Snippet Policy Network for Multi-class Varied-length ECG Early Classification

TL;DR

This paper introduces a deep reinforcement learning framework called Snippet Policy Network (SPN) for early classification of cardiovascular diseases from ECG data, effectively handling varied-length and long-length time series with improved accuracy.

Contribution

The paper presents the first approach specifically designed for early classification of cardiovascular diseases using varied-length ECG data, combining reinforcement learning with a novel network architecture.

Findings

SPN achieves over 80% accuracy in early ECG classification.

At least 7% improvement over state-of-the-art methods in multiple metrics.

Flexible input handling and dual-optimization for earliness and accuracy.

Abstract

Arrhythmia detection from ECG is an important research subject in the prevention and diagnosis of cardiovascular diseases. The prevailing studies formulate arrhythmia detection from ECG as a time series classification problem. Meanwhile, early detection of arrhythmia presents a real-world demand for early prevention and diagnosis. In this paper, we address a problem of cardiovascular disease early classification, which is a varied-length and long-length time series early classification problem as well. For solving this problem, we propose a deep reinforcement learning-based framework, namely Snippet Policy Network (SPN), consisting of four modules, snippet generator, backbone network, controlling agent, and discriminator. Comparing to the existing approaches, the proposed framework features flexible input length, solves the dual-optimization solution of the earliness and accuracy goals. Experimental results demonstrate that SPN achieves an excellent performance of over 80\% in terms of accuracy. Compared to the state-of-the-art methods, at least 7% improvement on different metrics, including the precision, recall, F1-score, and harmonic mean, is delivered by the proposed SPN. To the best of our knowledge, this is the first work focusing on solving the cardiovascular early classification problem based on varied-length ECG data. Based on these excellent features from SPN, it offers a good exemplification for addressing all kinds of varied-length time series early classification problems.