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Automated ECAP classification in objective measure software
Akhoun I. , Frohne-Buechner C. , Dykmans P. , Gault A. , Hamacher V.
Advanced Bionics, Hannover, Germany
ECAPs are an objective measure of the peripheral neural response with cochlear implant stimulation, which can
be recorded by back-telemetry. It is commonly used in clinical routine for assessing the auditory nerve
functionality at the end of surgery or during later fitting sessions, in particular using ECAP thresholds. Efficient
use of ECAPs require that most of the complexity of stimulation and recording configuration as well as data
analysis be done automatically, so that presence of an audiologist expert on objective measures to supervise the
measurements is not necessary. Automated ECAP algorithms aim to deliver regular ECAP function outcome 'by
single-click' without the need of parameterization of the stimulation and recording settings and without the need
for evaluation by highly trained experts. Automated ECAP-functions decide on which current-unit to set the
stimulation of the next trace depending on ECAP response classification as containing or not a neural response.
This way the stimulation level is in- or decreased until a satisfactory ECAP-threshold is obtained. ECAP
classification is a critical step in automated ECAP functions: it should be made as accurate as possible. Here, a
comparison was made between automatic and visual rating of a dataset of 18,375 ECAP traces. These traces
were originally obtained with RSPOM 1.3 using Smart-NRI for a loudness growth function with alternating
polarity paradigm (1490 functions, 9969 traces), and recovery functions (496 functions, 4837 traces) as well as
spread-of-excitation functions (256 functions, 3569 traces) with masker and probe paradigm. The two criteria
retained for automatic ECAP classification was the absolute voltage difference of N1-P1 and the signal-to-noise
ratio, estimated by the ratio between root-mean-square over the section where the neural response was
expected and over last 42 samples where neither neural response nor artifact was expected. Altogether, a high
degree of correct ECAP classification was achieved using the combination of +5 dB-SNR and 50 µV: it produced
the best compromise between high true-positives and -negatives and reasonably low level of false-positives and
-negatives. A distinction appeared between Smart-NRI on one side and Recovery / Spread of Excitation on the
other side, thought to be due to better quality traces obtained with forward-masking compared to alternate
polarity (used in Smart-NRI). These observations are implemented in the automated functionalities of the new
objective measures software VOLTA, which provides more flexibility than the SoundWave NRI module. VOLTA
enables measuring a variety of NRI paradigms, including spread-of-excitation and growth functions for threshold
measurements. All measurements are either manually configurable or automated (NRI-Express).Data may also
be retrospectively analyzed or exported to Excel. In demo-mode, CI professionals with less experience can
simulate measurements based on real NRI data.