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A Novel Algorithm to Analyze Multi-Frequency Electrocochleography Measurements to Monitor Electrode Placement During Cochlear Implant Surgery

Authors :
Eric E. Babajanian
Kanthaiah Koka
Aniket A. Saoji
Source :
Brain Sciences, Vol 14, Iss 11, p 1096 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Objectives: During cochlear implant (CI) electrode placement, single low-frequency (e.g., 500 Hz) cochlear microphonics (CM) measurements are used to monitor hair-cell function and provide feedback to avert insertion trauma. However, it can be difficult to differentiate between trauma and the electrode’s progression through the cochlea when monitored with a single frequency. Multi-frequency CM measurements, while more complex to analyze, can provide more accurate feedback by measuring CM from various locations along the basilar membrane. Methods: A new algorithm was developed to analyze multi-frequency CM tracings by comparing amplitude and phase changes across different test frequencies. The new algorithm was evaluated as to its ability to identify drop-alarm instances with the multi-frequency approach, as compared to single-frequency 500 Hz tracings. Results: The algorithm presented in this manuscript uses the relationship between CM amplitude and phase changes across frequencies to provide real-time feedback during CI electrode placement. The results show that multi-frequency CM tracings raised an alarm only 0.5 times, as compared to 2.8 instances of alarm raised for the single-frequency 500 Hz CM measurements. Conclusions: Multi-frequency CM tracings can help reduce the number of alarms which may be false positives prompting unnecessary electrode manipulations, thereby minimizing the risk of insertion trauma.

Details

Language :
English
ISSN :
20763425
Volume :
14
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Brain Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.220f139cd4b46b092eda2242f95313f
Document Type :
article
Full Text :
https://doi.org/10.3390/brainsci14111096