Time Frequency Spectral Representation of Auditory Brainstem Response (ABR) Data
(2012) MASM01 20121Mathematical Statistics
 Abstract (Swedish)
 The timefrequency (TF) spectral representation of Auditory Brainstem Response (ABR) signal data
provides information about their spectral contents. We apply the Spectrogram, Thomson Multitaper and
Peak Matched Multiple Window (PM MW) spectral estimation methods to four different number of clicks
per average (i.e., 1313, 300, 100 and 50 number of clicks per average) of a simulated signal data. For the
purpose of model selection we simulate sinusoidal signal data which have the same trend as the empirical
ABR signal data, and then apply the selected model to ABR data from 17 healthy, normal hearing individual
ears as recorded using SDBERA, SensoDetectBrainstem Evoked Response Audiometry. The root mean
square error (RMSE) is the... (More)  The timefrequency (TF) spectral representation of Auditory Brainstem Response (ABR) signal data
provides information about their spectral contents. We apply the Spectrogram, Thomson Multitaper and
Peak Matched Multiple Window (PM MW) spectral estimation methods to four different number of clicks
per average (i.e., 1313, 300, 100 and 50 number of clicks per average) of a simulated signal data. For the
purpose of model selection we simulate sinusoidal signal data which have the same trend as the empirical
ABR signal data, and then apply the selected model to ABR data from 17 healthy, normal hearing individual
ears as recorded using SDBERA, SensoDetectBrainstem Evoked Response Audiometry. The root mean
square error (RMSE) is the main tool used to compare the proposed spectral estimation methods. The
Spectrogram is found to be an appropriate method of spectral estimation for signals with relatively low
disturbance. In particular, for signals with a white disturbance with standard deviation, , value in the
interval 0,15.0, it is found to be best of the three methods. For 15.0 ≤ ≤ 30.0, the PM MW method
performs as good as the spectrogram, if not better. Finally, for ≥ 30.0 the PM MW continues to be the
best of the three methods where as the Spectrogram turns out to be worst of them. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/3008251
 author
 Gashaye, Amare Terefe
 supervisor

 Maria Sandsten ^{LU}
 organization
 course
 MASM01 20121
 year
 2012
 type
 H2  Master's Degree (Two Years)
 subject
 language
 English
 id
 3008251
 date added to LUP
 20120820 14:23:51
 date last changed
 20120820 14:23:51
@misc{3008251, abstract = {The timefrequency (TF) spectral representation of Auditory Brainstem Response (ABR) signal data provides information about their spectral contents. We apply the Spectrogram, Thomson Multitaper and Peak Matched Multiple Window (PM MW) spectral estimation methods to four different number of clicks per average (i.e., 1313, 300, 100 and 50 number of clicks per average) of a simulated signal data. For the purpose of model selection we simulate sinusoidal signal data which have the same trend as the empirical ABR signal data, and then apply the selected model to ABR data from 17 healthy, normal hearing individual ears as recorded using SDBERA, SensoDetectBrainstem Evoked Response Audiometry. The root mean square error (RMSE) is the main tool used to compare the proposed spectral estimation methods. The Spectrogram is found to be an appropriate method of spectral estimation for signals with relatively low disturbance. In particular, for signals with a white disturbance with standard deviation, , value in the interval 0,15.0, it is found to be best of the three methods. For 15.0 ≤ ≤ 30.0, the PM MW method performs as good as the spectrogram, if not better. Finally, for ≥ 30.0 the PM MW continues to be the best of the three methods where as the Spectrogram turns out to be worst of them.}, author = {Gashaye, Amare Terefe}, language = {eng}, note = {Student Paper}, title = {Time Frequency Spectral Representation of Auditory Brainstem Response (ABR) Data}, year = {2012}, }