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Title
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Comparison of two different methods to automatically classify auditory nerve responses recorded with NRT system
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Author
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Abstract
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| The Neural Response Telemetry (NRT) system(R) provides a simplified tool for Electrically Evoked Compound Action Potential (ECAP) recordings. This system was successfully validated in humans and permits in-situ measurement of auditory neural activity within the cochlea in response to electrical stimulation and allows many clinical applications. One of the main difficulties in using this system is the time required by the clinicians for both, measure and data analysis. Data analysis requires the selection of valid neural traces. This selection cannot be done only based on the amplitude information because trace morphology cues add to the decision. In this study, we propose two different recognition methods to automatically recognize neural responses traces recorded with NRT system. The first method uses artificial neural network technology (ANN) while the second uses a cross correlation method (CC). Both systems were fed with 120 neural responses divided in five different categories associated to the main response morphologies observed with NRT recordings. For both methods, ROC curves method was used with a set of one thousand NRT traces to compare and evaluate performances. NRT traces were classified by two human experts who worked together to reduce the expert subjectivity. NRT traces were classified in three different categories depending on the clinical application expected with each trace. Results show that CC method led to a higher degree of precision than the ANN method. Particularly a score of 82% was obtained for the correct positives and correct negatives identification with a false positive rate fixed at only 2%. The results presented in this study suggest that CC method is accurate enough to be used for clinical applications. |
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Language
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English
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Source (journal)
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Acta acustica united with acustica
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Publication
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2004
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ISSN
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1610-1928
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Volume/pages
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90
:3
(2004)
, p. 512-519
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ISI
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000222111400010
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