Modified T Statistics for Improved Detection of Aided Cortical Auditory Evoked Potentials in Hearing-Impaired Infants.

The cortical auditory evoked potential (CAEP) is a change in neural activity in response to sound, and is of interest for audiological assessment of infants, especially those who use hearing aids. Within this population, CAEP waveforms are known to vary substantially across individuals, which makes detecting the CAEP through visual inspection a challenging task. It also means that some of the best automated CAEP detection methods used in adults are probably not suitable for this population.

Longitudinal assessment of listening skills in UK infants with hearing aids using the LittlEARS auditory questionnaire.

Objective: To investigate listening skills in infant hearing aid users using the LittlEARS Auditory Questionnaire (LEAQ).

Design: Caregivers completed the LEAQ, and hearing aid data logging was recorded, at infant age 3-7 months and 7-21 months.

Study Sample: Seventy infant hearing aid users with permanent bilateral hearing loss, no developmental comorbidities, aged 3-7 months at first visit.

Longitudinal Changes in Hearing Aid Use and Hearing Aid Management Challenges in Infants.

Objective: If the benefits of newborn hearing screening and early intervention are to be fully realized, there is a need to understand the challenges of hearing aid management in infants. The aim was to investigate longitudinal changes in hearing aid use and hearing aid management challenges in very young infants.

Design: Eighty-one primarily female (99%) caregivers of infant hearing aid users completed a questionnaire about hearing aid management experiences, first when their infants were 3 to 7 months old (1 to 6 months after hearing aid fitting) and again at 7 to 21 months of age.

Efficient Detection of Cortical Auditory Evoked Potentials in Adults Using Bootstrapped Methods.

Background: Statistical detection methods are useful tools for assisting clinicians with cortical auditory evoked potential (CAEP) detection, and can help improve the overall efficiency and reliability of the test. However, many of these detection methods rely on parametric distributions when evaluating test significance, and thus make various assumptions regarding the electroencephalogram (EEG) data. When these assumptions are violated, reduced test sensitivities and/or increased or decreased false-positive rates can be expected.