Predicting electrode array impedance after one month from cochlear implantation surgery
This work addresses the problem of monitoring and predicting impedance changes to potentially improve hearing outcomes for cochlear implant patients, though it is incremental as it applies existing machine learning methods to a specific medical dataset.
The study tackled predicting electrode array impedance one month after cochlear implantation surgery using a dataset of 80 pediatric patients, achieving accuracies ranging from 66% to 100% across different electrode channels with an error tolerance of 0-3 kΩ.
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.