Respiratory sounds captured via auscultation contain critical clues for diagnosing pulmonary conditions. Automated classification of these sounds faces challenges due to subtle acoustic differences and severe class imbalance in clinical datasets. This study investigates respiratory sound classification with a focus on mitigating pronounced class imbalance. We propose a hybrid deep learning model that combines a Long Short-Term Memory (LSTM) network for sequential feature encoding with a Kolmogorov-Arnold Network (KAN) for classification. The model is integrated with a comprehensive feature extraction pipeline and targeted imbalance mitigation strategies. Experiments were conducted on a public respiratory sound database comprising six classes with a highly skewed distribution. Techniques such as focal loss, class-specific data augmentation, and Synthetic Minority Over-sampling Technique (SMOTE) were employed to enhance minority class recognition. The proposed Hybrid LSTM-KAN model achieves an overall accuracy of 94.6 percent and a macro-averaged F1 score of 0.703, despite the dominant COPD class accounting for over 86 percent of the data. Improved detection performance is observed for minority classes compared to baseline approaches, demonstrating the effectiveness of the proposed architecture for imbalanced respiratory sound classification.

翻译：通过听诊采集的呼吸音包含诊断肺部疾病的关键线索。由于临床数据集中存在细微的声学差异和严重的类别不平衡，这些声音的自动分类面临挑战。本研究以缓解显著类别不平衡为重点，探索呼吸音分类方法。我们提出了一种混合深度学习模型，该模型结合了用于序列特征编码的长短期记忆（LSTM）网络和用于分类的Kolmogorov-Arnold网络（KAN）。该模型集成了全面的特征提取流程和针对性的不平衡缓解策略。实验在一个公开的呼吸音数据库上进行，该数据库包含六个类别且分布高度偏斜。研究采用了焦点损失、类别特异性数据增强和合成少数类过采样技术（SMOTE）等方法以提升少数类识别能力。尽管占主导地位的COPD类别数据占比超过86%，所提出的混合LSTM-KAN模型仍实现了94.6%的整体准确率和0.703的宏平均F1分数。与基线方法相比，模型在少数类上的检测性能得到改善，证明了所提架构在不平衡呼吸音分类中的有效性。