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

Modified T2 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. This study therefore evaluates and optimizes the performance of new and existing methods for aided (i.e., the stimuli are presented through subjects' hearing aid(s)) CAEP detection in infants with hearing loss. Methods include the conventional Hotellings T2 test, various modified q-sample statistics, and two novel variants of T2 statistics, which were designed to exploit the correlation structure underlying the data. Various additional methods from the literature were also evaluated, including the previously best-performing methods for adult CAEP detection. Data for the assessment consisted of aided CAEPs recorded from 59 infant hearing aid users with mild to profound bilateral hearing loss, and simulated signals. The highest test sensitivities were observed for the modified T2 statistics, followed by the modified q-sample statistics, and lastly by the conventional Hotelling's T2 test, which showed low detection rates for ensemble sizes <80 epochs. The high test sensitivities at small ensemble sizes observed for the modified T2 and q-sample statistics are especially relevant for infant testing, as the time available for data collection tends to be limited in this population.

Objectification of intracochlear electrocochleography using machine learning

Introduction

Electrocochleography (ECochG) measures inner ear potentials in response to acoustic stimulation. In patients with cochlear implant (CI), the technique is increasingly used to monitor residual inner ear function. So far, when analyzing ECochG potentials, the visual assessment has been the gold standard. However, visual assessment requires a high level of experience to interpret the signals. Furthermore, expert-dependent assessment leads to inconsistency and a lack of reproducibility. The aim of this study was to automate and objectify the analysis of cochlear microphonic (CM) signals in ECochG recordings.

Methods

Prospective cohort study including 41 implanted ears with residual hearing. We measured ECochG potentials at four different electrodes and only at stable electrode positions (after full insertion or postoperatively). When stimulating acoustically, depending on the individual residual hearing, we used three different intensity levels of pure tones (i.e., supra-, near-, and sub-threshold stimulation; 250–2,000 Hz). Our aim was to obtain ECochG potentials with differing SNRs. To objectify the detection of CM signals, we compared three different methods: correlation analysis, Hotelling's T² test, and deep learning. We benchmarked these methods against the visual analysis of three ECochG experts.

Results

For the visual analysis of ECochG recordings, the Fleiss' kappa value demonstrated a substantial to almost perfect agreement among the three examiners. We used the labels as ground truth to train our objectification methods. Thereby, the deep learning algorithm performed best (area under curve = 0.97, accuracy = 0.92), closely followed by Hotelling's T² test. The correlation method slightly underperformed due to its susceptibility to noise interference.

Conclusions

Objectification of ECochG signals is possible with the presented methods. Deep learning and Hotelling's T² methods achieved excellent discrimination performance. Objective automatic analysis of CM signals enables standardized, fast, accurate, and examiner-independent evaluation of ECochG measurements.

Contributions of Counseling and Sound Generator Use in Tinnitus Retraining Therapy: Treatment Response Dynamics Assessed in a Secondary Analysis of a Randomized Trial

PURPOSE

Tinnitus retraining therapy (TRT) has been widely used for 30 years, but its efficacy and the component contributions from counseling and sound therapy remain controversial. The purpose of this secondary analysis from the Tinnitus Retraining Therapy Trial (TRTT) was to compare treatment response dynamics for TRT (counseling and conventional sound generators) with partial TRT (pTRT; counseling and placebo sound generators) and standard of care (SOC; a patient-centered counseling control).

METHOD

The TRTT randomized 151 participants with primary tinnitus (no significant hearing or sound tolerance problems) to TRT, pTRT, or SOC, each of which encouraged use of enriched environmental sound. The primary outcome, mean change in Tinnitus Questionnaire score assessed at baseline and follow-up across 18 months, was normalized for a common baseline and fitted with an exponential model. Time constants were estimated to quantify and compare the treatment response dynamics, which were evaluated for statistical significance using bootstrap analyses.

RESULTS

The change in response to TRT took less time to achieve than that for either pTRT or SOC, as demonstrated by time for normalized Tinnitus Questionnaire scores to decline to 63% and 99% of baseline TRT values: 1.2 months (95% CI [0.2, 1.9]) and 5.7 months (95% CI [0.9, 9.0]), respectively. Corresponding SOC values were 2.7 months (95% CI [1.5, 4.1]) and 12.4 months (95% CI [6.9, 19.0]), while those for pTRT were 2.2 months (95% CI [1.2, 3.4]) and 10.1 months (95% CI [5.7, 15.9]). The differences were significant for TRT versus SOC (p = .020), borderline significant for TRT versus pTRT (p = .057), but nonsignificant for pTRT versus SOC (p = .285). The magnitude of the asymptotic treatment response did not differ significantly among groups.

CONCLUSION

Sound generator use in TRT increases treatment efficiency (beyond any advantage from enriched environmental sound) without affecting treatment efficacy (determined by counseling).