Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: Assessment and Comparison

Fuzzy entropy (FuzEn) was introduced to alleviate limitations associated with sample entropy (SampEn) in the analysis of physiological signals. Over the past decade, FuzEn-based methods have been widely used in various real-world biomedical applications. Several fuzzy membership functions (MFs), including triangular, trapezoidal, Z-shaped, bell-shaped, Gaussian, constant-Gaussian, and exponential functions have been employed in FuzEn. However, these FuzEn-based metrics have not been systematically compared yet. Since the threshold value r used in FuzEn is not directly comparable across different MFs, we here propose to apply a defuzzication approach using a surrogate parameter called \u27center of gravity\u27 to re-enable a fair and direct comparison. To evaluate these MFs, we analyze several synthetic and three clinical datasets. FuzEn using the triangular, trapezoidal, and Z-shaped MFs may lead to undened entropy values for short signals, thus providing a very limited advantage over SampEn. When dealing with an equal value of the center of gravity, the Gaussian MF, as the fastest algorithm, results in the highest Hedges\u27 g effect size for long signals. Our results also indicate that the FuzEn based on exponential MF of order four better distinguishes short white, pink, and brown noises, and yields more signicant differences for the short real signals based on Hedges\u27 g effect size. The triangular, trapezoidal, and Z-shaped MFs are not recommended for short signals. We propose to use FuzEn with Gaussian and exponential MF of order four for characterization of short (around 50400 sample points) and long data (longer than 500 sample points), respectively. We expect FuzEn with Gaussian and exponential MF as well as the concept of defuzzication to play prominent roles in the irregularity analysis of biomedical signals. The MATLAB codes for the FuzEn with different MFs are available at https://github.com/HamedAzami/FuzzyEntropy_Matlab

Effect of age and gender on sEMG signals and force steadiness

Challenges encountered during daily activities are easily overcome by young adults but may be potential risk for falls and injuries among the elderly due to age-associated sensorimotor deficits. To mitigate these risks, early detection of neuromuscular changes is essential and it is important for these to be cost-efficient, non-invasive, high throughput and non-hazardous. Electromyogram (EMG) is a non-invasive recording of the muscle activity that uses inexpensive equipment and hence may be considered for this purpose. However, it is a gross non-specific signal and thus there is need for careful investigation to identify its suitability for studying age-associated changes to the muscles. This research has investigated non-invasive, superficially recorded EMG signals to identify the differences between young healthy adults (20-35 years) and older (60-80 years) subjects of both genders while they were performing isometric ankle plantar flexion and dorsiflexion. The study also studied age and gender differences in the maximal voluntary force, its steadiness, the time to reach steadiness and modulus of the force output prior to steadiness as measured at the foot plate during dorsi- and plantar-flexion. This study has experimentally demonstrated the significant increase in co-activation index around the ankle joint, decrease in the agonistic activity and increase in antagonistic activity in the major lower leg muscles due to ageing. Female participants were noted to have a higher co-activation index in comparison to the males of corresponding age group. From the analysis, it was observed that ageing causes an overall decline in muscle signal complexity affecting the whole muscle strength in both genders. Furthermore, it was also established that within the triceps surae muscle group, Soleus and the gastrocnemii showed varied effects of aging. Another key finding is the significant age and gender difference in the maximal force and its steadiness around the ankle joint during dorsiflexion. However, these differences are less significant during plantarflexion. Results of this study revealed that with age, there was an increase in the total modulus of the force used by the participant to stabilize the foot at a desired level of contraction, difference being more significant during dorsiflexion. This study highlights the age associated neuromuscular adaptations in plantarflexor and dorsiflexor muscles. This is reflected in the altered activity of agonistic and antagonistic muscles during isometric contractions, the reduction in the overall muscle signal complexity, and decreased strength and steadiness of the force exerted by the calf muscles. It has established gender differences in the reduction of the co-activation index and decreased force strength during ankle flexion movements

Normalised Mutual Information of High-Density Surface Electromyography during Muscle Fatigue

This study has developed a technique for identifying the presence of muscle fatigue based on the spatial changes of the normalised mutual information (NMI) between multiple high density surface electromyography (HD-sEMG) channels. Muscle fatigue in the tibialis anterior (TA) during isometric contractions at 40% and 80% maximum voluntary contraction levels was investigated in ten healthy participants (Age range: 21 to 35 years; Mean age = 26 years; Male = 4, Female = 6). HD-sEMG was used to record 64 channels of sEMG using a 16 by 4 electrode array placed over the TA. The NMI of each electrode with every other electrode was calculated to form an NMI distribution for each electrode. The total NMI for each electrode (the summation of the electrode's NMI distribution) highlighted regions of high dependence in the electrode array and was observed to increase as the muscle fatigued. To summarise this increase, a function, M(k), was defined and was found to be significantly affected by fatigue and not by contraction force. The technique discussed in this study has overcome issues regarding electrode placement and was used to investigate how the dependences between sEMG signals within the same muscle change spatially during fatigue

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Applications of normalised mutual information in high density surface electromyography

Normalised mutual information (NMI) is a measure derived from Shannon's entropy that has been used in a variety of fields to measure the similarity or dependence between random variables. In terms of biomedical signal processing, NMI has been used in electroencephalography to identify the functional connectivity between different regions of the brain by calculating the NMI between electrodes. Researchers have adopted this method for surface electromyography (sEMG) and have used NMI to find the functional connectivity between pairs of muscles. While these studies have been able to demonstrate that NMI can be used to measure the functional connectivity between muscles, there is little to no literature exploring other forms of sEMG signal analysis using NMI. Therefore, this research focussed on investigating alternative applications for the NMI of sEMG, the results and observations of which are discussed in this thesis.    During this research four applications for NMI were identified and investigated using high density sEMG (HD-sEMG). These applications were monitoring the progression of muscle fatigue, estimating the border between superficial muscles, locating the innervation zones (IZ) of a muscle, and identifying noisy electrodes. Initially, a method was developed to analyse HD-sEMG data using NMI, this method created NMI distributions which describe the similarity of each electrode with every other electrode. In order to summarise the NMI distributions, two additional methods were developed. The first method produced interaction maps which illustrate the number of electrodes that are similar to each electrode. The second method produced total NMI magnitude maps which show the similarity of each electrode with all the other electrodes through a sum of the NMI distributions. These methods were used to observe how muscle fatigue, IZs, noisy electrodes, and multiple muscle masses affected the NMI between electrodes. For each of the applications these observations were then used to determine whether the NMI was an appropriate measure. In each case changes in the NMI between electrodes were observed. For muscle fatigue the NMI was shown to significantly increase as the muscles fatigued, while the effect of contraction strength did not have a significant effect. This significant increase was observed in row wise electrode pairs, column wise electrode pairs, interaction maps, and total NMI magnitude maps. When an electrode array was positioned over an IZ changes in the NMI distribution shape were observed around the estimated location of the IZ. Similarly, when noise was introduced to the HD-sEMG recordings the NMI distributions of the noisy electrodes were significantly affected. And, placing the electrode array across two muscles showed that the NMI distributions of the electrodes from each muscle were distinctly dissimilar.   Based on these observations a method for identifying noisy electrodes was developed and tested using artificial data. This method was able to achieve an average accuracy above 90% for most scenarios. Another method was then developed that used the intersections between all NMI distributions to estimate the location of the border between the two muscles. It was able to achieve an average accuracy above 80% during strong contractions with 50% of the false positives being within 10mm of the target. All these results demonstrate that NMI has the potential to be used in a variety of applications outside of the functional connectivity when analysing sEMG signals. Additionally, these observations demonstrate how the NMI can change spatially over a muscle and that the value can change drastically depending on the inter-electrode distance and the electrode's position relative to the muscle

Neuro-musculoskeletal Models: A Tool to Study the Contribution of Muscle Dynamics to Biological Motor Control

Das Verständnis der Prinzipien, die menschlichen Bewegungen zugrunde liegen, ist die Basis für die Untersuchung der Entstehung gesunder Bewegungen und, was noch wichtiger ist, der Entstehung motorischer Störungen aufgrund neurodegenerativer Erkrankungen oder anderer pathologischer Zustände. Dieses Verständnis zu erlangen ist jedoch herausfordernd, da menschliche Bewegung das Ergebnis eines komplexen, dynamischen Zusammenspiels von biochemischen und biophysikalischen Prozessen im Bewegungsapparat und den hierarchisch organisierten neuronalen Kontrollstrukturen ist. Um die Wechselwirkungen dieser Strukturen zu untersuchen, bieten Computersimulationen, die mathematische Modelle des muskuloskelettalen Systems mit Modellen seiner neuronalen Kontrolle kombinieren, ein nützliches Werkzeug. In diesen Simulationen können einzelne Prozesse oder ganze Funktionseinheiten deaktiviert oder gestört werden, um die Auswirkungen dieser Veränderungen auf die vorhergesagten Bewegungen zu untersuchen. Die Plausibilität der zugrundeliegenden Modelle kann durch den Vergleich der Simulationen mit Daten aus Humanexperimenten und biologisch inspirierten Robotermodellen beurteilt werden. Das Ziel dieser Arbeit war es, neuro-muskuloskelettale Modelle als Hilfsmittel zur Untersuchung von Konzepten der biologischen Bewegungskontrolle zu verwenden. Von besonderem Interesse war der Beitrag der Muskeldynamik zur Kontrolle, d.h. wie die intrinsischen muskuloskelettalen Eigenschaften die motorische Kontrolle vereinfachen, ohne die motorische Genauigkeit zu beeinträchtigen. Zusätzlich wurde der Einfluss propriozeptiver Reflexmechanismen in verschiedenen Szenarien getestet. Die verwendeten neuro-muskuloskelettalen Modelle sind eine Kombination von Mehrkörpermodellen der Muskel-Skelett-Struktur des Armes oder des ganzen Körpers mit einem biologisch inspirierten hybriden Gleichgewichtspunkt-Kontrollmodell. In einer Simulationsstudie stellten wir fest, dass unser Armmodell realistische Reaktionen auf externe mechanische Störungen für zielgerichtete Bewegungen mit einem Freiheitsgrad vorhersagt. Auf dieser Grundlage simulierten wir die Anwendung von tragbaren Assistenzgeräten zur Kompensation unerwünschter Hypermetrie, d.h. einer überschießenden Reaktion bei zielgerichteten Bewegungen im Zusammenhang mit zerebellärer Ataxie und anderen neurodegenerativen Erkrankungen. Wir fanden heraus, dass einfache mechanische Hilfsmittel ausreichend sein können, um die Hypermetrien auf ein normales Niveau zu reduzieren. Wir stellten jedoch auch fest, dass die Größe des Drehmoments und der Kraft, die zur Kompensation der Störung erforderlich sind, möglicherweise deutlich unterschätzt wird, wenn die Muskel-Sehnen-Eigenschaften im Modell nicht berücksichtigt werden. Die Ergebnisse dieser beiden Studien bestätigten die Hypothese aus der Literatur, dass die Morphologie des Muskel-Skelett-Systems signifikant zur Bewegung beiträgt und somit deren Kontrolle vereinfacht. Deshalb haben wir einen informationstheoretischen Ansatz verwendet, um diesen Beitrag für zielgerichtete und oszillatorische Armbewegungen mit zwei Freiheitsgraden zu charakterisieren. Die Ergebnisse bestätigten, dass die unteren Kontrollebenen, einschließlich der Muskeln und ihrer Aktivierungsdynamik, wichtige Beiträge zur gesamten Kontrollhierarchie leisten. Beispielsweise führt ein einfaches, stückweise konstantes Muskelstimulationssignal, das nur wenig Information enthält, zu einer geschmeidigen Bewegung. Der physiologische Detailgrad, der in unseren Muskel-Skelett-Modellen enthalten ist, ermöglicht nicht nur die Untersuchung von Theorien zur motorischen Kontrolle, sondern auch die Untersuchung von Größen wie inneren Kräften in Muskeln und Gelenken, die experimentell normalerweise nicht zugänglich sind. Diese Größen sind zum Beispiel in der Ergonomie und für die Entwicklung von Assistenzgeräten von Bedeutung. In einer Ganzkörpersimulationsstudie untersuchten wir den Beitrag des Dehnungsreflexes zu den resultierenden Muskelkräften bei einer aktiven externen Repositionierung des Hüftgelenkes für einen großen Bereich von Bewegungsgeschwindigkeiten. Wir fanden heraus, dass der relative Kraftbeitrag des Feedback-Mechanismus vom modellierten kognitiven Zustand abhängig ist und einen nicht vernachlässigbaren Beitrag leistet, insbesondere bei hohen Repositionsgeschwindigkeiten. Die Gesamtheit unserer Ergebnisse zeigt, dass die Eigenschaften des Bewegungsapparates signifikant zur Erzeugung und Kontrolle von Bewegung beitragen und es daher wichtig ist, sie bei der Modellierung der menschlichen Bewegung zu berücksichtigen. Daher sprechen die Ergebnisse für die Kombination eines physiologisch fundierten biomechanischen und biochemischen Modells des Bewegungsapparates mit biologisch inspirierten Konzepten der motorischen Kontrolle. Diese Computersimulationen haben sich als ein nützliches Werkzeug zum Verständnis der Prozesse erwiesen, die der Erzeugung gesunder und pathologisch beeinträchtigter menschlicher Bewegungen zugrunde liegen.Understanding the principles underlying human movement is the basis for investigating the generation of healthy movements and, more importantly, the origins of motor disorders due to neurodegenerative diseases or other pathological conditions. However, gaining this understanding is challenging since human motion is the result of a complex, dynamic interplay of biochemical and biophysical processes in the musculoskeletal system and the hierarchically organized neuronal control structures. To study the interactions of these structures, computer simulations that combine mathematical models of the musculoskeletal system with models of its neuronal control provide a useful tool. In these simulations, single processes or whole functional units can be disabled or perturbed to study the effects of these changes on the predicted movements. The plausibility of the underlying models can be assessed by comparing the simulations with data from human experiments and biologically inspired robotic models. The purpose of this work was to use neuro-musculoskeletal models as tools to study concepts of biological motor control. Of particular interest was the contribution of muscle dynamics to the control, i.e. how the intrinsic musculoskeletal properties simplify motor control without compromising motor accuracy. Additionally, the influence of proprioceptive reflex mechanisms was tested in different scenarios. The neuro-musculoskeletal models that were used are a combination of multibody musculoskeletal models of the arm or the whole body with a biologically inspired hybrid equilibrium-point controller. In a simulation study, we found that our arm model predicts realistic reactions to external mechanical perturbations while performing one-degree-of-freedom goal-directed movements. Based on this, we simulated the application of wearable assistive devices to compensate for unwanted hypermetria, i.e. an overshooting response in goal-directed movements associated with cerebellar ataxia and other neurodegenerative disorders. We found that simple mechanical devices may be sufficient to reduce the hypermetria to a normal level. However, we also observed that the magnitude of torque and power that is required to compensate for the disorder may be significantly underestimated if muscle-tendon characteristics are not considered in the computational model. The results of these two studies confirmed the hypothesis from literature that the morphology of musculoskeletal systems significantly contributes to the movement and thus simplifies its control. Therefore, we made use of the information-theoretic approach of quantifying morphological computation to characterize this contribution for goal-directed and oscillatory arm movements with two degrees of freedom. The results asserted that the lower levels of control, including the muscles and their activation dynamics, make important contributions to the overall control hierarchy. For example, a simple piecewise constant muscle stimulation signal that contains only little information results in a smooth movement. The level of physiological detail that is included in our musculoskeletal models does not only allow for the examination of motor control theories but also makes it possible to study quantities like internal forces in muscles and joints, usually not experimentally accessible. These quantities are relevant, for example, in ergonomics and for the development of assistive devices. In a whole-body simulation study, we investigated the contribution of the stretch reflex to the resulting muscle forces during active external repositioning of the hip joint for a large range of movement velocities. We found that, depending on the modeled cognitive state, the relative force contribution of the feedback mechanism is not negligible, especially for high repositioning velocities. The entirety of our results shows that the properties of the musculoskeletal system significantly contribute to the generation and control of movement and, thus, it is important to take them into account when modeling human movement. Therefore, the results advocate the combination of a physiologically well-founded biomechanical and biochemical model of the musculoskeletal system with biologically inspired concepts of motor control. These computer simulations have proven to be a useful tool towards the comprehension of the processes underlying the generation of healthy and pathologically impaired human movements

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Advances in Clinical Neurophysiology

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests

Nineteenth Annual Conference on Manual Control

No abstract availabl