Two-stream Network for ECG Signal Classification

TL;DR

This paper introduces a two-stream neural network architecture for ECG signal classification that captures both individual heartbeat features and temporal correlations, achieving high accuracy on benchmark and real-world data.

Contribution

The paper presents a novel two-stream network that combines holistic ECG and heartbeat-specific analysis, improving classification accuracy over existing methods.

Findings

Achieved 99.38% accuracy on MIT-BIH dataset

Reached 88.07% positive accuracy on real-life data

Effectively captures temporal and individual heartbeat features

Abstract

Electrocardiogram (ECG), a technique for medical monitoring of cardiac activity, is an important method for identifying cardiovascular disease. However, analyzing the increasing quantity of ECG data consumes a lot of medical resources. This paper explores an effective algorithm for automatic classifications of multi-classes of heartbeat types based on ECG. Most neural network based methods target the individual heartbeats, ignoring the secrets embedded in the temporal sequence. And the ECG signal has temporal variation and unique individual characteristics, which means that the same type of ECG signal varies among patients under different physical conditions. A two-stream architecture is used in this paper and presents an enhanced version of ECG recognition based on this. The architecture achieves classification of holistic ECG signal and individual heartbeat and incorporates identified and temporal stream networks. Identified networks are used to extract features of individual heartbeats, while temporal networks aim to extract temporal correlations between heartbeats. Results on the MIT-BIH Arrhythmia Database demonstrate that the proposed algorithm performs an accuracy of 99.38\%. In addition, the proposed algorithm reaches an 88.07\% positive accuracy on massive data in real life, showing that the proposed algorithm can efficiently categorize different classes of heartbeat with high diagnostic performance.