Synthesis and decomposition of transient-evoked otoacoustic emissions based on an active auditory model

Post-processing analysis of transient-evoked otoacoustic emissions to detect 4 kHz-notch hearing impairment--a pilot study

Background

To identify a parameter to distinguish normal hearing from hearing impairment in the early stages. The parameter was obtained from transient-evoked otoacoustic emissions (TEOAEs), overcoming the limitations of the usually adopted waveform descriptive parameters which may fail in standard clinical screenings.

Material/Methods

Audiometric examinations and TEOAE analysis were conducted on 15 normal ears and on 14 hearing-impaired ears that exhibited an audiometric notch around 4 kHz. TEOAE signals were analyzed through a multivariate technique to filter out the individual variability and to highlight the dynamic structure of the signals. The new parameter (named radius 2-dimension – RAD2D) was defined and evaluated for simulated TEOAE signals modeling a different amount of hearing impairment.

Results

Audiometric examinations indicated 14 ears as impaired-hearing (IH), while the TEOAE ILO92 whole reproducibility parameter (WWR) indicated as IH 7 signals out of 14 (50%). The proposed new parameter indicated as IH 9 signals out of 14 (64%), reducing the number of false negative cases of WWR.

Conclusions

In this preliminary study there is evidence that the new parameter RAD2D defines the topology and the quantification of the damage in the inner ear. The proposed protocol can be useful in hearing screenings to identify hearing impairments much earlier than conventional pure tone audiometry and TEOAE pass/fail test.

A human auditory tuning curves matched wavelet function

This paper proposes a new quantitative approach to the problem of matching a wavelet function to a human auditory tuning curves. The auditory filter shapes were derived from the psychophysical measurements in normal-hearing listeners using the variant of the notched-noise method for brief signals in forward and simultaneous masking. These filters were used as templates for the designing a wavelet function that has the maximum matching to a tuning curve. The scaling function was calculated from the matched wavelet function and by using these functions, low pass and high pass filters were derived for the implementation of a filter bank. Therefore, new wavelet families were derived.

Multiresolution analysis of transient evoked otoacoustic emission

To improve transient evoked otoacoustic emission (TEOAE) test accuracy, a new technique in presented based on multiresolution signal decomposition. Using a wavelet function which optimized according to an active cochlear model, time frequency distribution of TEOAE is obtained. The coefficients of the filter bank are determined from Morlet mother wavelet function in the analysis section. In the synthesis section, time windows are obtained from deduced latency curve. By applying this multiresolution analysis, the correlative noise would be canceled. Therefore, the wave reproducibility can be increased without loss of measuring reliability. In abnormal cases where response is not expected the correlative noise is considered as response are completely disappeared. Thus this will increase the accuracy of screening results.

Autoregressive spectrum of transient evoked otoacoustic emissions: methods and applications

The transient evoked otoacoustic emissions (TEOAEs), elicited by a transient sound, is similar to the impulse (or white noise for a random process) response of a system. It fully reveals the information of cochlea in frequency domain. The aim of present study is to apply the autoregressive (AR) spectrum analysis to TEOAEs signal in our OAEs detection system and to find a new quantitative index of cochlear status. The AR spectrum is also applied to TEOAEs with contralateral acoustic stimulation (CAS) to study the function of medial olivocochlear system. The results show clearly that the AR spectrum of TEOAEs signal can be a promising method for diagnosing the impairment of cochlea and medial olivocochlear system.

Multiscale Detection of Transient Evoked Otoacoustic Emissions

This paper presents a unified approach to multiscale detection of transient evoked otoacoustic emissions (TEOAEs). Using statistical detection theory, it is shown that the optimal detector involves a time windowing operation where the window can be estimated from ensemble correlation information. The detector performs adaptive splitting of the signal into different frequency bands using either wavelet or wavelet packet decomposition. A simplified detector is proposed in which signal energy is omitted. The results show that the simplified detector performs significantly better than existing TEOAE detectors based on wave reproducibility or the modified variance ratio, whereas the detector involving signal energy does not offer such a performance advantage.

Detection of transient-evoked otoacoustic emissions and the design of time windows

A new approach to the design of time windows is presented for detection of transient-evoked otoacoustic emissions (TEOAE). The windows are designed with reference to a minimum mean square error criterion involving the correlation properties of the ensemble of responses. Latency information is introduced in the detection process by windowing at different scales that result from wavelet decomposition. The significance of both subject- and population-specific time windows is investigated. The detection performance is evaluated on a health screen database consisting of 4989 records. The results show that the present approach to windowing yields a significantly better performance in separating normal-hearing subjects from hearing-impaired subjects when compared to detection based on unwindowed signals. With time windowing, the specificity increased with almost 15% at a fixed sensitivity of 90%.

Bionic wavelet transform: a new time-frequency method based on an auditory model

In this paper, a new adaptive wavelet transform, named bionic wavelet transform (BWT), is developed based on a model of the active auditory system. The most distinguishing characteristic of BWT is that its resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. The automatically adjusted resolution, even in a fixed frequency along the time-axis, is achieved by introducing the active control of the auditory system into the wavelet transform (WT). Other properties of BWT include that: 1) BWT is a nonlinear transform that has high sensitivity and frequency selectivity; 2) BWT represents the signal with a concentrated energy distribution; and 3) the inverse BWT can reconstruct the original signal from its time-frequency representation. In order to compare these three properties between BWT and WT, experiments were conducted on both constructed signals and real speech signals. The results show that BWT performs better than WT in these three aspects, and that BWT is appropriate for speech signal processing, especially for cochlear implants.