Title: Fine-grained ECG Classification Based on Deep CNN and Online Decision Fusion,
Abstract: Early recognition of abnormal rhythm in ECG signals is crucial for monitoring
or diagnosing patients' cardiac conditions and increasing the success rate of
the treatment. Classifying abnormal rhythms into fine-grained categories is
very challenging due to the the broad taxonomy of rhythms, noises and lack of
real-world data and annotations from large number of patients. This paper
presents a new ECG classification method based on Deep Convolutional Neural
Networks (DCNN) and online decision fusion. Different from previous methods
which utilize hand-crafted features or learn features from the original signal
domain, the proposed DCNN based method learns features and classifiers from the
time-frequency domain in an end-to-end manner. First, the ECG wave signal is
transformed to time-frequency domain by using Short-Time Fourier Transform.
Next, specific DCNN models are trained on ECG samples of specific length.
Finally, an online decision fusion method is proposed to fuse past and current
decisions from different models into a more accurate one. Experimental results
on both synthetic and real-world ECG datasets convince the effectiveness and
efficiency of the proposed method.