  The traditional method of diagnosing heart disease on ECG signal is
artificial observation. Some have tried to combine expertise and signal
processing to classify ECG signal by heart disease type. However, the currency
is not so sufficient that it can be used in medical applications. We develop an
algorithm that combines signal processing and deep learning to classify ECG
signals into Normal AF other rhythm and noise, which help us solve this
problem. It is demonstrated that we can obtain the time-frequency diagram of
ECG signal by wavelet transform, and use DNN to classify the time-frequency
diagram to find out the heart disease that the signal collector may have.
Overall, an accuracy of 94 percent is achieved on the validation set. According
to the evaluation criteria of PhysioNet/Computing in Cardiology (CinC) in 2017,
the F1 score of this method is 0.957, which is higher than the first place in
the competition in 2017.
