Sensorineural hearing loss can be treated using Cochlear implantation. After this surgery using the electrode array impedance measurements, we can check the stability of the impedance value and the dynamic range. Deterioration of speech recognition scores could happen because of increased impedance values. Medicines used to do these measures many times during a year after the surgery. Predicting the electrode impedance could help in taking decisions to help the patient get better hearing. In this research we used a dataset of 80 patients of children who did cochlear implantation using MED-EL FLEX28 electrode array of 12 channels. We predicted the electrode impedance on each channel after 1 month from the surgery date. We used different machine learning algorithms like neural networks and decision trees. Our results indicates that the electrode impedance can be predicted, and the best algorithm is different based on the electrode channel. Also, the accuracy level varies between 66% and 100% based on the electrode channel when accepting an error range between 0 and 3 KO. Further research is required to predict the electrode impedance after three months, six months and one year.
翻译:感官听力损失可以通过Cochlear 植入来治疗。 在使用电极阵列阻力测量器进行手术后, 我们可以检查阻力值和动态范围的稳定性。 语音识别分数可能因为阻力值的增加而出现恶化。 手术后一年内用药物来进行许多次这样的测量。 预测电阻力可以帮助病人做出更好的听力决定。 在这项研究中, 我们使用了80个使用MED-EL FLEX28电极阵列12个频道进行冷凝植入的儿童病人的数据集。 我们预测了手术1个月后每个频道的电阻力。 我们使用了不同的机器学习算法, 如神经网络和决策树。 我们的结果表明, 电阻力是可以预测的, 并且根据电极频道的不同算法, 在接受0至3年的误差时, 精确度介于66%到100 %之间。 还需要进一步的研究来预测3个月、 6个月和1年后的电阻力。