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ORIGINAL ARTICLE
SPECTRAL KURTOSIS OF OTOACOUSTIC EMISSIONS USING
THE HUSIMI TRANSFORM: A PILOT STUDY
1
Department of Biophysics and Human Physiology, Warsaw Medical University, Warsaw, Poland
2
Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland
Publication date: 2015-12-31
Corresponding author
Tomasz I. Siedlecki
Tomasz Siedlecki, Department of Biophysics and Human Physiology, Warsaw Medical University, Chalubinskiego 5 Str., 02-004 Warsaw, Poland, e-mail: tsiedlecki@wum.edu.pl
Tomasz Siedlecki, Department of Biophysics and Human Physiology, Warsaw Medical University, Chalubinskiego 5 Str., 02-004 Warsaw, Poland, e-mail: tsiedlecki@wum.edu.pl
J Hear Sci 2015;5(4):15-25
KEYWORDS
spontaneous otoacoustic emissionsprobabilitykurtosisevoked otoacoustic emissionsstatistical distributionsmomentsstimulus frequency otoacoustic emissions
ABSTRACT
Background:
Time-frequency distributions can help reveal resonant modes of OAEs. The Husimi transform is the time-frequency distribution of probability. The sound pressure probability density function for a given frequency can be derived from the Husimi transform. Using the Husimi transform as the weight function, it is possible to define the spectral kurtosis of OAEs.
Material and Methods:
The Husimi transform was calculated numerically from OAE data recorded from subjects with normal hearing. We examined click-evoked OAEs (CEOAEs) and tone-burst-evoked OAEs (TBOAEs) with stimuli centered at 1, 2, and 4 kHz, and the presence of spontaneous OAEs (SOAEs) was also investigated. The aim of this study was to examine the statistical properties of otoacoustic emissions (OAEs) and relate them to resonant modes of the cochlea. Assuming that the probability of the sound pressure of an OAE at any time and frequency is given by a Husimi-type transform, we analyzed statistical features of the probability distribution, particularly spectral kurtosis.
Results:
With evoked OAEs, a minimum in kurtosis was found at frequencies close to SOAEs. With TBOAEs, three sorts of SOAEs were found: those with high positive kurtosis, those with small positive kurtosis, and those with negative kurtosis; in these cases, SOAEs appeared at the same frequency as the kurtosis minimum.
Conclusions:
The kurtosis of evoked components of an OAE is strongly affected by the presence of an SOAE. The number of positive peaks and negative troughs of spectral kurtosis in a given frequency band seem to be characteristic of each subject. It is suggested that a new way of distinguishing types of OAEs may involve calculating the spectral kurtosis, and this may be diagnostically useful.
Time-frequency distributions can help reveal resonant modes of OAEs. The Husimi transform is the time-frequency distribution of probability. The sound pressure probability density function for a given frequency can be derived from the Husimi transform. Using the Husimi transform as the weight function, it is possible to define the spectral kurtosis of OAEs.
Material and Methods:
The Husimi transform was calculated numerically from OAE data recorded from subjects with normal hearing. We examined click-evoked OAEs (CEOAEs) and tone-burst-evoked OAEs (TBOAEs) with stimuli centered at 1, 2, and 4 kHz, and the presence of spontaneous OAEs (SOAEs) was also investigated. The aim of this study was to examine the statistical properties of otoacoustic emissions (OAEs) and relate them to resonant modes of the cochlea. Assuming that the probability of the sound pressure of an OAE at any time and frequency is given by a Husimi-type transform, we analyzed statistical features of the probability distribution, particularly spectral kurtosis.
Results:
With evoked OAEs, a minimum in kurtosis was found at frequencies close to SOAEs. With TBOAEs, three sorts of SOAEs were found: those with high positive kurtosis, those with small positive kurtosis, and those with negative kurtosis; in these cases, SOAEs appeared at the same frequency as the kurtosis minimum.
Conclusions:
The kurtosis of evoked components of an OAE is strongly affected by the presence of an SOAE. The number of positive peaks and negative troughs of spectral kurtosis in a given frequency band seem to be characteristic of each subject. It is suggested that a new way of distinguishing types of OAEs may involve calculating the spectral kurtosis, and this may be diagnostically useful.
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