  Deep convolutional neural networks (CNNs) have brought breakthroughs in
processing clinical electrocardiograms (ECGs), speaker-independent speech and
complex images. However, typical CNNs require a fixed input size while it is
common to process variable-size data in practical use. Recurrent networks such
as long short-term memory (LSTM) are capable of eliminating the restriction,
but suffer from high computational complexity. In this paper, we propose local
pattern aggregation-based deep-learning models to effectively deal with both
problems. The novel network structure, called LPANet, has cropping and
aggregation operations embedded into it. With these new features, LPANet can
reduce the difficulty of tuning model parameters and thus tend to improve
generalization performance. To demonstrate the effectiveness, we applied it to
the problem of premature ventricular contraction detection and the experimental
results shows that our proposed method has certain advantages compared to
classical network models, such as CNN and LSTM.
