1. NON-LINEAR ANALYSIS OF SURFACE ELECTROMYOGRAPHY IN PARKINSON’S DISEASE Matthew Flood 1,2* 1 School of Electrical & Electronic Engineering, University College Dublin, Belfield, Dublin 4. Prof. Madeleine Lowery 1,2 2 Insight Centre for Data Analytics, O'Brien Centre for Science, Belfield, Dublin 4.

2. Parkinson’s Disease Bradykinetic gait Muscle tremor Muscle rigidity Akinesia

3. Parkinson’s Disease No biomarker Need to quantify disease severity Levodopa medication & deep brain stimulation Limited research on nonlinear analysis of EMG

4. Aim To distinguish Parkinsonian EMG from healthy controls using advanced signal analysis, enabling early diagnosis and biomarkers to assess therapeutic interventions.

5. Experimental Methods B. Upper leg musclesA. Experimental setup C. Surface EMG

6. Recurrence Quantification Analysis Embedding Dimension * Time Delay * Trajectory Radius * Line length https://code.google.com/p/girs/downloads/list Rössler System EMG

7. Recurrenc e Plot EMG EMG (mV) Time (sec) PD Patient %DET = 38.76% %DET = 0% Noise %DET = 3.545% Healthy Control Time (sec) Recurrence Quantification Analysis

8. Agonist-antagonist EMG Coherence Intermuscular Coherence

9. EMG Kurtosis and skew Kurto sis Skewn ess Negative Skew Positive & Negative Kurtosis Positive Skew

10. Results:Recurrence Quantification Analysis * p = 0.0475

11. Results:Agonist-antagonist EMG coherence * * ** Parkinson's DiseaseControls * p < 0.001 ** p = 0.014

12. Results:EMG Kurtosis and Skew * * p = 0.0457

13. Conclusio ns Determinism of EMG signal increases in Parkinson’s Disease, greater motor unit synchrony. Coherence higher in patients in θ, α, β, & γ frequency bands, greater shared common input across muscles. Kurtosis higher in Parkinson’s disease, greater signal structure. Pathological synchrony of motoneuron firing patterns in isometric muscle contractions in Parkinson’s disease.

14. Future Work Sensitivity analysis of recurrence quantification parameters to optimize for parkinsonian EMG. Identify relationship between coherence and other nonlinear measures (e.g. Sample Entropy, LZ Complexity). Use simulation modelling to identify mechanisms underlying difference in parkinsonian EMG. Apply analysis methods to quantify the efficacy of exercise intervention in PD patients.

15. The researchers would like to thank Prof. Bente Jensen and colleagues at the Dept. of Exercise and Sports Sciences at the University of Copenhagen, Denmark, for contributing their data. This project was funded under the SFI Insight Centre for Data Analytics. Acknowledge ments