  Medical time series data, such as Electroencephalography (EEG) and
Electrocardiography (ECG), play a crucial role in healthcare, such as
diagnosing brain and heart diseases. Existing methods for medical time series
classification primarily rely on handcrafted biomarkers extraction and
CNN-based models, with limited exploration of transformers tailored for medical
time series. In this paper, we introduce Medformer, a multi-granularity
patching transformer tailored specifically for medical time series
classification. Our method incorporates three novel mechanisms to leverage the
unique characteristics of medical time series: cross-channel patching to
leverage inter-channel correlations, multi-granularity embedding for capturing
features at different scales, and two-stage (intra- and inter-granularity)
multi-granularity self-attention for learning features and correlations within
and among granularities. We conduct extensive experiments on five public
datasets under both subject-dependent and challenging subject-independent
setups. Results demonstrate Medformer's superiority over 10 baselines,
achieving top averaged ranking across five datasets on all six evaluation
metrics. These findings underscore the significant impact of our method on
healthcare applications, such as diagnosing Myocardial Infarction, Alzheimer's,
and Parkinson's disease. We release the source code at
\url{https://github.com/DL4mHealth/Medformer}.
