Neural networks: a systematic introduction
Neural networks: a systematic introduction
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
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The auditory nerve's electrically evoked compound action potential is recorded in deaf patients equipped with the Nucleus^(R) 24 cochlear implant using a reverse telemetry system (NRT(TM)). Since the threshold of the NRT response (NRT-T) is thought to reflect the psychophysics needed for programming cochlear implants, efforts have been made by specialized management teams to develop its use. This study aimed at developing a valid tool, based on artificial neural networks (ANN) technology, for automatic estimation of NRT-T. The ANN used was a single layer perceptron, trained with 120 NRT traces. Learning traces differed from data used for the validation. A total of 550 NRT traces from 11 cochlear implant subjects were analyzed separately by the system and by a group of physicians with expertise in NRT analysis. Both worked to determine 37 NRT-T values, using the response amplitude growth function (AGF) (linear regression of response amplitudes obtained at decreasing stimulus intensity levels). The validity of the system was assessed by comparing the NRT-T values automatically determined by the system with those determined by the physicians. A strong correlation was found between automatic and physician-obtained NRT-T values (Pearson r correlation coefficient 0.9). ANOVA statistics confirmed that automatic NRT-Ts did not differ from physician-obtained values (F=0.08999, P=0.03). Moreover, the average error between NRT-Ts predicted by the system and NRT-Ts measured by the physicians (3.6 stimulation units) did not differ significantly from the average error between NRT-Ts measured by each of the three physicians (4.2 stimulation units). In conclusion, the automatic system developed in this study was found to be as efficient as human experts for fitting the amplitude growth function and estimating NRT-T, with the advantage of considerable time-saving.