Title :
Automated classification of electrically-evoked compound action potentials
Author :
Akhoun, Idrick ; Bestel, Julie ; Pracht, Philip ; El-Zir, Elie ; Van-den-Abbeele, Thierry
Author_Institution :
Adv. Bionics Eur. Res. Centre, Hanover, Germany
Abstract :
Electrically-evoked compound action potentials (ECAPs) is an objective measure of peripheral neural encoding of electrical stimulation delivered by cochlear implants (CIs) at the auditory nerve level. ECAPs play a key role in automated CI fitting and outcome diagnosis, as long as presence of genuine ECAP is accurately detected automatically. Combination of ECAP amplitudes and signal-to-noise ratio are shown to efficiently detect true responses, by comparing them to subjective visual expert judgments. Corresponding optimal thresholds were calculated from Receiver-Operating-Characteristic curves. This was conducted separately on three artifact rejection methods: alternate polarity, masker-probe and modified-masker-probe. This model resulted in sensitivity and specificity error of 3.3% in learning, 3.5% in testing and 5.0% in verification. It was found that the following combination of ECAP amplitude and signal-to-noise ratio would be accurate predictors: 22 μV and 1.3 dB SNR thresholds for alternate polarity, 35 μV and -0.2 dB for masker-probe and 44 μV and -0.2 dB for modified-masker-probe.
Keywords :
bioelectric potentials; cochlear implants; medical signal processing; neurophysiology; sensitivity analysis; signal classification; signal denoising; ECAP amplitudes; artifact rejection methods; auditory nerve level; automated CI fitting; automated classification; cochlear implants; electrical stimulation; electrically-evoked compound action potentials; modified-masker-probe; outcome diagnosis; peripheral neural encoding; receiver-operating characteristic curves; signal-to-noise ratio; specificity error; subjective visual expert judgments; Biomedical measurement; Current measurement; Pollution measurement; Sensitivity; Signal to noise ratio; Testing; Visualization; biomedical signal processing; cochlear implants; data mining;
Conference_Titel :
Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on
Conference_Location :
Montpellier
DOI :
10.1109/NER.2015.7146716