ABSTRACT
BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: The MES method is more effective than other methods. This technique may improve respiratory monitoring and assisted ventilation in patients with respiratory diseases.
