EMGdi signal enhancement based on ICA decomposition and wavelet transform

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Morphological ECG subtraction method for removing ECG artifacts from diaphragm EMG

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.

A DENOISING METHOD OF DIAPHRAGM ELECTROMYOGRAM SIGNALS BASED ON DUAL-THRESHOLD FILTER

Diaphragmatic electromyography (EMGdi) signals can effectively reflect human respiratory process and effort, but simultaneously interfered by electrocardiogram (ECG) signals, leading to the limited application of EMGdi signals. Conventional denoising schemes for ECG cancellation from EMGdi signals are usually limited to the cases of irregular ECG signals. For this purpose, this paper proposes a denoising method of ECG signals by using a dual-threshold filter, which performs effectively in the scenarios of irregular ECG condition, such as arrhythmia. Specifically, we first employ wavelet transform to decompose EMGdi signals to wavelets of different frequencies, by which QRS complex detection is performed to determine the location of ECG signals. Then we propose to remove the ECG interference by reforming the interference range with the adjacent signals. Experimental results indicate that the proposed denoising method performs superior to the state-of-the-art schemes, especially for the cases of weak EMGdi signal scenarios.

Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging

According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods cannot meet the demands of further engineering applications. In order to improve the imaging accuracy of our single-pixel camera, some optimization methods of key technologies in compressive sensing are proposed in this paper. First, in terms of sparse signal decomposition, based on traditional discrete wavelet transform and the characteristics of coefficients distribution in wavelet domain, a constraint condition of the exponential decay is proposed and a corresponding constraint matrix is designed to optimize the original wavelet decomposition basis. Second, for the construction of deterministic binary sensing matrices in the single-pixel camera, on the basis of a Gram matrix, a new algorithm model and a new method of initializing a compressed sensing measurement matrix are proposed to optimize the traditional binary sensing matrices via mutual coherence minimization. The gradient projection-based algorithm is used to solve the new mathematical model and train deterministic binary sensing measurement matrices with better performance. Third, the proposed optimization methods are applied to our single-pixel imaging system for optimizing the existing imaging methods. Compared with the conventional methods of single-pixel imaging, the accuracy of image reconstruction and the quality of single-pixel imaging have been significantly improved by our methods. The superior performance of our proposed methods has been fully tested and the effectiveness has also been demonstrated by numerical simulation experiments and practical imaging experiments.

Differential activation of the human costal and crural diaphragm during voluntary and involuntary breaths

Simultaneous electromyographic recordings from the human costal and crural diaphragm during voluntary augmented breathing and involuntary rebreathing show that the increase in inspiratory crural diaphragm activity was ~60% of the increase in costal diaphragm activity. However costal to crural diaphragm activation did not differ between the two tasks. The dissociation in the amplitude of activation of the costal and crural diaphragm becomes apparent only as the drive to breathe increases above tidal breathing.

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

In this paper, we present and investigate a special kind of stationary wavelet algorithm using "inverse" hard threshold to eliminate the electrocardiogram (ECG) interference included in diaphragmatic electromyographic (EMGdi). Differing from traditional wavelet hard threshold, "inverse" hard threshold is used to shrink strong coefficients of ECG interference and reserve weak coefficients of EMGdi signal. Meanwhile, a novel QRS location algorithm is proposed for the position detection of R wave by using low frequency coefficients in this paper. With the proposed method, raw EMGdi is decomposed by wavelet at fifth scale. Then, each ECG interference threshold is calculated by mean square, which is estimated by wavelet coefficients in the ECG cycle at each level. Finally, ECG interference wavelet coefficients are removed by "inverse" hard threshold, and then the de-noised signal is reconstructed by wavelet coefficients. The simulation and clinical EMGdi de-noising results show that the "inverse" hard threshold investigated in this paper removes the ECG interference in EMGdi availably and reserves its signal characteristics effectively, as compared to wavelet threshold.