Effective removal of cardiac interference noise is essential to improve the accuracy of thoracic impedance respiration measurements.A cascaded LMS adaptive filter which can effectively remove the cardiac interference noise is proposed according to the characteristics of the real thoracic impedance respiration signal.The comparisons with several commonly used denoising algorithms reveal that the proposed algorithm can better remove cardiac interference while retaining the respiratory component of the original thoracic impedance respiration signal.The feasibility and accuracy of the proposed algorithm are further verified using real respiratory data with cardiac interference,and the experimental data show that the proposed method has a signal-to-noise ratio of(23.568±3.037)dB,a mean square error of 0.002±0.001,and a mean absolute error of 0.033±0.011,which fully confirms its effectiveness and stability.

Effective removal of cardiac interference noise is essential to improve the accuracy of thoracic impedance respiration measurements.A cascaded LMS adaptive filter which can effectively remove the cardiac interference noise is proposed according to the characteristics of the real thoracic impedance respiration signal.The comparisons with several commonly used denoising algorithms reveal that the proposed algorithm can better remove cardiac interference while retaining the respiratory component of the original thoracic impedance respiration signal.The feasibility and accuracy of the proposed algorithm are further verified using real respiratory data with cardiac interference,and the experimental data show that the proposed method has a signal-to-noise ratio of(23.568±3.037)dB,a mean square error of 0.002±0.001,and a mean absolute error of 0.033±0.011,which fully confirms its effectiveness and stability.

The main shortcomings of using electrocortical stimulation (ECS) in identifying the motor functional area around the focus in neurosurgery are certainly time-consuming, possibly cerebral cortex injuring and perhaps triggering epilepsy. To solve these problems, we in our research presented an intraoperative motor cortex functional mapping based on electrocorticography (ECoG). At first, using power spectrum estimation, we analyzed the characteristic of ECoG which was related to move task, and selected Mu rhythm as the move-related feature. Then we extracted the feature from original ECoG by multi-resolution wavelet analysis. By calculating the sum value of feature in every channel and observing the distribution of these sum values, we obtained the correlation between the cortex area under the electrode and motor cortex functional area. The results showed that the distribution of the relationship between the cortex under the electrode and motor cortex functional area was almost consistent with those identified by ECS which was called as the gold-standard. It indicated that this method was basically feasible, and it just needed five minutes totally. In conclusion, ECoG-based and passive identification of motor cortical function may serve as a useful adjunct to ECS in the intraoperative mapping.
Brain Mapping ; Electric Stimulation ; Electrocorticography ; Electrodes, Implanted ; Electroencephalography ; Epilepsy ; Humans ; Motor Cortex ; physiology ; Wavelet Analysis