Mapping the Diagnostic Space of Auditory Brainstem Responses: An Interpretable Framework for Classification

Background: Auditory Brainstem Responses (ABRs) are objective and highly standardized evoked potentials, but their clinical interpretation still largely depends on expert visual assessment. Most automated ABR approaches focus on signal detection, threshold estimation, or waveform recognition, whereas the diagnostic reasoning linking ABR features to clinically meaningful categories remains insufficiently formalized. Methods: We developed a theoretical, rule-based framework for ABR classification. Nine clinically relevant descriptors were defined: amplitudes of waves I, III, and V; latencies of waves I, III, and V; the I/V amplitude ratio; and the I-V and III-V interpeak intervals. The complete theoretical combinatorial space was generated and progressively reduced using rules that accounted for non-applicable descriptors, algebraic consistency, and neurobiological plausibility. Two expert raters independently labeled valid configurations and compared with language-model-assisted and logic-based rule systems. Agreement was assessed using percentage concordance and Cohen's k;. Descriptor importance was explored using Random Forest analysis. Results: The initial space of 5,832 possible ABR configurations was reduced to 343 valid configurations. Expert raters showed high agreement, with 84.55% concordance and Cohen's k = 0.620. Agreement between expert classifications and language-model-assisted rules was lower, whereas logic-based rules showed higher agreement with both raters and were retained as the final proposed rule set. The diagnostic space was highly asymmetric, with NORM, PPSHA, and PPC occupying narrow regions, and PB and MIXED representing broader diagnostic domains. Wave I amplitude was the most influential descriptor across classification agents. Conclusions: This work provides an interpretable framework for ABR classification by transforming a broad theoretical combinatorial space into a constrained diagnostic domain. This proof-of-concept study supports the feasibility of modelling ABR interpretation as a structured diagnostic space grounded in auditory neurobiology, providing a reproducible foundation for future clinical validation and semi-automated decision-support tools.