Method of diagnosing heart disease based on deep learning ECG signal
This addresses the need for automated, accurate heart disease diagnosis in medical applications, though it appears incremental as it builds on existing methods by integrating deep learning with signal processing.
The paper tackles the problem of diagnosing heart disease from ECG signals by developing an algorithm that combines signal processing and deep learning to classify signals into categories like Normal, AF, other rhythm, and noise, achieving an accuracy of 94% and an F1 score of 0.957, which surpasses the 2017 PhysioNet/CinC competition winner.
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.