PMID- 29020054
OWN - NLM
STAT- MEDLINE
DCOM- 20171030
LR  - 20181113
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 10
DP  - 2017
TI  - A novel approach for automatic visualization and activation detection of evoked 
      potentials induced by epidural spinal cord stimulation in individuals with spinal 
      cord injury.
PG  - e0185582
LID - 10.1371/journal.pone.0185582 [doi]
LID - e0185582
AB  - Voluntary movements and the standing of spinal cord injured patients have been 
      facilitated using lumbosacral spinal cord epidural stimulation (scES). 
      Identifying the appropriate stimulation parameters (intensity, frequency and 
      anode/cathode assignment) is an arduous task and requires extensive mapping of 
      the spinal cord using evoked potentials. Effective visualization and detection of 
      muscle evoked potentials induced by scES from the recorded electromyography (EMG) 
      signals is critical to identify the optimal configurations and the effects of 
      specific scES parameters on muscle activation. The purpose of this work was to 
      develop a novel approach to automatically detect the occurrence of evoked 
      potentials, quantify the attributes of the signal and visualize the effects 
      across a high number of scES parameters. This new method is designed to automate 
      the current process for performing this task, which has been accomplished 
      manually by data analysts through observation of raw EMG signals, a process that 
      is laborious and time-consuming as well as prone to human errors. The proposed 
      method provides a fast and accurate five-step algorithms framework for activation 
      detection and visualization of the results including: conversion of the EMG 
      signal into its 2-D representation by overlaying the located signal building 
      blocks; de-noising the 2-D image by applying the Generalized Gaussian Markov 
      Random Field technique; detection of the occurrence of evoked potentials using a 
      statistically optimal decision method through the comparison of the probability 
      density functions of each segment to the background noise utilizing 
      log-likelihood ratio; feature extraction of detected motor units such as 
      peak-to-peak amplitude, latency, integrated EMG and Min-max time intervals; and 
      finally visualization of the outputs as Colormap images. In comparing the 
      automatic method vs. manual detection on 700 EMG signals from five individuals, 
      the new approach decreased the processing time from several hours to less than 15 
      seconds for each set of data, and demonstrated an average accuracy of 98.28% 
      based on the combined false positive and false negative error rates. The 
      sensitivity of this method to the signal-to-noise ratio (SNR) was tested using 
      simulated EMG signals and compared to two existing methods, where the novel 
      technique showed much lower sensitivity to the SNR.
FAU - Mesbah, Samineh
AU  - Mesbah S
AD  - Department of Electrical and Computer Engineering, University of Louisville, 
      Louisville, KY, United States of America.
AD  - Department of Bioengineering, University of Louisville, Louisville, KY, United 
      States of America.
AD  - Frazier Rehab Institute, Kentucky One Health, Louisville, KY, United States of 
      America.
FAU - Angeli, Claudia A
AU  - Angeli CA
AD  - Frazier Rehab Institute, Kentucky One Health, Louisville, KY, United States of 
      America.
AD  - Department of Neurological Surgery, University of Louisville, Louisville, KY, 
      United States of America.
AD  - Kentucky Spinal Cord Injury Research Center, University of Louisville, 
      Louisville, KY, United States of America.
FAU - Keynton, Robert S
AU  - Keynton RS
AD  - Department of Bioengineering, University of Louisville, Louisville, KY, United 
      States of America.
FAU - El-Baz, Ayman
AU  - El-Baz A
AUID- ORCID: 0000-0001-7264-1323
AD  - Department of Bioengineering, University of Louisville, Louisville, KY, United 
      States of America.
FAU - Harkema, Susan J
AU  - Harkema SJ
AD  - Frazier Rehab Institute, Kentucky One Health, Louisville, KY, United States of 
      America.
AD  - Department of Neurological Surgery, University of Louisville, Louisville, KY, 
      United States of America.
AD  - Kentucky Spinal Cord Injury Research Center, University of Louisville, 
      Louisville, KY, United States of America.
LA  - eng
PT  - Clinical Trial
PT  - Journal Article
DEP - 20171011
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Adult
MH  - Algorithms
MH  - Artifacts
MH  - Automation
MH  - Electric Stimulation
MH  - *Electric Stimulation Therapy
MH  - Electromyography/*methods
MH  - Epidural Space/*physiopathology
MH  - Evoked Potentials/*physiology
MH  - Humans
MH  - Imaging, Three-Dimensional
MH  - Male
MH  - Signal Processing, Computer-Assisted
MH  - Signal-To-Noise Ratio
MH  - Spinal Cord Injuries/*physiopathology/*therapy
MH  - Time Factors
PMC - PMC5636093
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2017/10/12 06:00
MHDA- 2017/10/31 06:00
PMCR- 2017/10/11
CRDT- 2017/10/12 06:00
PHST- 2016/12/15 00:00 [received]
PHST- 2017/09/16 00:00 [accepted]
PHST- 2017/10/12 06:00 [entrez]
PHST- 2017/10/12 06:00 [pubmed]
PHST- 2017/10/31 06:00 [medline]
PHST- 2017/10/11 00:00 [pmc-release]
AID - PONE-D-16-49649 [pii]
AID - 10.1371/journal.pone.0185582 [doi]
PST - epublish
SO  - PLoS One. 2017 Oct 11;12(10):e0185582. doi: 10.1371/journal.pone.0185582. 
      eCollection 2017.