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Noise Elimination of Electrocardiogram Signals Using the Evolutionary Bispectrum

The electrocardiogram (ECG) signal provides a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of the ECG waveform, leading to a false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Since when the signal is non-stationary, like the ECG signal, neither the regular power spectrum nor the bispectrum can handle this problem because they do not reflect the time variation of the process characteristics. With the recent introduction of the evolutionary bispectrum (EB) in digital signal processing, a new approach to the analysis problem has been devised. The work in this paper is focusing on the reduction of the noise interferences by introducing a new algorithm based on the EB. This approach exploits the fact that the EB contains information regarding both the phase and the magnitude of the system. Also, we will show that if the ECG signal is corrupted by stationary/non-stationary noise with symmetric distribution, the noise can be removed using the EB. To show the effectiveness of the proposed method, some simulation is declared.