Flood, Matthew W.
Flood, Matthew W.
Flood, Matthew W.
Now showing 1 - 9 of 9
- PublicationAnalysis of Surface Electromyography in Parkinson's Disease Using Time Frequency and Recurrence Quantification MethodsThe work presented here aims to establish the optimal RQA variables for the calculation of RQA parameters (REC, DET, JRP, etc.), and to apply these parameters to EMG data of Parkinson’s disease patients. Additionally, the cross-correlation of these parameters with time frequency features will be assessed with the intention of classifying Parkinson’s patients from healthy controls.
- PublicationAnalysis of Parkinsonian Surface Electomyography Through Advanced Signal Processing and Nonlinear MethodsParkinson’s disease (PD) is a neurodegenerative disease that affects approx. 4% of people over 80 years of age . The result of depleted dopaminergic neurons in the substantia nigra, PD is characterised with symptoms such as muscle rigidity, bradykinetic gait, and severe tremor. To distinguish Parkinsonian electromyographic (EMG) signals from those of healthy controls, recent studies have employed nonlinear methods which can capture the underlying activity of the neuromuscular system. Recurrence quantification analysis (RQA) has been shown to effectively characterise the degree of repeated synchronous structure in non-linear dynamical systems including parkinsonian EMG, through parameters such as determinism (%DET) and recurrence rate (%REC) . Additional parameters such as intermuscular coherence and kurtosis have also been used to observe changes in EMG signals under various conditions [2,3]. To date, limited research has examined the potential to discern EMG of individuals with PD from healthy controls using RQA and intermuscular coherence. The work presented here aims to examine differences in Parkinsonian EMG from that of healthy controls using these measures.
- PublicationNon-Linear Analyses of Surface Electromyography in Parkinsons DiseaseNon-linear measures, such as recurrence quantification analysis, have been applied to electromyographic (EMG) data to capture the underlying activity of the neuromuscular system. The application of such approaches to EMG data from individuals with Parkinson’s disease (PD) is presented here. Preliminary results indicate differences in the level of determinism and coherence that distinguish Parkinsonian EMG from that of healthy age-matched controls.
- PublicationBeta-band Motor Unit Coherence and Nonlinear Surface EMG Features of the First Dorsal Interosseous Muscle Vary with ForceMotor unit (MU) firing times are weakly coupled across a range of frequencies during voluntary contractions. Coherent activity within the beta-band (15-35 Hz) has been linked to oscillatory cortical processes, providing evidence of functional connectivity between the motoneuron pool and motor cortex. The aim of this study was to investigate whether beta-band MU coherence is altered with increasing abduction force in the first dorsal interosseous muscle. Coherence between MU firing times, extracted from decomposed surface EMG signals, was investigated in 17 subjects at 10%, 20%, 30% and 40% of maximum voluntary contraction. Corresponding changes in nonlinear surface EMG features, specifically sample entropy and determinism which are sensitive to MU synchronization, were also examined. A reduction in beta-band and alpha-band coherence was observed as force increased (F(3, 151) = 32, p < .001 and F(3, 151) = 27, p < .001, respectively), accompanied by corresponding changes in nonlinear surface EMG features. A significant relationship between the nonlinear features and MU coherence was also detected (r = -0.43 ± 0.1 and r = 0.45 ± 0.1, for sample entropy and determinism, respectively, both p < .001). The reduction in beta-band coherence suggests a change in the relative contribution of correlated and uncorrelated pre-synaptic inputs to the motoneuron pool, and/or a decrease in the responsiveness of the motoneuron pool to synchronous inputs at higher forces. The study highlights the importance of considering muscle activation when investigating changes in MU coherence or nonlinear EMG features, and examines other factors that can influence coherence estimation.
374Scopus© Citations 10
- PublicationThe Influence of Force Level and Motor Unit Coherence on Nonlinear Surface EMG Features Examined Using Model SimulationNonlinear features extracted from surface EMG signals have been previously used to infer information on coherent or synchronous activity in the underlying motor unit discharges. However, it has not yet been assessed how these features are affected by the density of the surface EMG signal, and whether changes in the level of muscle activation can influence the effective detection of correlated motor unit firing. To examine this, a motoneuron pool model receiving a beta-band modulated cortical input was used to generate correlated motor unit firing trains. These firing trains were convolved with motor unit action potentials generated from an anatomically accurate electrophysiological model of the first dorsal interosseous muscle. The sample entropy (SampEn) and percentage determinism (%DET) of recurrence quantification analysis were calculated from the composite surface EMG signals, for signals comprised of both correlated and uncorrelated motor unit firing trains. The results show that although both SampEn and %DET are influenced by motor unit coherence, they are differentially affected by muscle activation and motor unit distribution. The results also suggest that sample entropy may provide a more accurate assessment of the underlying motor unit coherence than percentage determinism, as it is less sensitive to factors unrelated to motor unit synchrony.
335Scopus© Citations 2
- PublicationQuantitative clinical assessment of motor function during and following LSVT-BIG® therapyBackground LSVT-BIG® is an intensively delivered, amplitude-oriented exercise therapy reported to improve mobility in individuals with Parkinson’s disease (PD). However, questions remain surrounding the efficacy of LSVT-BIG® when compared with similar exercise therapies. Instrumented clinical tests using body-worn sensors can provide a means to objectively monitor patient progression with therapy by quantifying features of motor function, yet research exploring the feasibility of this approach has been limited to date. The aim of this study was to use accelerometer-instrumented clinical tests to quantify features of gait, balance and fine motor control in individuals with PD, in order to examine motor function during and following LSVT-BIG® therapy. Methods Twelve individuals with PD undergoing LSVT-BIG® therapy, eight non-exercising PD controls and 14 healthy controls were recruited to participate in the study. Functional mobility was examined using features derived from accelerometry recorded during five instrumented clinical tests: 10 m walk, Timed-Up-and-Go, Sit-to-Stand, quiet stance, and finger tapping. PD subjects undergoing therapy were assessed before, each week during, and up to 13 weeks following LSVT-BIG®. Results Accelerometry data captured significant improvements in 10 m walk and Timed-Up-and-Go times with LSVT-BIG® (p < 0.001), accompanied by increased stride length. Temporal features of the gait cycle were significantly lower following therapy, though no change was observed with measures of asymmetry or stride variance. The total number of Sit-to-Stand transitions significantly increased with LSVT-BIG® (p < 0.001), corresponding to a significant reduction of time spent in each phase of the Sit-to-Stand cycle. No change in measures related to postural or fine motor control was observed with LSVT-BIG®. PD subjects undergoing LSVT-BIG® showed significant improvements in 10 m walk (p < 0.001) and Timed-Up-and-Go times (p = 0.004) over a four-week period when compared to non-exercising PD controls, who showed no week-to-week improvement in any task examined. Conclusions This study demonstrates the potential for wearable sensors to objectively quantify changes in motor function in response to therapeutic exercise interventions in PD. The observed improvements in accelerometer-derived features provide support for instrumenting gait and sit-to-stand tasks, and demonstrate a rescaling of the speed-amplitude relationship during gait in PD following LSVT-BIG®.
15Scopus© Citations 12
- PublicationIncreased EMG intermuscular coherence and reduced signal complexity in Parkinson's diseaseObjectives: To investigate differences in surface electromyography (EMG) features in individuals with idiopathic Parkinson's disease (PD) and aged-matched controls. Methods: Surface EMG was recorded during isometric leg extension in PD patients prior to, and after undergoing a locomotor training programme, and in aged-matched controls. Differences in EMG structure were quantified using determinism (%DET), sample entropy (SampEn) and intermuscular coherence. Results: %DET was significantly higher, and SampEn significantly lower, in PD patients. Intermuscular coherence was also significantly higher in the PD group in theta, alpha and beta frequency bands. %DET increased and SampEn decreased with increasing Movement-Disorder-Society UPDRS scores, while theta band coherence was significantly correlated with total MDS-UPDRS scores and torque variance. Neither %DET, SampEn nor intermuscular coherence changed in response to training. Conclusions: The differences observed are consistent with increased synchrony among motor units within and across leg muscles in PD. Differences between EMG signals recorded from the PD and control groups persisted post-therapy, after improvements in walking capacity occurred. Significance: These results provide insight into changes in motoneuron activity in PD, demonstrate increased beta band intramuscular coherence in PD for the first time, and support the development of quantitative biomarkers for PD based on advanced surface EMG features.
331Scopus© Citations 24
368Scopus© Citations 2
- PublicationGait Event Detection from Accelerometry using the Teager-Kaiser Energy OperatorObjective: A novel method based on the application of the Teager-Kaiser Energy Operator is presented to estimate instances of initial contact (IC) and final contact (FC) from accelerometry during gait. The performance of the proposed method was evaluated against four existing gait event detection (GED) methods under three walking conditions designed to capture the variance of gait in real-world environments. Methods: A symmetric discrete approximation of the Teager-Kaiser energy operator was used to capture simultaneous amplitude and frequency modulations of the shank acceleration signal at IC and FC during flat treadmill walking, inclined treadmill walking, and flat indoor walking. Accuracy of estimated gait events were determined relative to gait events detected using force-sensitive resistors. The performance of the proposed algorithm was assessed against four established methods by comparing mean-absolute error, sensitivity, precision and F1-score values. Results: The proposed method demonstrated high accuracy for GED in all walking conditions, yielding higher F1-scores (IC: >0.98, FC: >0.9) and lower mean-absolute errors (IC: <0.018s, FC: <0.039s) than other methods examined. Estimated ICs from shank-based methods tended to exhibit unimodal distributions preceding the force-sensitive resistor estimated ICs, whereas estimated gait events for waist-based methods had quasi-uniform random distributions and lower accuracy. Conclusion: Compared to established gait event detection methods, the proposed method yielded comparably high accuracy for IC detection, and was more accurate than all other methods examined for FC detection. Significance: The results support the use of the Teager-Kaiser Energy Operator for accurate automated GED across a range of walking conditions.
742Scopus© Citations 17