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Xu Zhang
University of Science and Technology of China, Hefei, China

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Journal article
Published: 09 March 2021 in IEEE Journal of Translational Engineering in Health and Medicine
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Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average $F_{1}$ score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.

ACS Style

Jing Zhang; Deng Liang; Aiping Liu; Min Gao; Xiang Chen; Xu Zhang; Xun Chen. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG. IEEE Journal of Translational Engineering in Health and Medicine 2021, 9, 1 -11.

AMA Style

Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chen. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG. IEEE Journal of Translational Engineering in Health and Medicine. 2021; 9 ():1-11.

Chicago/Turabian Style

Jing Zhang; Deng Liang; Aiping Liu; Min Gao; Xiang Chen; Xu Zhang; Xun Chen. 2021. "MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG." IEEE Journal of Translational Engineering in Health and Medicine 9, no. : 1-11.

Journal article
Published: 06 January 2021 in Computers in Biology and Medicine
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Myoelectric interfaces have received much attention in the field of prosthesis control, neuro-rehabilitation systems and human-computer interaction. However, when different users perform the same gesture, the electromyography (EMG) signals can vary greatly. It is essential to design a multiuser myoelectric interface that can be simply used by novel users while maintaining good gesture classification performance. To cope with this problem, canonical correlation analysis (CCA) has been used to extract the inherent user-independent properties of EMG signals generated from the same gestures from multiple users and demonstrated superior performance. In this paper, we move forward to propose a novel framework based on CCA and optimal transport (OT), termed as CCA-OT. By optimal transport, the discrepancies in data distribution between the transformed feature matrix from the training and the testing sets can be further reduced. Experimental results on the defined 13 Chinese sign language gestures performed by 10 intact-limbed subjects demonstrated that the classification rate of our proposed CCA-OT framework is significantly higher than that of the CCA-only framework with an 8.49% promotion, which shows the necessity to reduce the drift in probability distribution functions (PDFs) of the different domains. The CCA-OT framework provides a promising method for the multiuser myoelectric interface which can be easily adapted to new users. This improvement will further facilitate the widespread implementation of myoelectric control systems using pattern recognition techniques.

ACS Style

Bo Xue; Le Wu; Kun Wang; Xu Zhang; Juan Cheng; Xiang Chen; Xun Chen. Multiuser gesture recognition using sEMG signals via canonical correlation analysis and optimal transport. Computers in Biology and Medicine 2021, 130, 104188 .

AMA Style

Bo Xue, Le Wu, Kun Wang, Xu Zhang, Juan Cheng, Xiang Chen, Xun Chen. Multiuser gesture recognition using sEMG signals via canonical correlation analysis and optimal transport. Computers in Biology and Medicine. 2021; 130 ():104188.

Chicago/Turabian Style

Bo Xue; Le Wu; Kun Wang; Xu Zhang; Juan Cheng; Xiang Chen; Xun Chen. 2021. "Multiuser gesture recognition using sEMG signals via canonical correlation analysis and optimal transport." Computers in Biology and Medicine 130, no. : 104188.

Journal article
Published: 07 December 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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Estimation of muscle contraction force based on the macroscopic feature of surface electromyography (SEMG) has been widely reported, but the use of microscopic neural drive information has not been thoroughly investigated. In this study, a novel method is proposed to process individual motor unit (MU) activities (firing sequences and action potential waveforms) derived from the decomposition of high density SEMG (HD-SEMG), and it is applied to muscle force estimation. In the proposed method, a supervised machine learning approach was conducted to determine the twitch force of each MU according to its action potential waveforms, which enables separate calculation of every MU’s contribution to force. Thus, the muscle force was predicted through a physiologically meaningful muscle force model. In the experiment, HD-SEMG data were recorded from the abductor pollicis brevis muscles of eight healthy subjects during their performance of thumb abduction with the force increasing gradually from zero to four force levels (10%, 20%, 30%, 40% of the maximal voluntary contraction), while the true muscle force was measured simultaneously. When the proposed method was used, the root mean square difference (RMSD) of the error of the estimated force with respect to the measured force was reported to be 8.3% ± 2.8%. The proposed method also significantly outperformed the other four common methods for force estimation (RMSD: from 11.7% to 20%, p < 0.001), demonstrating its effectiveness. This study offers a useful tool for exploiting the neural drive information towards muscle force estimation with improved precision. The proposed method has wide applications in precise motor control, sport and rehabilitation medicine.

ACS Style

Xu Zhang; Ge Zhu; Maoqi Chen; Xun Chen; Xiang Chen; Ping Zhou. Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 3148 -3157.

AMA Style

Xu Zhang, Ge Zhu, Maoqi Chen, Xun Chen, Xiang Chen, Ping Zhou. Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (12):3148-3157.

Chicago/Turabian Style

Xu Zhang; Ge Zhu; Maoqi Chen; Xun Chen; Xiang Chen; Ping Zhou. 2020. "Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 12: 3148-3157.

Journal article
Published: 03 December 2020 in Journal of NeuroEngineering and Rehabilitation
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Background Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated. Methods Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI). Results The experimental results showed that: (1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z scores; (2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; (3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly. Conclusions This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.

ACS Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury. Journal of NeuroEngineering and Rehabilitation 2020, 17, 1 -14.

AMA Style

Xu Zhang, Xinhui Li, Xiao Tang, Xun Chen, Xiang Chen, Ping Zhou. Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury. Journal of NeuroEngineering and Rehabilitation. 2020; 17 (1):1-14.

Chicago/Turabian Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. 2020. "Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury." Journal of NeuroEngineering and Rehabilitation 17, no. 1: 1-14.

Preprint content
Published: 11 November 2020
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Background: Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated.Methods: Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI).Results: The experimental results showed that: 1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z-scores; 2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; 3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly.Conclusions: This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.

ACS Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury. 2020, 1 .

AMA Style

Xu Zhang, Xinhui Li, Xiao Tang, Xun Chen, Xiang Chen, Ping Zhou. Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury. . 2020; ():1.

Chicago/Turabian Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. 2020. "Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury." , no. : 1.

Journal article
Published: 06 October 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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To reduce the bad effect of electrode shifts on myoelectric pattern recognition, this paper presents an adaptive electrode calibration method based on core activation regions of muscles. In the proposed method, the high-density surface electromyography (HD-sEMG) matrix collected during hand gesture execution is decomposed into source signal matrix and mixed coefficient matrix by fast independent component analysis algorithm firstly. The mixed coefficient vector whose source signal has the largest two-norm energy is selected as the major pattern, and core activation region of muscles is extracted by traversing the major pattern periodically using a sliding window. The electrode calibration is realized by aligning the core activation regions in unsupervised way. Gestural HD-sEMG data collection experiments with known and unknown electrode shifts are carried out on 9 gestures and 11 participants. A CNN+LSTM-based network is constructed and two network training strategies are adopted for the recognition task. The experimental results demonstrate the effectiveness of the proposed method in mitigating the bad effect of electrode shifts on gesture recognition accuracy and the potentials in reducing user training burden of myoelectric control systems. With the proposed electrode calibration method, the overall gesture recognition accuracies increase about (5.72∼7.69)%. In specific, the average recognition accuracy increases (13.32∼17.30)% when using only one batch of data in data diversity strategy, and increases (12.01∼13.75)% when using only one repetition of each gesture in model update strategy. The proposed electrode calibration algorithm can be extended and applied to improve the robustness of myoelectric control system.

ACS Style

Ruochen Hu; Xiang Chen; Xu Zhang; Xun Chen. Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 29, 11 -20.

AMA Style

Ruochen Hu, Xiang Chen, Xu Zhang, Xun Chen. Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 29 ():11-20.

Chicago/Turabian Style

Ruochen Hu; Xiang Chen; Xu Zhang; Xun Chen. 2020. "Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29, no. : 11-20.

Original research article
Published: 17 July 2020 in Frontiers in Bioengineering and Biotechnology
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Background: There is a great demand for convenient and quantitative assessment of upper-limb traumatic peripheral nerve injuries (PNIs) beyond their clinical routine. This would contribute to improved PNI management and rehabilitation. Objective: The aim of this study was to develop a novel surface EMG examination method for quantitatively evaluating traumatic upper-limb PNIs. Methods: Experiments were conducted to collect surface EMG data from forearm muscles on both sides of seven male subjects during their performance of eight designated hand and wrist motion tasks. All participants were clinically diagnosed as unilateral traumatic upper-limb PNIs on the ulnar nerve, median nerve, or radial nerve. Ten healthy control participants were also enrolled in the study. A novel framework consisting of two modules was also proposed for data analysis. One module was first used to identify whether a PNI occurs on a tested forearm using a machine learning algorithm by extracting and classifying features from surface EMG data. The second module was then used to quantitatively evaluate the degree of injury on three individual nerves on the examined arm. Results: The evaluation scores yielded by the proposed method were highly consistent with the clinical assessment decisions for three nerves of all 34 examined arms (7 × 2 + 10 × 2), with a sensitivity of 81.82%, specificity of 98.90%, and significate linear correlation (p < 0.05) in quantitative decision points between the proposed method and the routine clinical approach. Conclusion: This study offers a useful tool for PNI assessment and helps to promote extensive clinical applications of surface EMG.

ACS Style

Weidi Tang; Xu Zhang; Yong Sun; Bo Yao; Xiang Chen; Xun Chen; Xiaoping Gao. Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography. Frontiers in Bioengineering and Biotechnology 2020, 8, 795 .

AMA Style

Weidi Tang, Xu Zhang, Yong Sun, Bo Yao, Xiang Chen, Xun Chen, Xiaoping Gao. Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography. Frontiers in Bioengineering and Biotechnology. 2020; 8 ():795.

Chicago/Turabian Style

Weidi Tang; Xu Zhang; Yong Sun; Bo Yao; Xiang Chen; Xun Chen; Xiaoping Gao. 2020. "Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography." Frontiers in Bioengineering and Biotechnology 8, no. : 795.

Preprint content
Published: 17 July 2020
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Background: Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated. Methods: Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI). Results: The experimental results showed that: 1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z-scores; 2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; 3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly. Conclusions: This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.

ACS Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury. 2020, 1 .

AMA Style

Xu Zhang, Xinhui Li, Xiao Tang, Xun Chen, Xiang Chen, Ping Zhou. Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury. . 2020; ():1.

Chicago/Turabian Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. 2020. "Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury." , no. : 1.

Journal article
Published: 15 July 2020 in IEEE Journal of Biomedical and Health Informatics
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This paper presents an effective transfer learning (TL) strategy for the realization of surface electromyography (sEMG)-based gesture recognition with high generalization and low training burden. To realize the idea of taking a well-trained model as the feature extractor of the target networks, a convolutional neural network (CNN)-based source network is designed and trained as the general gesture EMG feature extraction network firstly. To fully cover possible muscle activation modes related to hand gestures, 30 hand gestures involving various states of finger joints, elbow joint and wrist joint are selected to compose the source task. Then, two types of target networks, in the forms of CNN-only and CNN+LSTM (long short-term memory) respectively, are designed with the same CNN architecture as the feature extraction network. Finally, gesture recognition experiments on three different target gesture datasets are carried out under TL and Non-TL strategies respectively. The experimental results verify the validity of the proposed TL strategy in improving hand gesture recognition accuracy and reducing training burden. For both the CNN-only and the CNN+LSTM target networks, on the three target datasets from new users, new gestures and different collection scheme, the proposed TL strategy improves the recognition accuracy by 10%~38%, reduces the training time to tens of times, and guarantees the recognition accuracy of more than 90% when only 2 repetitions of each gesture are used to fine-tune the parameters of target networks. The proposed TL strategy has important application value for promoting the development of myoelectric control systems.

ACS Style

Xiang Chen; Yu Li; Ruochen Hu; Xu Zhang; Xun Chen. Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method. IEEE Journal of Biomedical and Health Informatics 2020, 25, 1292 -1304.

AMA Style

Xiang Chen, Yu Li, Ruochen Hu, Xu Zhang, Xun Chen. Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method. IEEE Journal of Biomedical and Health Informatics. 2020; 25 (4):1292-1304.

Chicago/Turabian Style

Xiang Chen; Yu Li; Ruochen Hu; Xu Zhang; Xun Chen. 2020. "Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method." IEEE Journal of Biomedical and Health Informatics 25, no. 4: 1292-1304.

Journal article
Published: 16 June 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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The objective of this study is to explore the diagnostic decision and sensitivity of the surface electromyogram (EMG) clustering index (CI) with respect to post-stroke motor unit (MU) alterations through a simulation approach by the existing motor neuron pool model and surface EMG model. In the simulation analysis, three patterns of diagnostic decisions were presented in 24 groups representing eight types in three degrees of MU alterations. Specifically, the CI decision exhibited an abnormally increased pattern for five types, an abnormally decreased pattern for two types, and an invariant pattern for one type. Furthermore, the CI diagnostic decision was found to be highly sensitive to three types because a 50% degree of alteration in these types resulted in a distinct deviation of 2.5 in the CI Z-score. The mixed CI patterns were confirmed in experimental data collected from the paretic muscles of 14 subjects with stroke, as compared to the healthy muscles of 10 control subjects. Given the simulation results as a guideline, the CI diagnostic decision could be interpreted from general neural or muscular changes into specific MU changes (in eight types). This can further promote clinical applications of the convenient surface EMG tool in examining and monitoring paretic muscle changes toward customized stroke rehabilitation.

ACS Style

Xu Zhang; Xiao Tang; Zhongqing Wei; Xiang Chen; Xun Chen. Model-Based Sensitivity Analysis of EMG Clustering Index With Respect to Motor Unit Properties: Investigating Post-Stroke FDI Muscle. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 1836 -1845.

AMA Style

Xu Zhang, Xiao Tang, Zhongqing Wei, Xiang Chen, Xun Chen. Model-Based Sensitivity Analysis of EMG Clustering Index With Respect to Motor Unit Properties: Investigating Post-Stroke FDI Muscle. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (8):1836-1845.

Chicago/Turabian Style

Xu Zhang; Xiao Tang; Zhongqing Wei; Xiang Chen; Xun Chen. 2020. "Model-Based Sensitivity Analysis of EMG Clustering Index With Respect to Motor Unit Properties: Investigating Post-Stroke FDI Muscle." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 8: 1836-1845.

Journal article
Published: 13 April 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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High-density surface electromyography (HD-sEMG) can provide rich temporal and spatial information about muscle activation. However, HD-sEMG signals are often contaminated by power line interference (PLI) and white Gaussian noise (WGN). In the literature, independent component analysis (ICA) and canonical correlation analysis (CCA), as two popular used blind source separation techniques, are widely used for noise removal from HD-sEMG signals. In this paper, a novel method to remove PLI and WGN was proposed based on independent vector analysis (IVA). Taking advantage of both ICA and CCA, this method exploits the higher order and second-order statistical information simultaneously. Our proposed method was applied to both simulated and experimental EMG data for performance evaluation, which was at least 37.50% better than ICA and CCA methods in terms of relative root mean squared error and 28.84% better than ICA and CCA methods according to signal to noise ratio. The results demonstrated that our proposed method performed significantly better than either ICA or CCA. Specifically, the mean signal to noise ratio increased considerably. Our proposed method is a promising tool for denoising HD-sEMG signals while leading to a minimal distortion.

ACS Style

Kun Wang; Xun Chen; Le Wu; Xu Zhang; Xiang Chen; Z. Jane Wang. High-Density Surface EMG Denoising Using Independent Vector Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 1271 -1281.

AMA Style

Kun Wang, Xun Chen, Le Wu, Xu Zhang, Xiang Chen, Z. Jane Wang. High-Density Surface EMG Denoising Using Independent Vector Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (6):1271-1281.

Chicago/Turabian Style

Kun Wang; Xun Chen; Le Wu; Xu Zhang; Xiang Chen; Z. Jane Wang. 2020. "High-Density Surface EMG Denoising Using Independent Vector Analysis." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 6: 1271-1281.

Journal article
Published: 19 March 2020 in IEEE Transactions on Instrumentation and Measurement
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To achieve high precision and robust positioning on mobile devices, we propose a novel audio signal arrival time detection algorithm consisting of both coarse and fine searches. A coarse search uses the cosine theorem to extract the audio data segment (ADS) from the received signal of a mobile device, and a fine search then uses the waveform characteristics of the auto-correlation function of the source signal to estimate the audio arrival time (AAT) from the ADS. An indoor threshold determination experiment, a static positioning experiment, and a moving target positioning experiment were carried out on the developed acoustic indoor positioning system (AIPS) in a hall. The performance of the proposed audio detection algorithm was compared to two other typical detection algorithms. The results of the static positioning experiment show that, in an non-line of sight (NLOS) environment, 90% of the positioning results estimated by the proposed algorithm have an error of less than 0.50 m, whereas only 56% and 6% of the positioning results estimated by the other two classical algorithms have errors of less than 0.50 m. In the target moving positioning experiment, the positions estimated by the proposed algorithm are closer to the true trajectories and have fewer (less than 0.5%) abnormal positioning errors than the other two classical algorithms. The experiment results also show that the reliability judgement of the positioning results can further improve the robustness of the system. The proposed method has important application value for the implementation of a highly precise and reliable AIPS.

ACS Style

Shuai Cao; Xiang Chen; Xu Zhang; Xun Chen. Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning. IEEE Transactions on Instrumentation and Measurement 2020, 69, 7341 -7352.

AMA Style

Shuai Cao, Xiang Chen, Xu Zhang, Xun Chen. Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning. IEEE Transactions on Instrumentation and Measurement. 2020; 69 (10):7341-7352.

Chicago/Turabian Style

Shuai Cao; Xiang Chen; Xu Zhang; Xun Chen. 2020. "Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning." IEEE Transactions on Instrumentation and Measurement 69, no. 10: 7341-7352.

Preprint content
Published: 17 March 2020
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Background: Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated.Methods: Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI).Results: The experimental results showed that: 1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z-scores; 2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; 3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly.Conclusions: This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.

ACS Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury. 2020, 1 .

AMA Style

Xu Zhang, Xinhui Li, Xiao Tang, Xun Chen, Xiang Chen, Ping Zhou. Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury. . 2020; ():1.

Chicago/Turabian Style

Xu Zhang; Xinhui Li; Xiao Tang; Xun Chen; Xiang Chen; Ping Zhou. 2020. "Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury." , no. : 1.

Research article
Published: 31 December 2019 in Journal of Healthcare Engineering
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Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

ACS Style

Qingze Liu; Aiping Liu; Xu Zhang; Xiang Chen; Ruobing Qian; Xun Chen. Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis. Journal of Healthcare Engineering 2019, 2019, 1 -13.

AMA Style

Qingze Liu, Aiping Liu, Xu Zhang, Xiang Chen, Ruobing Qian, Xun Chen. Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis. Journal of Healthcare Engineering. 2019; 2019 ():1-13.

Chicago/Turabian Style

Qingze Liu; Aiping Liu; Xu Zhang; Xiang Chen; Ruobing Qian; Xun Chen. 2019. "Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis." Journal of Healthcare Engineering 2019, no. : 1-13.

Journal article
Published: 16 September 2019 in IEEE Journal of Biomedical and Health Informatics
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In this paper, deep belief net (DBN) was applied into the field of wearable-sensor based Chinese Sign Language (CSL) recognition. Eight subjects were involved in the study, and all of the subjects finished a five-day experiment performing CSL on a target word set consisting of 150 CSL subwords. During the experiment, surface electromyography (sEMG), accelerometer (ACC), and gyroscope (GYRO) signals were collected from the participants. In order to obtain the optimal structure of the network, three different sensor fusion strategies, including data-level fusion, feature-level fusion, and decision-level fusion, were explored. In addition, for the feature-level fusion strategy, two different feature sources, which are hand-crafted features and network generated features, and two different network structures, which are fully-connected net and DBN, were also compared. The result showed that feature level fusion could achieve the best recognition accuracy among the three fusion strategies, and feature-level fusion with network generated features and DBN could achieve the best recognition accuracy. The best recognition accuracy realized in this study was 95.1% for the user-dependent test and 88.2% for the user-independent test. The significance of the study is that it applied the deep learning method into the field of wearable sensors-based CSL recognition, and according to our knowledge it's the first study comparing human engineered features with the network generated features in the correspondent field. The results from the study shed lights on the method of using network-generated features during sensor fusion and CSL recognition.

ACS Style

Yi Yu; Xiang Chen; Shuai Cao; Xu Zhang; Xun Chen. Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net. IEEE Journal of Biomedical and Health Informatics 2019, 24, 1310 -1320.

AMA Style

Yi Yu, Xiang Chen, Shuai Cao, Xu Zhang, Xun Chen. Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net. IEEE Journal of Biomedical and Health Informatics. 2019; 24 (5):1310-1320.

Chicago/Turabian Style

Yi Yu; Xiang Chen; Shuai Cao; Xu Zhang; Xun Chen. 2019. "Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net." IEEE Journal of Biomedical and Health Informatics 24, no. 5: 1310-1320.

Accepted manuscript
Published: 01 July 2019 in Journal of Neural Engineering
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In this study, we proposed a dynamic voluntary contraction force estimation framework and implemented it for elbow-flexion force estimation during arm posture dynamically changing between pronation and supination. High-density surface electromyography (HD-sEMG) from biceps brachii and brachialis muscles and the elbow-flexion force were measured synchronously. The simplified Hill model was adopted to establish the relation between HD-sEMG and the elbow-flexion force. In the training process of the force estimation model, HD-sEMG data from static isometric elbow flexion tasks in two force modes (staircase and sinusoidal) and two postures (supination or pronation) were used. The nonnegative matrix factorization (NMF) algorithm was adopted to decompose HD-sEMG into activation patterns and the corresponding time-varying coefficient vectors. The major activation pattern was used to select the appropriate sEMG channels for extracting the input signal of the force estimation model. In the testing phase, the elbow-flexion force estimation during arm posture dynamically changing between pronation and supination was conducted. HD-sEMG was also decomposed using NMF algorithm. In this case, the concept of the major activation pattern is no longer applicable because the activation areas of biceps brachii and brachialis vary with the change of arm posture. An improved channel selection scheme based on the ratio of activation intensities was presented to extract the input signal of the well-trained simplified Hill model. (1) the improved channel selection scheme could locate effectively the primary muscle activation areas related to the change of arm posture; (2) the input signal extraction method based on the ratio of activation intensities obtained the best force estimation performance compared to the method based on all channels of HD-sEMG array and the method based on major activation pattern; (3) the force estimation performance was better when the simplified Hill model was calibrated with the sinusoidal force data rather than staircase data. This research provides an effective solution to realize muscle force estimation during a dynamic voluntary contraction task. It can be further extended to the research fields of biomechanics, sports, and rehabilitation medicine.

ACS Style

Ruochen Hu; Xiang Chen; Chengjun Huang; Shuai Cao; Xu Zhang; Xun Chen. Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination. Journal of Neural Engineering 2019, 16, 066005 .

AMA Style

Ruochen Hu, Xiang Chen, Chengjun Huang, Shuai Cao, Xu Zhang, Xun Chen. Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination. Journal of Neural Engineering. 2019; 16 (6):066005.

Chicago/Turabian Style

Ruochen Hu; Xiang Chen; Chengjun Huang; Shuai Cao; Xu Zhang; Xun Chen. 2019. "Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination." Journal of Neural Engineering 16, no. 6: 066005.

Journal article
Published: 22 May 2019 in Sensors
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This paper presents a novel audio indoor localization system. In the proposed system, four speakers placed at known positions transmit chirp signals according to the time-division multiple access (TDMA) plus frequency-division multiple access (FDMA) transmission scheme. A smartphone receives the signal via a built-in microphone and calculates the time differences of arrival (TDOAs). Using TDOA measurements, the position is estimated by the shrinking-circle method. In particular, to reduce the positioning error in moving conditions, a TDOA correction method based on Doppler shifts is proposed. The performance of the proposed system was evaluated in real-world experiments using a 10.971 m × 5.684 m positioning area. The results of the static-target positioning experiment showed that the TDMA+FDMA transmission scheme has more advantages in improving the update rate of the positioning system than the TDMA-only transmission scheme. The results of the moving-target positioning experiment under three different speeds demonstrated that the positioning errors were reduced by about 10 cm when the Doppler-shift-based TDOA correction method was adopted. This research provides a possible framework for the realization of a TDOA-chirp-based acoustic indoor positioning system with high positioning accuracy and update rate.

ACS Style

Xiang Chen; Yuheng Chen; Shuai Cao; Lei Zhang; Xu Zhang; Xun Chen. Acoustic Indoor Localization System Integrating TDMA+FDMA Transmission Scheme and Positioning Correction Technique. Sensors 2019, 19, 2353 .

AMA Style

Xiang Chen, Yuheng Chen, Shuai Cao, Lei Zhang, Xu Zhang, Xun Chen. Acoustic Indoor Localization System Integrating TDMA+FDMA Transmission Scheme and Positioning Correction Technique. Sensors. 2019; 19 (10):2353.

Chicago/Turabian Style

Xiang Chen; Yuheng Chen; Shuai Cao; Lei Zhang; Xu Zhang; Xun Chen. 2019. "Acoustic Indoor Localization System Integrating TDMA+FDMA Transmission Scheme and Positioning Correction Technique." Sensors 19, no. 10: 2353.

Review
Published: 21 March 2019 in IEEE Sensors Journal
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Electroencephalography (EEG) has been widely used for studying brain function. As cortical signals recorded by the EEG are very weak, they are often obscured by motion artifacts and various non-brain physiological activities, such as eye movement, heart rhythms, and muscle activity, adversely affecting subsequent analysis and interpretation. Over the past decades, a number of techniques have been developed for preprocessing EEG recordings to improve the signal-to-noise ratio. However, based on our extensive literature survey, despite an increasing trend recently, only 14.72% of published studies on EEG preprocessing were involved with the removal of electromygraphic (EMG) artifacts. Given that ambulatory healthcare systems are continuously emerging, artifacts induced by muscle contraction become unavoidable, whereas in the past data tended to be collected in well-controlled clinical/laboratory settings. Motivated by the fact that EMG artifact removal is becoming an important issue to be addressed, we investigated the state-of-the-art muscle artifact removal methods systematically and comparatively, from the perspective of signal processing. In this review, we first present the signal characteristics from brain and muscle activity, and highlight the importance of this issue for subsequent artifact removal. We then provide an overview and taxonomy of representative methods. Based on the results in reported studies, we describe the pros and cons of different methods and give suggestions on selecting a suitable technique in different scenarios (e.g., single-channel, few-channel, and multichannel). Finally, we discuss remaining challenges and provide feasible recommendations for further exploration in this field.

ACS Style

Xun Chen; Xueyuan Xu; Aiping Liu; Soojin Lee; Xiang Chen; Xu Zhang; Martin J. McKeown; Z. Jane Wang. Removal of Muscle Artifacts From the EEG: A Review and Recommendations. IEEE Sensors Journal 2019, 19, 5353 -5368.

AMA Style

Xun Chen, Xueyuan Xu, Aiping Liu, Soojin Lee, Xiang Chen, Xu Zhang, Martin J. McKeown, Z. Jane Wang. Removal of Muscle Artifacts From the EEG: A Review and Recommendations. IEEE Sensors Journal. 2019; 19 (14):5353-5368.

Chicago/Turabian Style

Xun Chen; Xueyuan Xu; Aiping Liu; Soojin Lee; Xiang Chen; Xu Zhang; Martin J. McKeown; Z. Jane Wang. 2019. "Removal of Muscle Artifacts From the EEG: A Review and Recommendations." IEEE Sensors Journal 19, no. 14: 5353-5368.

Journal article
Published: 01 January 2019 in Mathematical Biosciences and Engineering
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Complex neuromuscular changes have been reported to occur in paretic muscles following stroke, but whether and how they can recover under rehabilitation therapy remain unclear. A tracking analysis protocol needs to be designed involving multiple sessions of surface electromyography (sEMG) examinations during the rehabilitation procedure. Following such a protocol, this pilot study is aimed to monitor paretic muscle changes using three sEMG indicators namely clustering index (CI), root mean square (RMS) and medium frequency (MDF). Initially, a single sEMG examination was performed on the abductor pollicis brevis (APB) muscle on both sides of 23 subjects with stroke and one side of 18 healthy control subjects. With these data to establish CI diagnostic criterion, the paretic muscles of all subjects with stroke showed a very board CI distribution pattern from abnormally low values through normality to abnormally high values. Afterwards, 9 out of 23 subjects with stroke had their paretic muscles examined at least twice before and after the treatment. Almost all paretic muscles had an increase of the RMS, a change of the MDF approaching to the value of the contralateral muscle, and a change of the CI returning to its normal range after common rehabilitation treatments. Finally, 4 of the 9 subjects with stroke participated into repeated examinations of their paretic muscles. The combined use of three indicators helped to reveal specific neuromuscular processes contributing to recovery of paretic muscles, due to their complementary diagnostic powers. Furthermore, neuromuscular processes were found to vary across subjects in type, order and timing during rehabilitation. In conclusion, given the 4 cases following the tracking analysis protocol, this pilot study preliminarily demonstrates usability of three sEMG indicators as tools for examining and monitoring stroke rehabilitation procedure in terms of improvements of paretic muscle changes. All the revealed complex neuromuscular processes imply the necessity of applying sEMG examinations in monitoring rehabilitation procedure, with the potential of offering important guidelines for designing better and individualized protocols toward improved stroke rehabilitation.

ACS Style

Ge Zhu; Xu Zhang; Xiao Tang; Xiang Chen; Xiaoping Gao. Examining and monitoring paretic muscle changes during stroke rehabilitation using surface electromyography: A pilot study. Mathematical Biosciences and Engineering 2019, 17, 216 -234.

AMA Style

Ge Zhu, Xu Zhang, Xiao Tang, Xiang Chen, Xiaoping Gao. Examining and monitoring paretic muscle changes during stroke rehabilitation using surface electromyography: A pilot study. Mathematical Biosciences and Engineering. 2019; 17 (1):216-234.

Chicago/Turabian Style

Ge Zhu; Xu Zhang; Xiao Tang; Xiang Chen; Xiaoping Gao. 2019. "Examining and monitoring paretic muscle changes during stroke rehabilitation using surface electromyography: A pilot study." Mathematical Biosciences and Engineering 17, no. 1: 216-234.

Journal article
Published: 20 November 2018 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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The progressive FastICA peel-off (PFP) is a recently developed blind source separation approach for high-density surface EMG decomposition. This study explores a novel application of PFP for automatic decomposition of multi-channel intramuscular electromyogram signals. The automatic PFP (APFP) was used to decompose an open access multichannel intramuscular EMG dataset, simultaneously collected from the brachioradialis muscle using 6 to 8 fine wire or needle electrodes. Given usually limited number of intramuscular electrodes compared with high-density surface EMG recording, a modification was made to the original APFP framework to dramatically increase the decomposition yield. A total of 131 motor units were automatically decomposed by the APFP framework from 10 multichannel intramuscular EMG signals, among which 128 motor units were also manually identified from the expert interactive EMGLAB decomposition. The average matching rate of discharge instants for all the common motor units was (98.71±1.73) %. The outcomes of this study indicate that the APFP framework can also be used to automatically decompose multichannel intramuscular EMG with high accuracies, even though the number of recording channels is relatively small compared with high-density surface EMG.

ACS Style

Maoqi Chen; Xu Zhang; Ping Zhou. Automatic Multichannel Intramuscular Electromyogram Decomposition: Progressive FastICA Peel-Off and Performance Validation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2018, 27, 76 -84.

AMA Style

Maoqi Chen, Xu Zhang, Ping Zhou. Automatic Multichannel Intramuscular Electromyogram Decomposition: Progressive FastICA Peel-Off and Performance Validation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2018; 27 (1):76-84.

Chicago/Turabian Style

Maoqi Chen; Xu Zhang; Ping Zhou. 2018. "Automatic Multichannel Intramuscular Electromyogram Decomposition: Progressive FastICA Peel-Off and Performance Validation." IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, no. 1: 76-84.