title: Subspace based adaptive denoising of surface EMG from neurological injury patients.
abstract: OBJECTIVE
  After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings.
  
  
  METHODS
  The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals.
  
  
  RESULTS
  The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity.
  
  
  CONCLUSIONS
  The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.
authors.affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.forename: Dongwen
authors.initials: D
authors.lastname: Ying
authors.forename: William
authors.initials: W
authors.lastname: Zev Rymer
authors.forename: Ping
authors.initials: P
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013119:Spinal Cord Injuries / Q000503:physiopathology*