Multistage Nonlinear Optimization to Recover Neural Activation Patterns From Evoked Compound Action Potentials of Cochlear Implant Users.
OBJECTIVE
Electrically evoked compound action potentials (ECAPs) have been employed as a measure of neural activation evoked by cochlear implant (CI) stimulation. A forward-masking procedure is commonly used to reduce stimulus artefacts. This method estimates the joint neural activation produced by two electrodes-one acting as probe and the other as masker; as such, the measured ECAPs depend on the activation patterns produced by both. We describe an approach--termed panoramic ECAP ("PECAP")--that allows reconstruction of the underlying neural activation pattern of individual channels from ECAP amplitudes.


METHODS
The proposed approach combines two constrained nonlinear optimization stages. PECAP was validated against simulated and physiological data from CI users. The physiological data consisted of ECAPs measured from four users of Cochlear devices. For each subject, an 18 ×18 ECAP amplitude matrix was measured using a forward-masking method.


RESULTS
The results from computer simulations indicate that our approach can reliably estimate the underlying activation patterns from ECAP amplitudes even for instances of neural "dead regions" or cross-turn stimulation. The operating signal-to-noise ratio (SNR) for the proposed algorithm was 5 dB or higher, which matched well the SNR measured from human physiological data. Human ECAPs were fitted with our procedure to determine neural activation patterns.


CONCLUSIONS
PECAP can be used to identify undesirable features of the neural activation pattern of individual CI users.


CONCLUSIONS
Our approach may have clinical application as an objective measure of electrode-to-neuron interface and may be used to devise ad hoc stimulation strategies.
Stefano
S
Cosentino
Etienne
E
Gaudrain
John M
JM
Deeks
Robert P
RP
Carlyon
D000465:Algorithms; D003051:Cochlea / Q000502:physiology*; D003054:Cochlear Implants; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D006801:Humans; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted