Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles

Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles

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

Multifractality, Interactivity, and the Adaptive Capacity of the Human Movement System: A Perspective for Advancing the Conceptual Basis of Neurologic Physical Therapy

Background and Purpose: Physical therapists seek to optimize movement as a means of reducing disability and improving health. The short-term effects of interventions designed to optimize movement ultimately are intended to be adapted for use across various future patterns of behavior, in potentially unpredictable ways, with varying frequency, and in the context of multiple tasks and environmental conditions. In this perspective article, we review and discuss the implications of recent evidence that optimal movement variability, which previously had been associated with adaptable motor behavior, contains a specific complex nonlinear feature known as “multifractality.” Summary of Key Points: Multifractal movement fluctuation patterns reflect robust physiologic interactivity occurring within the movement system across multiple time scales. Such patterns provide conceptual support for the idea that patterns of motor behavior occurring in the moment are inextricably linked in complex, physiologic ways to patterns of motor behavior occurring over much longer periods. The human movement system appears to be particularly tuned to multifractal fluctuation patterns and exhibits the ability to reorganize its output in response to external stimulation embedded with multifractal features. Recommendations for Clinical Practice: As a fundamental feature of human movement, multifractality opens new avenues for conceptualizing the link between physiologic interactivity and adaptive capacity. Preliminary evidence supporting the positive influence of multifractal rhythmic auditory stimulation on the gait patterns of individuals with Parkinson disease is used to illustrate how physical therapy interventions might be devised to specifically target the adaptive capacity of the human movement system. Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, https://links.lww.com/JNPT/A183)

The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy

Fractal features of surface electromyogram: a new measure for low level muscle activation

Identifying finger and wrist flexion based actions using single channel surface electromyogram have a number of rehabilitation, defence and human computer interface applications. These applications are currently infeasible because of unreliability in classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during maintained wrist and finger flexion. It has been established in literature that surface electromyogram (sEMG) and other such biosignals are fractal signals. Some researchers have determined that fractal dimension (FD) is related to strength of muscle contraction. On careful analysis of fractal properties of sEMG, this research work has established that FD is related to the muscle size and complexity and not to the strength of muscle contraction. The work has also identified a novel feature, maximum fractal length (MFL) of the signal, as a good measure of strength of contraction of the muscle. From the analysis, it is observed that while at high level of contraction, root mean square (RMS) is an indicator of strength of contraction of the muscle, this relationship is not very strong when the muscle contraction is less than 50% maximum voluntary contraction. This work has established that MFL is a more reliable measure of strength of contraction compared to RMS, especially at low levels of contraction. This research work reports the use of fractal properties of sEMG to identify the small changes in strength of muscle contraction and the location of the active muscles. It is observed that fractal dimension (FD) of the signal is related with the properties of the muscle while maximum fractal length (MFL) is related to the strength of contraction of the associated muscle. The results show that classifying MFL and FD of a single channel sEMG from the forearm it is possible to accurately identify a set of finger and wrist flexion based actions even when the muscle activity is very weak. It is proposed that such a system could be used to control a prosthetic hand or for human computer interface

Study on the non-linear metrics contribution to estimate atrial fibrillation organization from the surface electrocardiogram

[EN] Atrial fibrillation (AF) is the most frequently diagnosed arrhythmia, characterized by an uncoordinated atrial electrical activation, thus causing the atria to be unable to pump blood effectively. The prevalence of AF is expected to increase significantly in the next decades as the population ages. However, both the knowledge and the treatment of this arrhythmia still have to experiment a significant progress. Previous studies have reported that AF organization, which can be defined as the repetitiveness degree of the atrial activity pattern, correlates with the arrhythmia status as well as with the therapy outcome. Thus, estimating AF organization from surface electrocardiographic (ECG) recordings constitutes a very interesting approach because ECG recordings are easy and cheap to obtain. The objective of this doctoral thesis is to assess the use of a variety of nonlinear indices in the estimation of AF organization from single-lead noninvasive ECG recordings. Apart from the most common noninvasive AF organization estimators, such as Sample Entropy (SampEn) and the dominant atrial frequency (DAF), the following nonlinear indices have been studied: Fuzzy Entropy, Spectral Entropy, Lempel-Ziv Complexity and Hurst Exponents. Moreover, since the presence of noise and ventricular residuals affects the performance of nonlinear methods, the application of a strategy aimed at reducing these nuisances has been evaluated. Therefore, the application of these metrics over the atrial activity fundamental waveform, named the main atrial wave (MAW), has been proposed. In this doctoral thesis, the following scenarios involving AF organization have been considered: the prediction of paroxysmal AF spontaneous termination, the study of the earlier signs anticipating AF termination and the classification between paroxysmal and persistent AF from short ECG recordings. Firstly, the performance of the studied metrics discriminating events related to AF organization was tested making use of a reference database aimed at predicting AF spontaneous termination. In this study, most of the proposed indices provided higher accuracy than traditional AF organization estimators. Accuracy values higher than 90% were obtained with several indices. In particular, the generalized Hurst exponents of order 1 and 2, H(1) and H(2), achieved outstanding results, thus being selected for later studies in this thesis. Furthermore, the computation of H(2) depends on two critical parameters, namely, the analyzed interval length (L) and the maximum search window for self-similarities (tau). Hence, a study with 660 combinations on these two parameters was performed, together with the sampling frequency (fs) of the recording, in order to obtain their optimal combination in computing AF organization. On the other hand, previous works analyzing the spontaneous termination of AF have been only focused on the last 2 minutes preceding the termination. In contrast, a different scenario considering longer recordings to detect the earlier signs anticipating paroxysmal AF termination has been analyzed for the first time in this thesis. H(2) was selected for the study because of its highest accuracy in AF termination prediction. Additionally, the DAF and SampEn were also computed as references. Through this study it has been corroborated that AF organization only varies significantly within the last 3 minutes before spontaneous termination. As a consequence, the early prediction of paroxysmal AF spontaneous termination does not seem feasible through the current signal analysis tools. Finally, H(2) was applied in the classification between paroxysmal and persistent AF from short ECG recordings, achieving a higher diagnostic accuracy than DAF and SampEn. This result suggests that the analysis of ambulatory ECG recordings through H(2) could be a future alternative to the use of Holter ECG recordings in the classification between paroxysmal and persistent AF.[ES] La fibrilación auricular (FA) es la arritmia más frecuente y se caracteriza por una actividad auricular descoordinada, que impide que las aurículas bombeen sangre de manera eficaz. Se espera que la prevalencia de la FA aumente significativamente en las próximas décadas debido al envejecimiento de la población. Sin embargo, tanto el conocimiento relativo a esta arritmia como su tratamiento son todavía mejorables. Estudios previos han relacionado la organización de la FA, que se puede definir como el grado de repetitividad de la actividad auricular, con el estado de la arritmia o su respuesta al tratamiento. Además, la estimación de la organización de la FA a partir de registros electrocardiográficos (ECG) de superficie resulta especialmente interesante porque su obtención es sencilla y barata. El objetivo de esta tesis doctoral es evaluar el uso de distintos índices no lineales para estimar la organización de la FA a partir del ECG. Además de los estimadores no invasivos de organización más comunes, como la entropía muestral (SampEn) y la frecuencia auricular dominante (DAF), se han estudiado los siguientes métodos no lineales: la entropía borrosa, la entropía espectral, la complejidad Lempel-Ziv y los exponentes de Hurst. Además, se ha estudiado el uso de una estrategia destinada a la reducción del ruido y los residuos de actividad ventricular para mejorar el desempeño de métodos no lineales. Así, los índices estudiados también se han aplicado sobre la forma de onda fundamental de la actividad auricular, conocida como la onda auricular principal (MAW). Se han considerado los siguientes escenarios relacionados con la organización de la FA: la predicción de la terminación espontánea de la FA paroxística, el estudio de los primeros indicios de terminación espontánea de la FA y la clasificación entre FA paroxística y FA persistente a partir de registros ECG de corta duración. Primero, se estudió la capacidad de los índices estudiados para distinguir eventos relacionados con la organización de la FA mediante el análisis de una base de datos de referencia para la predicción de su terminación espontánea. La mayoría de los índices propuestos consiguieron una mayor precisión que los estimadores tradicionales de organización. Así, varios de los índices obtuvieron una precisión superior al 90% en la predicción de la terminación espontánea de la FA. En particular, los exponentes de Hurst generalizados de orden 1 y 2, H(1) y H(2), lograron los mejores resultados de clasificación. Puesto que el cálculo de H(2) depende de dos parámetros críticos, la longitud del intervalo analizado (L) y el tamaño máximo de la ventana donde buscar similitudes (tau), se llevó a cabo un estudio con 660 combinaciones de esos dos parámetros junto con la frecuencia de muestreo (fs) del registro para determinar el uso óptimo de este índice. Por otra parte, los trabajos previos que han estudiado la terminación espontánea de la FA se han centrado en los últimos 2 minutos antes de la terminación. Por contra, en esta tesis doctoral se han estudiado por primera vez registros de mayor duración para detectar los primeros indicios de la terminación de la FA. Para ello, se eligió el uso de H(2) por su alta precisión en la predicción de la terminación de la FA. Además, la DAF y SampEn se calcularon como referencias. En este estudio se ha comprobado que la organización de la FA solamente presenta variaciones significativas en los últimos 3 minutos antes de su terminación espontánea. Por ello, la predicción temprana de la terminación no parece posible con los medios actuales de análisis de la señal. Por último, se aplicó H(2) para clasificar entre FA paroxística y FA persistente a partir de ECGs de corta duración, obteniendo una mayor precisión diagnóstica que la DAF y SampEn. Este resultado sugiere que el análisis de ECGs ambulatorios por medio de H(2) puede ser en el futuro una alte[CA] La fibril·lació auricular (FA) és l'arítmia més freqüent i es caracteritza per una activitat auricular descoordinada, que impedix que les aurícules bomben sang de manera eficaç. S'espera que la prevalença de la FA augmente significativament en les pròximes dècades a causa de l'envelliment de la població. No obstant això, tant el coneixement relatiu a esta arítmia com el seu tractament són encara millorables. Estudis previs han relacionat l'organització de la FA, que es pot definir com el grau de repetitivitat de l'activitat auricular, amb l'estat de l'arítmia o la seua resposta al tractament. A més, l'estimació de l'organització de la FA a partir de registres electrocardiogràfics (ECG) de superfície resulta especialment interessant perquè la seua obtenció és senzilla i barata. L'objectiu d'esta tesi doctoral és avaluar l'ús de distints índexs no lineals en l'estimació de l'organització de la FA a partir de l'ECG de superfície. A més dels estimadors no invasius d'organització més comuns, com l'entropia mostral (SampEn) i la freqüència auricular dominant (DAF), s'han estudiat els següents mètodes no lineals: l'entropia borrosa, l'entropia espectral, la complexitat Lempel-Ziv i els exponents de Hurst. A més, s'ha estudiat l'ús d'una estratègia destinada a la reducció del soroll i els residus d'activitat ventricular per a millorar la seua capacitat d'estimar l'organització. Així, doncs, els índexs estudiats també s'han aplicat sobre la forma d'onda fonamental de l'activitat auricular, coneguda com l'onda auricular principal (MAW). S'han considerat els següents escenaris relacionats amb l'organització de la FA: la predicció de la terminació espontània de la FA paroxística, l'estudi dels primers indicis de terminació espontània de la FA i la classificació entre FA paroxística i FA persistent a partir de registres ECG de curta duració. Primer, es va estudiar la capacitat dels índexs estudiats per a distingir esdeveniments relacionats amb l'organització de la FA per mitjà de l'anàlisi d'una base de dades de referència per a la predicció de la seua terminació espontània. La majoria dels índexs proposats van aconseguir una major precisió que els estimadors tradicionals d'organització de la FA. Així, alguns dels índexs van obtindre una precisió superior al 90% en la predicció de la terminació espontània de la FA. En particular, els exponents de Hurst generalitzats d'orde 1 i 2, H(1) i H(2), van aconseguir els millors resultats de classificació. Com el càlcul de H(2) depén de dos paràmetres crítics, la longitud de l'interval analitzat (L) i la grandària màxima de la finestra on buscar similituds (tau), es va dur a terme un estudi amb 660 combinacions d'eixos dos paràmetres junt amb la freqüència de mostratge (fs) del registre per a determinar la combinació òptima de valors per a estimar l'organització de la FA. D'altra banda, els treballs previs que han estudiat la terminació espontània de la FA s'han centrat en els últims 2 minuts abans de la terminació. Per contra, en esta tesi doctoral s'han estudiat per primera vegada registres de major duració amb l'objectiu de detectar els primers indicis de la terminació de la FA. Es va triar l'ús de H(2) per a este estudi per la seua alta precisió en la predicció de la terminació de la FA. A més, la DAF i SampEn es van calcular com a referències. En este estudi s'ha comprovat que l'organització de la FA només presenta variacions significatives en els últims 3 minuts abans de la seua terminació espontània. Per això, la predicció primerenca de la terminació no pareix possible amb els mitjans actuals d'anàlisi del senyal. Finalment, es va aplicar H(2) per a classificar entre FA paroxística i FA persistent a partir d'ECGs de curta duració, obtenint una millor precisió diagnòstica que amb la DAF i SampEn. Este resultat suggerix que l'anàlisi d'ECGs ambulatoris per mitjà de H(2) pot ser en eJulián Seguí, M. (2015). Study on the non-linear metrics contribution to estimate atrial fibrillation organization from the surface electrocardiogram [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56150TESI