Electrophysiology and Perception of Speech in Noise in Older Listeners: Effects of Hearing Impairment and Age

Abstract

Objectives: Speech perception in background noise is difficult for many individuals, and there is considerable performance variability across listeners. The combination of physiological and behavioral measures may help to understand sources of this variability for individuals and groups and prove useful clinically with hard-to-test populations. The purpose of this study was threefold: (1) determine the effect of signal-to-noise ratio (SNR) and signal level on cortical auditory evoked potentials (CAEPs) and sentence-level perception in older normal-hearing (ONH) and older hearing-impaired (OHI) individuals, (2) determine the effects of hearing impairment and age on CAEPs and perception, and (3) explore how well CAEPs correlate with and predict speech perception in noise.

Design: Two groups of older participants (15 ONH and 15 OHI) were tested using speech-in-noise stimuli to measure CAEPs and sentence-level perception of speech. The syllable /ba/, used to evoke CAEPs, and sentences were presented in speech-spectrum background noise at four signal levels (50, 60, 70, and 80 dB SPL) and up to seven SNRs (-10, -5, 0, 5, 15, 25, and 35 dB). These data were compared between groups to reveal the hearing impairment effect and then combined with previously published data for 15 young normal-hearing individuals to determine the aging effect.

Results: Robust effects of SNR were found for perception and CAEPs. Small but significant effects of signal level were found for perception, primarily at poor SNRs and high signal levels, and in some limited instances for CAEPs. Significant effects of age were seen for both CAEPs and perception, while hearing impairment effects were only found with perception measures. CAEPs correlate well with perception and can predict SNR50s to within 2 dB for ONH. However, prediction error is much larger for OHI and varies widely (from 6 to 12 dB) depending on the model that was used for prediction.

Conclusions: When background noise is present, SNR dominates both perception-in-noise testing and cortical electrophysiological testing, with smaller and sometimes significant contributions from signal level. A mismatch between behavioral and electrophysiological results was found (hearing impairment effects were primarily only seen for behavioral data), illustrating the possible contributions of higher order cognitive processes on behavior. It is interesting that the hearing impairment effect size was more than five times larger than the aging effect size for CAEPs and perception. Sentence-level perception can be predicted well in normal-hearing individuals; however, additional research is needed to explore improved prediction methods for older individuals with hearing impairment.

Figure 1

Grand mean electrophysiology waveforms (n=15 for each group) for all 14 conditions tested. Robust morphology effects of SNR are evident while more subtle signal level effects are not as clear. Waveforms for each group are overlaid demonstrating group differences in waveform morphology across SNR and signal level. Generally, waveforms are more robust for YNH (solid black line) than for ONH (red dashed line) and OHI (dotted blue line) groups. Differences between older groups vary depending on the peak, but generally show larger ONH P2 amplitudes and larger OHI N1 amplitudes.

Figure 2

Growth functions of electrophysiology measures (P1, N1, P2, N2, and area) as a function of group at an 80-dB signal level. Peak latencies (left) are generally similar across group, while peak amplitudes (right) for YNH (solid black) are generally larger than ONH (dashed red) and OHI (dotted blue) groups, except for N1 amplitude in which OHI appears to have a larger peak than ONH, similar to YNH.

Figure 3

Sentence-level psychometric functions at an 80-dB signal level. YNH (solid black) show the best performance followed by ONH (dashed red) and then OHI (dotted blue) groups. The magnitude of hearing impairment effects at 50% (ONH vs. OHI) was about five to six times greater than the magnitude of age effects (YNH vs. ONH).

Figure 4

Accuracy of electrophysiology-based predictions of behavior. Predicted SNR50s are plotted against measured SNR50s for YNH (black x), ONH (red +), and OHI (blue o) for all SNR and signal level conditions. Estimates are based on the model created from YNH data. The solid black line represents an accurate prediction. Deviations from this line were used to calculate the root mean squared prediction error. Predictions are relatively good for YNH and ONH groups and quite poor for the OHI group.

Figure 5

Accuracy of electrophysiology-based predictions of behavior for the OHI group using a model specific to OHI. Predicted SNR50s are plotted against measured SNR50s. The solid black line represents an accurate prediction; deviations from this line were used to calculate the root mean squared prediction error. Predictions are better using an OHI-specific model than the YNH model as in Figure 4.

Figure 6

Signal and noise audibility for the OHI group. Mean and min/max audiometric thresholds for the OHI group are shown (solid black and dashed green, respectively) together with 1/3^rd octave band measurements for signal (a) and noise (b) measurements (dotted lines). The shaded area represents portions of the signal and noise that were not audible on average and likely explain the signal level effect that was found for the OHI group.