  Recently, the Bag-Of-Word (BOW) algorithm provides efficient features and
promotes the accuracy of the ECG classification system. However, BOW algorithm
has two shortcomings: (1). it has large quantization errors and poor
reconstruction performance; (2). it loses heart beat's time information, and
may provide confusing features for different kinds of heart beats. Furthermore,
ECG classification system can be used for long time monitoring and analysis of
cardiovascular patients, while a huge amount of data will be produced, so we
urgently need an efficient compression algorithm. In view of the above
problems, we use the wavelet feature to construct the sparse dictionary, which
lower the quantization error to a minimum. In order to reduce the complexity of
our algorithm and adapt to large-scale heart beats operation, we combine the
Online Dictionary Learning with Feature-sign algorithm to update the dictionary
and coefficients. Coefficients matrix is used to represent ECG beats, which
greatly reduces the memory consumption, and solve the problem of quantitative
error simultaneously. Finally, we construct the pyramid to match coefficients
of each ECG beat. Thus, we obtain the features that contain the beat time
information by time stochastic pooling. It is efficient to solve the problem of
losing time information. The experimental results show that: on the one hand,
the proposed algorithm has advantages of high reconstruction performance for
BOW, this storage method is high fidelity and low memory consumption; on the
other hand, our algorithm yields highest accuracy in ECG beats classification;
so this method is more suitable for large-scale heart beats data storage and
classification.
