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. Author manuscript; available in PMC: 2020 Sep 15.
Published in final edited form as: Hear Res. 2019 Aug 8;381:107782. doi: 10.1016/j.heares.2019.107782

Figure 4. Flowchart of the decision tree for the thresholding algorithm.

Figure 4.

The input is an ABR waveform level series from which the correlation-coefficient level is fit by either sigmoidal or power functions. C1: this conditional compares, for the sigmoidal fit, the slope (d) and range (min and max values a and b) to acceptable values. C2: compares RMS errors of the two fits to choose the better strategy. C3: flags noisy waveforms by assessing the adjusted r2 of the fit. C4: flags cases requiring visual thresholding, i.e. where the maximum fit value is less than criterion (0.35). A-D: refer to example waveform stacks taking each of the four logical paths through the algorithm, as illustrated in Figure 5. The percentages of ABR runs taking each of the four logical paths for each of the two datasets are given at the bottom (green).