  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.
