Graph-Based Auditory Brainstem Response Threshold Estimation from Incomplete Intensity Series

Auditory brainstem response (ABR) threshold determination requires clinicians to identify the transition between waveforms with and without a detectable response across stimulus intensities. Automation remains challenging because responses near the threshold are weak, stimulus series are often incomplete, and the interpretation of one waveform depends on the remaining series from the same ear. To address these challenges, we represent each ear as a masked stimulus intensity response graph and propose ABR-GraphDoseNet for automated ABR threshold estimation. The model encodes waveform morphology at each stimulus intensity, exchanges information across an ordered intensity graph, and models candidate threshold transitions with a decoder for adjacent nodes. A global threshold head further integrates evidence across the complete ear series. We evaluated 2064 ears from 1045 subjects using ten-fold cross-validation grouped by subject. ABR-GraphDoseNet achieved the best overall performance, with a mean absolute error of 0.90±0.27 dB, an exact accuracy of 93.80±1.41%, and an accuracy within 10 dB of 98.64±1.01%. Ablation experiments identified the global threshold head and morphology tokenizer as the largest contributors, while adaptive edges and the boundary decoder provided smaller complementary gains. Overall, ABR-GraphDoseNet provides an accurate, structured, and auditable framework for modeling incomplete ABR stimulus series. External validation across devices, acquisition protocols, and institutions remains necessary.