A segment-wise reconstruction method based on bidirectional long short term memory for Power Line Interference suppression

Abstract

The overlap between the signal components of Power Line Interference (PLI) and biomedical signals in the frequency domain makes the filtered results prone to severe distortion. Electrocardiogram (ECG) is a type of biomedical electronic signal used for cardiac diagnosis. The objective of this work is to suppress the PLI components from biomedical signals with minimal distortion, and the object of study is mainly the ECG signals. In this study, we propose a novel segment-wise reconstruction method to suppress the PLI in biomedical signals based on the Bidirectional Recurrent Neural Networks with Long Short Term Memory (Bi-LSTM). Experiments are conducted on both synthetic and real signals, and quantitative comparisons are made with a traditional IIR notch filter and two state-of-the-art methods in the literature. The results show that by our method, the output Signal-to-Noise Ratio (SNR) is improved by more than 7dB and the settling time for step response is reduced to 0.09s on average. The results also demonstrate that our method has enough generalization ability for unforeseen signals without retraining.