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Time Frequency Spectral Representation of Auditory Brainstem Response (ABR) Data

Gashaye, Amare Terefe (2012) MASM01 20121
Mathematical Statistics
Abstract (Swedish)
The time-frequency (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 SD-BERA, SensoDetect-Brainstem Evoked Response Audiometry. The root mean
square error (RMSE) is the... (More)
The time-frequency (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 SD-BERA, SensoDetect-Brainstem 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:
author
Gashaye, Amare Terefe
supervisor
organization
course
MASM01 20121
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
3008251
date added to LUP
2012-08-20 14:23:51
date last changed
2012-08-20 14:23:51
@misc{3008251,
  abstract     = {The time-frequency (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 SD-BERA, SensoDetect-Brainstem 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},
}