MamKanformer: A hybrid Mamba and KAN model based on Medformer for Medical Time-Series Classification

The rapid advancements in sensing technology and artificial intelligence have spurred a substantial number of novel demands and challenges within the realm of medical time series analysis. Notably, the effective extraction and utilization of the long-term and short-term dependency relationships, along with the features at various granularities encapsulated within high-dimensional and multivariate time series, have emerged as one of the pressing issues that demand immediate resolution. To address these, we propose a deep learning model MamKanformer for multichannel EEG signal processing. The model firstly solves the problem of long-short term dependencies which are difficult to be modeled effectively by Feed Forward in Transformer by using Mamba architecture. Subsequently, Kan is utilized to capture the critical feature information of different granularities contained in the time series data to assist in enhancing the output of the model. Finally, extensive experiments conducted on three public datasets have effectively verified the efficacy of the method proposed in this paper. Specifically, the MamKanformer model has demonstrated a superior performance over the other 11 baselines. It has achieved an average top ranking across all six metrics within the three datasets, which unequivocally attests to the outstanding capabilities of our proposed method.