  This paper proposes piece-wise matching layer as a novel layer in
representation learning methods for electrocardiogram (ECG) classification.
Despite the remarkable performance of representation learning methods in the
analysis of time series, there are still several challenges associated with
these methods ranging from the complex structures of methods, the lack of
generality of solutions, the need for expert knowledge, and large-scale
training datasets. We introduce the piece-wise matching layer that works based
on two levels to address some of the aforementioned challenges. At the first
level, a set of morphological, statistical, and frequency features and
comparative forms of them are computed based on each periodic part and its
neighbors. At the second level, these features are modified by predefined
transformation functions based on a receptive field scenario. Several scenarios
of offline processing, incremental processing, fixed sliding receptive field,
and event-based triggering receptive field can be implemented based on the
choice of length and mechanism of indicating the receptive field. We propose
dynamic time wrapping as a mechanism that indicates a receptive field based on
event triggering tactics. To evaluate the performance of this method in time
series analysis, we applied the proposed layer in two publicly available
datasets of PhysioNet competitions in 2015 and 2017 where the input data is ECG
signal. We compared the performance of our method against a variety of known
tuned methods from expert knowledge, machine learning, deep learning methods,
and the combination of them. The proposed approach improves the state of the
art in two known completions 2015 and 2017 around 4% and 7% correspondingly
while it does not rely on in advance knowledge of the classes or the possible
places of arrhythmia.
