Automated Dynamic Electrocardiogram Noise Reduction Using Multilayer LSTM Network

Junjie Guan, Rui Li, Renfa Li, Wanli Li, Jilong Wang, Guoqi Xie

Abstract: With the development of Internet of Things, the Healthcare Industrial IoT has become an effective way to curb the high mortality rate of heart disease. The accuracy of such system is mainly rely on the quality of ECG signals, in which noise reduction has been widely used. However, in the IoT environment, many kinds of noise which cannot be predicted in advance exist in signals, and make the signal morphology seriously damaged, which brings great challenge to the existing de-noise methods. By considering the self-adaptation and self-learning of deep neural network, we have proposed a multilayer LSTM model to the noise reduction of dynamic ECGs. Unlike other methods, our model makes both noise and ECG signals as part of time-series data, while other methods always consider them separately. Benefit from the recurrent structure of LSTM model, the most representative features will be extracted in LSTM memory units. By stacking multiple layers per time step, the useful information will be continuously refined and the noise signal will be discarded. Even if the ECG signals comprise many kinds of noise simultaneously, the model can still restore ECG signals with high quality without relying on threshold or signal quality. The experimental results show that the proposed model is insensitive to noise and the improvement of signal-to-noise ratio up to 55dB. This result is much better than the existing methods, which indicates LSTM is a new competitive method for ECG noise reduction.