Correlation of respiratory activity of contralateral diaphragm muscles for evaluation of recovery following hemiparesis.

Douglas E. Dow, Wen Zhi Zhan, Gary C. Sieck, Carlos B. Mantilla

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Respiration is impaired by disruption of the central drive for inspiration to the diaphragm muscle (DIAm). Some function may recover involving nerve regeneration, reinnervation or neuroplasticity. A research animal model involves inducing hemiparesis of the DIAm and monitoring any recovery under different conditions. Methods to accurately track the level of functional recovery are needed. In this study, an algorithm was developed and tested to quantify the relative amount of electromyogram (EMG) activity that temporally correlated for an experimental (EXP) hemi-DIAm with its intact contralateral hemi-DIAm. An average rectified value (ARV) trace was calculated. A template was formed of the ARV trace of the intact hemi-DIAm, with higher positive values corresponding with periods of inspirations and lower negative values corresponding with quiet periods. This template was multiplied by the EXP ARV trace to reward (more positive) periods of correlating activity, and punish (more negative) periods of high activity on the EXP side that corresponded with quiet periods on the intact side. The average integrated value was the index of correlating contralateral activity (I(CCA)). A negative I(CCA) value indicated no net correlation of activity, and a positive value indicated a net correlation of activity. The algorithm was tested on rats having the conditions of control or hemi-paresis induced by denervatation (DNV), tetrodotoxin administration (TTX) or cervical spinal hemi-section (SH). Control had high positive I(CCA) values, and DNV had negative values. TTX maintained negative I(CCA) values at 3, 7 and 14 days, indicating a lack of functional recovery. SH maintained negative values at 3 and 7 days, but a subset had positive values at 14 days indicating some functional recovery.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics


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