  Cardiac arrhythmia, a condition characterized by irregular heartbeats, often
serves as an early indication of various heart ailments. With the advent of
deep learning, numerous innovative models have been introduced for diagnosing
arrhythmias using Electrocardiogram (ECG) signals. However, recent studies
solely focus on the performance of models, neglecting the interpretation of
their results. This leads to a considerable lack of transparency, posing a
significant risk in the actual diagnostic process. To solve this problem, this
paper introduces MambaCapsule, a deep neural networks for ECG arrhythmias
classification, which increases the explainability of the model while enhancing
the accuracy.Our model utilizes Mamba for feature extraction and Capsule
networks for prediction, providing not only a confidence score but also signal
features. Akin to the processing mechanism of human brain, the model learns
signal features and their relationship between them by reconstructing ECG
signals in the predicted selection. The model evaluation was conducted on
MIT-BIH and PTB dataset, following the AAMI standard. MambaCapsule has achieved
a total accuracy of 99.54% and 99.59% on the test sets respectively. These
results demonstrate the promising performance of under the standard test
protocol.
