MambaResp-KAN: A State Space Model with Kolmogorov–Arnold Networks and Diffusion-Based Augmentation for Explainable Respiratory Disease Classification

Automated respiratory disease classification from auscultation sounds holds transformative potential for early clinical screening, yet existing approaches remain constrained by the quadratic complexity of Transformer-based sequence encoders, the limited expressiveness of conventional multi-layer perceptron classifiers, and the persistent challenge of scarce annotated medical audio data. This paper presents MambaResp-KAN, a novel architecture that unifies Bidirectional Mamba state space models, Kolmogorov–Arnold Network classifiers with learnable B-spline activation functions, multi-modal gated cross-attention fusion of WavLM, BEATs, and handcrafted spectral features, and class-conditional denoising diffusion probabilistic model augmentation into a single end-to-end framework for explainable respiratory sound analysis. The Bidirectional Mamba encoder achieves linear-time sequence modeling through input-dependent selective state space discretization, processing forward and reverses temporal streams with gated aggregation to capture both causal and anti-causal dependencies in respiratory waveforms. The Kolmogorov–Arnold Network classifier replaces fixed-activation neurons with learnable univariate B-spline functions on each network edge, directly grounded in the Kolmogorov–Arnold representation theorem, yielding a classifier that is both more parameter-efficient and intrinsically interpretable than standard multi-layer perceptrons. A gated cross-modal attention mechanism fuses embeddings from the self-supervised WavLM and BEATs audio encoders with handcrafted MFCC and spectral features, while a class-conditional denoising diffusion probabilistic model synthesizes high-fidelity respiratory audio to alleviate class imbalance. Integrated Gradients attribution and KAN concept bottleneck analysis provide clinician-interpretable explanations of model decisions. Evaluated on two benchmark datasets, Asthma Detection V2 (five classes, 1,211 samples) and KAUH (four classes, 940 samples), MambaResp-KAN achieves classification accuracies of 99.6% and 99.4%, respectively, surpassing the prior state-of-the-art E-RespiNet by 0.7 and 0.6 percentage points while using 62% fewer parameters and reducing inference latency by 56.3%. Cross-dataset evaluation yields an average accuracy of 84.0% with a generalization gap of 15.8%, compared to 23.3% for E-RespiNet, confirming improved transferability across clinical institutions.