Study of the electromyographic signal dynamic behavior in Amyotrophic Lateral Sclerosis (ALS)

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neurons degeneration, which reduces muscular force, being very difficult to diagnose. Mathematical methods are used in order to analyze the surface electromiographic signal’s dynamic behavior (Fractal Dimension (FD) and Multiscale Entropy (MSE)), evaluate different muscle group’s synchronization (Coherence and Phase Locking Factor (PLF)) and to evaluate the signal’s complexity (Lempel-Ziv (LZ) techniques and Detrended Fluctuation Analysis (DFA)). Surface electromiographic signal acquisitions were performed in upper limb muscles, being the analysis executed for instants of contraction for ipsilateral acquisitions for patients and control groups. Results from LZ, DFA and MSE analysis present capability to distinguish between the patient group and the control group, whereas coherence, PLF and FD algorithms present results very similar for both groups. LZ, DFA and MSE algorithms appear then to be a good measure of corticospinal pathways integrity. A classification algorithm was applied to the results in combination with extracted features from the surface electromiographic signal, with an accuracy percentage higher than 70% for 118 combinations for at least one classifier. The classification results demonstrate capability to distinguish members between patients and control groups. These results can demonstrate a major importance in the disease diagnose, once surface electromyography (sEMG) may be used as an auxiliary diagnose method

Non-invasive electrophysiological assessment of the corticospinal tract in health and disease

PhD ThesisTo date, no candidate markers of upper motor neuron (UMN) function have performed sufficiently well to enter widespread clinical use, and the lack of such markers impedes both the diagnostic process and clinical trials in motor neuron disease (MND). We studied 15-30Hz intermuscular coherence (IMC), a novel marker of UMN function, and central motor conduction time (CMCT), an established marker of UMN function based on transcranial magnetic stimulation (TMS), in healthy volunteers and patients newly diagnosed with MND. To clarify the relative contributions of different parts of the motor system to IMC generation, we examined IMC in patients with longstanding diagnoses of hereditary spastic paraparesis (HSP), multifocal motor neuropathy (MMN) and inclusion body myositis (IBM). Previous studies reported conflicting results for the relationship between CMCT and predictors such as age and height. We only found a significant correlation between lower limb CMCT and height. IMC did not vary significantly with age, allowing data from healthy subjects across all ages to be pooled into a single normative dataset. The variability of IMC between subjects was considerable, and within a given subject variability was greater between than within recording sessions; potential contributors are discussed. Anodal transcranial direct current stimulation (tDCS) caused a significant increase in IMC, but interindividual variability was substantial, which might hinder its future use as an adjunct to IMC. To compare individual disease groups to the normal cohort, we evaluated the area under the receiver-operating characteristic curve (AUC). IMC generally matched or exceeded the performance of CMCT in discriminating patients with MND from normal, achieving AUCs of 0.83 in the upper and 0.79 in the lower limb. Previous evidence suggests that IMC abnormalities are primarily attributable to corticospinal tract (CST) dysfunction. In line with this, most patients with HSP exhibited diminished IMC. However, patients with MMN also showed decreased IMC, suggesting either that subclinical CST involvement was present or that dysfunction of lower motor neurons (LMNs) may affect IMC; clarification through computational modelling is suggested. In iii IBM, IMC was generally increased, which might reflect that the altered motor unit discharge pattern makes synchronisation more readily detectable. IMC appears to be a promising marker of CST function. It remains to be clarified how strongly it is influenced by LMN lesions, and optimisation of methods should help to minimise the variability of results. Since IMC is non-invasive and can be measured using commonly available EMG equipment, wider dissemination should prove straightforward.Wellcome Trus

Towards Amyotrophic Lateral Sclerosis Interpretable Diagnosis Using Surface Electromyography

Amyotrophic Lateral Sclerosis (ALS) is a fast-progressing disease with no cure. It is diagnosed through the assessment of clinical exams, such as needle electromyography, which measures themuscles’ electrical activity by inserting a needle into themuscle tissue. Nevertheless, surface electromyography (SEMG) is emerging as a more practical and less painful alternative. Even though these exams provide relevant information regarding the electric structures conducted in the muscles, ALS symptoms are similar to those of other neurological disorders, preventing a faster detection of the disease. This dissertation focuses on implementing and analyzing innovative SEMG features related to the morphology of the functional structures present in the signal. To assess the efficiency of these features, a framework is proposed, aiming to distinguish healthy from pathological signals through the use of a classification algorithm. The classification task was performed using SEMG signals acquired from the upper limb muscles of healthy and ALS subjects. The results show the utility of employing the proposed set of features for ALS diagnosis, with an F1 Score higher than 80% in most experimental conditions. The novel features improved the model’s overall performance when combined with other state-of-art SEMG features and also demonstrated efficiency when used individually. These outcomes are of significant importance in supporting the use of SEMG as a complementary diagnosis exam. The proposed features demonstrate promising contributions for better and faster detection of ALS and increased classification interpretabilityA Esclerose Lateral Amiotrófica (ELA) é uma doença incurável de progressão rápida. O seu diagnóstico é feito através da avaliação de exames clínicos como a eletromiografia de profundidade, que mede a atividade elétrica muscular com agulhas inseridas no músculo. No entanto, a eletromiografia de superfície (SEMG) surge como uma alternativa mais prática e menos dolorosa. Embora ambos os exames forneçam informações relevantes sobre as estruturas elétricas conduzidas nos músculos, os sintomas da ELA são semelhantes aos de outras doenças neurológicas, impedindo uma identificação mais precoce da doença. Esta dissertação foca-se na implementação e análise de atributos inovadores de SEMG relacionados com a morfologia das estruturas funcionais presentes no sinal. Para avaliar a eficiência destes atributos, é proposto um framework, com o objetivo de distinguir sinais saudáveis e sinais patológicos através de um algoritmo de classificação. A tarefa de classificação foi realizada utilizando sinais de SEMG adquiridos dos músculos dos membros superiores de indivíduos saudáveis e com ELA. Os resultados demonstram a utilidade do conjunto de atributos proposto para o diagnóstico de ELA, com uma métrica de classificação F1 superior a 80% na maioria das condições experimentais. Os novos atributos melhoraram o desempenho geral do modelo quando combinados com outros atributos de SEMG do estado da arte, e também se comprovaram eficientes quando aplicados individualmente. Estes resultados são de grande importância na justificação da aplicabilidade da SEMG como um exame complementar de diagnóstico da ELA. Os atributos apresentados demonstram ser promissores para um melhor e mais rápido diagnóstico, e facilitam a explicação dos resultados da classificação devido à sua interpretabilidade

Modulation of electrical stimulation applied to human physiology and clinical diagnostic

The use, manipulation and application of electrical currents, as a controlled interference mechanism in the human body system, is currently a strong source of motivation to researchers in areas such as clinical, sports, neuroscience, amongst others. In electrical stimulation (ES), the current applied to tissue is traditionally controlled concerning stimulation amplitude, frequency and pulse-width. The main drawbacks of the transcutaneous ES are the rapid fatigue induction and the high discomfort induced by the non-selective activation of nervous fibers. There are, however, electrophysiological parameters whose response, like the response to different stimulation waveforms, polarity or a personalized charge control, is still unknown. The study of the following questions is of great importance: What is the physiological effect of the electric pulse parametrization concerning charge, waveform and polarity? Does the effect change with the clinical condition of the subjects? The parametrization influence on muscle recruitment can retard fatigue onset? Can parametrization enable fiber selectivity, optimizing the motor fibers recruitment rather than the nervous fibers, reducing contraction discomfort? Current hardware solutions lack flexibility at the level of stimulation control and physiological response assessment. To answer these questions, a miniaturized, portable and wireless controlled device with ES functions and full integration with a generic biosignals acquisition platform has been created. Hardware was also developed to provide complete freedom for controlling the applied current with respect to the waveform, polarity, frequency, amplitude, pulse-width and duration. The impact of the methodologies developed is successfully applied and evaluated in the contexts of fundamental electrophysiology, psycho-motor rehabilitation and neuromuscular disorders diagnosis. This PhD project was carried out in the Physics Department of Faculty of Sciences and Technology (FCT-UNL), in straight collaboration with PLUX - Wireless Biosignals S.A. company and co-funded by the Foundation for Science and Technology.Fundação para a Ciência e Tecnologia (FCT); PLUX - Wireless Biosignals, S.A.; FCT-UNL- CEFITE

Neuroimaging of human motor control in real world scenarios: from lab to urban environment

The main goal of this research programme was to explore the neurophysiological correlates of human motor control in real-world scenarios and define mechanism-specific markers that could eventually be employed as targets of novel neurorehabilitation practice. As a result of recent developments in mobile technologies it is now possible to observe subjects' behaviour and monitor neurophysiological activity whilst they perform natural activities freely. Investigations in real-world scenarios would shed new light on mechanisms of human motor control previously not observed in laboratory settings and how they could be exploited to improve rehabilitative interventions for the neurologically impaired. This research programme was focussed on identifying cortical mechanisms involved in both upper- (i.e. reaching) and lower-limb (i.e. locomotion) motor control. Complementary results were obtained by the simultaneous recordings of kinematic, electromyographic and electrocorticographic signals. To study motor control of the upper-limb, a lab­based setup was developed, and the reaching movement of healthy young individuals was observed in both stable and unstable (i.e. external perturbation) situations. Robot-mediated force-field adaptation has the potential to be employed in rehabilitation practice to promote new skills learning and motor recovery. The muscular (i.e. intermuscular couplings) and neural (i.e. spontaneous oscillations and cortico­muscular couplings) indicators of the undergoing adaptation process were all symbolic of adaptive strategies employed during early stages of adaptation. The medial frontal, premotor and supplementary motor regions appeared to be the principal cortical regions promoting adaptive control and force modulation. To study locomotion control, a mobile setup was developed and daily life human activities (i.e. walking while conversing, walking while texting with a smartphone) were investigated outside the lab. Walking in hazardous environments or when simultaneously performing a secondary task has been demonstrated to be challenging for the neurologically impaired. Healthy young adults showed a reduced motor performance when walking in multitasking conditions, during which whole-brain and task-specific neural correlates were observed. Interestingly, the activity of the left posterior parietal cortex was predictive of the level of gait stability across individuals, suggesting a crucial role of this area in gait control and determination of subject specific motor capabilities. In summary, this research programme provided evidence on different cortical mechanisms operative during two specific scenarios for "real­world" motor behaviour in and outside the laboratory-setting in healthy subjects. The results suggested that identification of neuro-muscular indicators of specific motor control mechanisms could be exploited in future "real-world" rehabilitative practice

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Coherence and phase locking disruption in electromyograms of patients with amyotrophic lateral sclerosis

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaIn motor neuron disease, the aim of therapy is to prevent or slow neuronal degeneration and early diagnosis is thus essential. Hypothesising that beta-band (15-30 Hz) is a measure of pathways integrity as shown in literature, coherence and PLF could be used as an electrophysiological indicator of upper and lower neuron integrity in patients with ALS. Before further analysis, synthetic EMG signals were computed to verify the used algorithm. Coherence and PLF analyses were performed for instants of steady contraction from contra and ipsilateral acquisitions. Ipsilateral acquisitions were performed for one member of each group and results present significant differences between both groups. Contrarily, contralateral acquisitions were performed on 6 members of each group and results present no significant differences. PLF analysis was computed for ipsilateral acquisitions and, similarly to coherence, results show significant differences between both groups. PLF was also analysed for contralateral acquisitions, and results show no significant differences within groups, as expected since no coherence was found for the same acquisitions. So, while control subjects present no neuronal or muscular problems and therefore higher synchrony and coherence for beta-band frequencies, patients with ALS do not present synchronism or coherence in any frequency, specially for beta-band. All results allowed to conclude that contralateral coherence is not a good measure of corticospinal pathways integrity. However, ipsilateral acquisitions show promising results and it is possible to affirm that ipsilateral measurements may reflect neuronal degeneration. For future work is suggested a deeper analysis of PLF, that appear to have potential as a quantitative test of upper and lower neuron integrity related to ALS

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions