EMGdi signal enhancement based on ICA decomposition and wavelet transform

The application of ECG cancellation in diaphragmatic electromyographic by using stationary wavelet transform

“…As a clinical signal, we can not accurately obtain its real signal, thus, SNR and PSRE are not useful to evaluate the efficient of algorithms. Clinical EMGdi is usually assessed by median frequency and total power after de-noising [13,17,18]. Total power is denoted as following…”

“…As shown in Table 2, median frequency of EMGdi processed by wavelet threshold is 138.37 Hz, and median frequency of EMGdi processed by ''inverse'' hard threshold is 108.36 Hz. In literatures [13,17,18], normal median frequency of EMGdi is in the range from 80 to 117 Hz after removing the ECG interference. Median frequency of EMGdi de-noised by ''inverse'' hard threshold is closer to normal EMGdi than wavelet threshold.…”

“…In order to verify the effectiveness of different algorithm for solving the problem of detection of QRS wave, Orthogonal Wavelet Transform (OWT) algorithm [13,17,18] and Max Min Difference (MMD) algorithm [21] are used for comparing with Stationary Wavelet Transform (SWT) algorithm proposed in this paper. Besides, we use MIT-BIH arrhythmia ECG data [22] as the objects of QRS wave detection with different signal to noise ratio (SNR).…”

“…At present, various algorithms were proposed to remove the ECG from EMGdi. Independent component analysis was used for separating ECG from EMGdi [13], however, this method required 5 channels to record EMGdi signal, which consumed much more hardware resources. As a tradition signal processing method, high pass filter removed low frequency and kept high frequency [14], which was not effective way to eliminate ECG noise in EMGdi.…”

“…Time-frequency characteristic of wavelet transform can reflect the change of local short signal, therefore, wavelet transform was widely used in non-stationary biological signal [15][16][17][18]. According to the distribution characteristic of ECG, wavelet threshold algorithm was used to localize QRS position and set up the de-noised threshold [13,17,18]. Most of the ECG interference were removed in this algorithm, but it still had several shortcomings: firstly, wavelet coefficients that are bigger than threshold are ignored, which lead to loss information of EMGdi; secondly, the proportions of threshold are adjusted by experimental experience; finally, the detection of QRS peak location is based on correlation arithmetic large scale coefficients, which may result in inaccuracy of QRS location.…”