MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network

TL;DR

MambaCapsule is a deep learning model that improves the accuracy and interpretability of ECG-based cardiac arrhythmia diagnosis by combining feature extraction and capsule networks, providing confidence scores and signal features.

Contribution

This paper introduces MambaCapsule, a novel neural network architecture that enhances transparency and accuracy in ECG arrhythmia classification.

Findings

Achieved over 99.5% accuracy on MIT-BIH and PTB datasets.

Provides explainability through signal feature reconstruction.

Demonstrates promising performance under standard testing protocols.

Abstract

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.