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Arrhythmia detection from ECG is an important research subject in theprevention and diagnosis of cardiovascular diseases. The prevailing studiesformulate arrhythmia detection from ECG as a time series classificationproblem. Meanwhile, early detection of arrhythmia presents a real-world demandfor early prevention and diagnosis. In this paper, we address a problem ofcardiovascular disease early classification, which is a varied-length andlong-length time series early classification problem as well. For solving thisproblem, we propose a deep reinforcement learning-based framework, namelySnippet Policy Network (SPN), consisting of four modules, snippet generator,backbone network, controlling agent, and discriminator. Comparing to theexisting 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 ofover 80\% in terms of accuracy. Compared to the state-of-the-art methods, atleast 7% improvement on different metrics, including the precision, recall,F1-score, and harmonic mean, is delivered by the proposed SPN. To the best ofour knowledge, this is the first work focusing on solving the cardiovascularearly classification problem based on varied-length ECG data. Based on theseexcellent features from SPN, it offers a good exemplification for addressingall kinds of varied-length time series early classification problems.

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