  Early recognition of abnormal rhythms in ECG signals is crucial for
monitoring and diagnosing patients' cardiac conditions, increasing the success
rate of the treatment. Classifying abnormal rhythms into exact categories is
very challenging due to the broad taxonomy of rhythms, noises and lack of
large-scale real-world annotated data. Different from previous methods that
utilize hand-crafted features or learn features from the original signal
domain, we propose a novel ECG classification method by learning deep
time-frequency representation and progressive decision fusion at different
temporal scales in an end-to-end manner. First, the ECG wave signal is
transformed into the time-frequency domain by using the Short-Time Fourier
Transform. Next, several scale-specific deep convolutional neural networks are
trained on ECG samples of a specific length. Finally, a progressive online
decision fusion method is proposed to fuse decisions from the scale-specific
models into a more accurate and stable one. Extensive experiments on both
synthetic and real-world ECG datasets demonstrate the effectiveness and
efficiency of the proposed method.
