Correlation based analysis of sEMG signals during complex muscle activity. Feasibility study of new methodology.

Assessment of complex motor task (CMT) competency is still very prone to bias. Objective assessment is based either on outcomes leaving the process out of the equitation or on checklists with all their limitations. We tested the hypothesis that muscular recruitment patterns assessed with surface Electromyography (sEMG) will be different between novices and skilled trainees. sEMG signals of the muscles that potentially are characterized by the highest level of engagement at complex motor task were submitted to comprehensive correlation analysis. Standard methods of estimating the correlation coefficients were compared with more advanced analysis including cross-wavelet coherence and calculation of mutual information. We conclude that with appropriate analytical tools it is possible to compare sEMG signals during complex motor tasks and that at least on our very small sample it differs between individuals.

[1]  James R Korndorffer,et al.  Simulator training for laparoscopic suturing using performance goals translates to the operating room. , 2005, Journal of the American College of Surgeons.

[2]  Huiru Zheng,et al.  Mutual information-based approach to the analysis of dynamic electrocardiograms. , 2008, Technology and health care : official journal of the European Society for Engineering and Medicine.

[3]  Johann Issartel,et al.  A Practical Guide to Time—Frequency Analysis in the Study of Human Motor Behavior: The Contribution of Wavelet Transform , 2006, Journal of motor behavior.

[4]  John L. Semmlow,et al.  Biosignal and Medical Image Processing , 2004 .

[5]  K. Moorthy,et al.  Laparoscopic skills training and assessment , 2004, The British journal of surgery.

[6]  S Abboud,et al.  The use of cross-correlation function for the alignment of ECG waveforms and rejection of extrasystoles. , 1984, Computers and biomedical research, an international journal.

[7]  L. Bartolomeo,et al.  Biomechanical analysis of induced mental stress in laparoscopy surgical training by surface Electromyography , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[8]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[9]  Paul G Gauger,et al.  Laparoscopic Skills Are Improved With LapMentor™ Training: Results of a Randomized, Double-Blinded Study , 2006, Annals of surgery.

[10]  Parvati Dev,et al.  Comparison of training on two laparoscopic simulators and assessment of skills transfer to surgical performance. , 2005, Journal of the American College of Surgeons.

[11]  Pere Caminal,et al.  Auto-Mutual Information Function for Predicting Pain Responses in EEG Signals during Sedation , 2014 .

[12]  O D Creutzfeldt,et al.  Crosscorrelation between the activity of septal units and hippocampal EEG during arousal. , 1974, Brain research.

[13]  A Stefanovska,et al.  Oscillatory dynamics of vasoconstriction and vasodilation identified by time-localized phase coherence , 2011, Physics in medicine and biology.

[14]  T. Wren,et al.  Cross-correlation as a method for comparing dynamic electromyography signals during gait. , 2006, Journal of biomechanics.

[15]  P. McClintock,et al.  Testing for time-localized coherence in bivariate data. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Aslak Grinsted,et al.  Nonlinear Processes in Geophysics Application of the Cross Wavelet Transform and Wavelet Coherence to Geophysical Time Series , 2022 .

[17]  D. Oudit,et al.  The pain of surgery: pain experienced by surgeons while operating. , 2010, International journal of surgery.

[18]  Roberto Merletti,et al.  Electromyography. Physiology, engineering and non invasive applications , 2005 .

[19]  Jonathan P. Braman,et al.  Musculoskeletal pain in resident orthopaedic surgeons: results of a novel survey. , 2014, The Iowa orthopaedic journal.

[20]  J. Gore,et al.  Mutual information analysis of the EEG in patients with Alzheimer's disease , 2001, Clinical Neurophysiology.

[21]  D. Oleynikov,et al.  Robotic surgery and training: electromyographic correlates of robotic laparoscopic training , 2005, Surgical Endoscopy And Other Interventional Techniques.

[22]  Roberto Merletti,et al.  Atlas of Muscle Innervation Zones , 2012 .

[23]  R. Renner,et al.  Axiomatic Relation between Thermodynamic and Information-Theoretic Entropies. , 2015, Physical review letters.

[24]  Johann Issartel,et al.  The relevance of the cross-wavelet transform in the analysis of human interaction – a tutorial , 2015, Front. Psychol..

[25]  Paulina Trybek,et al.  Evaluation of the training objectives with surface electromyography , 2016, Bio Algorithms Med Syst..

[26]  David Rudrauf,et al.  Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence , 2002, Neurophysiologie Clinique/Clinical Neurophysiology.

[27]  Leanne M Williams,et al.  Sex differences in functional connectivity in first-episode and chronic schizophrenia patients. , 2004, The American journal of psychiatry.

[28]  M. Vallverdú,et al.  Mutual information measures applied to EEG signals for sleepiness characterization. , 2015, Medical engineering & physics.

[29]  Ladislav Kristoufek,et al.  What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis , 2014, PloS one.