Morphological ECG subtraction method for removing ECG artifacts from diaphragm EMG.

Abstract

Diaphragmatic electromyographic (EMGdi) is a helpful method to reflect the respiratory center's activity visually. However, the electrocardiogram (ECG) severely affected its weakness, limiting its use. To remove the ECG artifact from the EMGdi, we designed a Morphological ECG subtraction method (MES) based on three steps: 1) ECG localization, 2) morphological tracking, and 3) ECG subtractor. We evaluated the MES method against the wavelet-based dual-threshold and stationary wavelet filters using visual and frequency-domain characteristics (median frequency and power ratio). The results show that the MES method can preserve the features of the original diaphragm signal for both surface diaphragm signal (SEMGdi) and clinical collection of diaphragm signal (EMGdi_clinic), and it is more effective than the wavelet-based dual-threshold and stationary wavelet filtering methods. The MES method is more effective than other methods. This technique may improve respiratory monitoring and assisted ventilation in patients with respiratory diseases.