and Institute for Intelligent Systems at the University of Memphis, will not only foster interdisciplinary research bringing together fields as diverse as computer engineering and audiology, but will eventually allow severely hearing-impaired people lead the lives of their normal-hearing counterparts.

 
Hearing loss is the most common birth defect in U.S. affecting 12,000 newborns every year. Cochlear implants (CIs) are an effective intervention for adults and children with severe-to-profound sensorineural hearing loss who fail to benefit from acoustic hearing aids. However, without proper tuning of CIs to each individual’s hearing deficiencies, optimal access to sound cannot be delivered, even in the case of good candidate selection, surgery, and rehabilitation support. At present, no universal standards or well-defined good clinical practices for tuning CIs exist to guide the audiologists. With more than 200,000 CI users worldwide and an annual increase of over 30,000, lack of proper tuning is a severe bottleneck to the usage of available life-changing technology.

The $298,203 grant from NSF will fund research that aim to automatically tune CIs for individuals with severe-to-profound hearing loss. Dr. Lisa Lucks Mendel, associate professor at the School of Communication Sciences and Disorders, U of M, worked with Banerjee in obtaining the grant and will handle the clinical aspects of the project.

Before joining U of M, Banerjee spent over three years leading the research in a startup company, Audigence Inc., for developing automated software solutions for tuning digital hearing devices (cochlear implants, hearing aids). This research has a number of U.S. and international patents pending, attracted investor funding over $3 million, and led to the launch of the company’s first product – a commercial software called Clarujust^TM – for the end-user at the Academy of Doctors of Audiology Annual Convention in Tampa, FL on October 29-31, 2009. Clarujust^TM has been covered widely by major news and television channels, such as, Health First, Fox, ABC News, USA Today, etc. across U.S. In March 2012, Audigence's IP was acquired by Cochlear Corp., the world’s leading CI manufacturer. Most importantly, in a pilot study at the University of Florida, 17 of the 20 CI recipients preferred to continue using the Clarujust^TM-tuned setting over traditionally-tuned settings.

Banerjee said, “While Clarujust^TM was quite successful in many ways, one thorn in the bud was its test-retest variability. That is, consecutive tests done on the same patient in exactly the same way sometimes had very different outcomes. This makes the data from tests unreliable and hence, the goodness of the tuned device parameters is in doubt.” He attributes two factors for this behavior – lack of adequate test data and the analysis of an individual’s stimulus-response errors in terms of hand-coded features. “These hand-coded features fail to capture the context or norms in the hearing abilities of each individual.”

These observations and insights led Banerjee to seek out Mendel at the other end of the campus and together submit a proposal to NSF within his six months of joining U of M. Their working hypothesis is that the deficiencies in hearing for individuals with significant hearing loss are reflected in their speech. Banerjee and his team aim to address the shortcomings of Clarujust^TM with an entirely different approach – by learning features from day-to-day speech around the clock in an unsupervised and online manner. Informally, features in this project will be snippets of sound of very short duration (e.g., one millisecond) that recur in speech and using which a given speech can be reconstructed. The learning algorithms will be installed in the implanted CI device. Since the algorithms will learn online, the speech will not be recorded or stored and privacy will not be compromised. The learned feature hierarchy from the speech of a severely hearing-impaired individual 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. This information will guide audiologists to better tune CIs to enhance the audibility of speech.

Other than Banerjee and Mendel, the project will involve electrical and computer engineering students, audiology students as well as clinicians, who will work together to design and deploy the learning algorithms for customized tuning of the CI devices for each individual with severe-to-profound hearing loss. Besides CI tuning, the algorithms will be applicable to a variety of monitoring applications within healthcare and beyond. 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. The project will transform the traditional ways in which the clinical needs of continuously-monitored patients are met. Its success will open up avenues for around the clock medical attention focused on the specific needs of individual patients at minimal cost.

The grant will be distributed over a three-year period. It will support graduate students and cover the cost of equipments and travel needed for the project.