FIGNet: A Robust and Interpretable Fuzzy-Irreversible Gated Network for Auditory Brainstem Response Classification

Auditory brainstem response (ABR) is an important tool for newborn hearing screening and neurological assessment. However, its signals are often difficult to be accurately resolved due to noise interference and weak waveforms, and the need for repeated measurements under multiple sound intensity conditions results in time-consuming data acquisition. Therefore, there is an urgent need to develop an automatic classification model with high accuracy, robustness and good interpretability to achieve stable and effective recognition performance with minimal ABR data. This study presents FIGNet, a new deep learning model that combines type-2 fuzzy logic with a time-irreversible attention mechanism to address uncertainty and temporal direction in ABR signals. Fuzzy attention helps reduce the impact of noise, while the irreversible attention models the one-way nature of neural responses. Experiments on real ABR datasets show that FIGNet outperforms existing models in both binary and five-class classification tasks. It achieves 93.72% accuracy in binary classification and 84.42% accuracy in five-class classification. Visualization results-including confusion matrices, and accuracy curves under different noise levels-further confirm that FIGNet can focus on key waveform areas and stay reliable even in noisy conditions. These findings demonstrate that FIGNet offers fast, interpretable, and robust performance for clinical ABR analysis, achieving high classification accuracy under both clean and noisy conditions.