Abstract
A modular artificial neural-network system composed of two levels of modules has been developed for the classification of auditory brainstem response (ABR) to be used in electrophysiologic hearing-threshold determination. The first level consists of four neural-network modules, each of which is trained to recognize one of four basic ABR waveforms (high, mid, low and, no response). The final classifications (response, no response, and cannot determine) are decided by a second-level neural network or a rule-base system. The performance of the system was evaluated by computing Bayesian boundaries obtained by the nearest neighbor method. The results show that the modular system is trained faster than previous nonmodular networks and yields higher recognition rates. Eliminating stimulus and muscle artifacts by limiting input window further improves the recognition rate.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Annual Conference on Engineering in Medicine and Biology |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | Publ by IEEE |
| Pages | 1879-1880 |
| Number of pages | 2 |
| Volume | 13 |
| Edition | pt 4 |
| ISBN (Print) | 0780302168 |
| State | Published - Dec 1 1991 |
| Externally published | Yes |
| Event | Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Orlando, FL, USA Duration: Oct 31 1991 → Nov 3 1991 |
Other
| Other | Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
|---|---|
| City | Orlando, FL, USA |
| Period | 10/31/91 → 11/3/91 |
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ASJC Scopus subject areas
- Bioengineering
Cite this
Auditory brainstem response classification using modular neural networks. / Wen, Han; Ozdamar, Ozcan.
Proceedings of the Annual Conference on Engineering in Medicine and Biology. Vol. 13 pt 4. ed. Piscataway, NJ, United States : Publ by IEEE, 1991. p. 1879-1880.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Auditory brainstem response classification using modular neural networks
AU - Wen, Han
AU - Ozdamar, Ozcan
PY - 1991/12/1
Y1 - 1991/12/1
N2 - A modular artificial neural-network system composed of two levels of modules has been developed for the classification of auditory brainstem response (ABR) to be used in electrophysiologic hearing-threshold determination. The first level consists of four neural-network modules, each of which is trained to recognize one of four basic ABR waveforms (high, mid, low and, no response). The final classifications (response, no response, and cannot determine) are decided by a second-level neural network or a rule-base system. The performance of the system was evaluated by computing Bayesian boundaries obtained by the nearest neighbor method. The results show that the modular system is trained faster than previous nonmodular networks and yields higher recognition rates. Eliminating stimulus and muscle artifacts by limiting input window further improves the recognition rate.
AB - A modular artificial neural-network system composed of two levels of modules has been developed for the classification of auditory brainstem response (ABR) to be used in electrophysiologic hearing-threshold determination. The first level consists of four neural-network modules, each of which is trained to recognize one of four basic ABR waveforms (high, mid, low and, no response). The final classifications (response, no response, and cannot determine) are decided by a second-level neural network or a rule-base system. The performance of the system was evaluated by computing Bayesian boundaries obtained by the nearest neighbor method. The results show that the modular system is trained faster than previous nonmodular networks and yields higher recognition rates. Eliminating stimulus and muscle artifacts by limiting input window further improves the recognition rate.
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UR - http://www.scopus.com/inward/citedby.url?scp=0026310314&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0026310314
SN - 0780302168
VL - 13
SP - 1879
EP - 1880
BT - Proceedings of the Annual Conference on Engineering in Medicine and Biology
PB - Publ by IEEE
CY - Piscataway, NJ, United States
ER -