to monitor the inner ear function of cochlear implant (CI) 2 patients during surgery. Current ECochG-based trauma detec-3 tion shows low sensitivity and specificity and depends on visual 4 analysis by experts. Trauma detection could be improved by 5 including electric impedance data recorded simultaneously with 6 the ECochG. However, combined recordings are rarely used 7 because the impedance measurements produce artifacts in the 8 ECochG.

Bioelectrical signals are often pulse-shaped with superimposed interference signals. In this context, accurate identification of features such as pulse onsets, peaks, amplitudes, and duration is a frequent problem. In this paper, we present a versatile method of rather low computational complexity to robustly identify such features in real-world signals. For that, we take use of two straight-line models fit to the observations by minimizing a quadratic cost term, and then identify desired features by tweaked likelihood measures. To demonstrate the idea and facilitate access to the method, we provide examples from the field of cardiology.