A novel spatial feature for the identification of motor tasks using high-density electromyography

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG—force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle’s coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs

High-density surface electromyography maps after computer-aided training in individual with congenital transverse deficiency: a case study

Background: The aim of this study was to determine whether computer-aided training (CAT) of motor tasks would increase muscle activity and change its spatial distribution in a patient with a bilateral upper-limb congenital transverse deficiency. We believe that our study makes a significant contribution to the literature because it demonstrates the usefulness of CAT in promoting the neuromuscular adaptation in people with congenital limb deficiencies and altered body image. Case presentation: The patient with bilateral upper-limb congenital transverse deficiency and the healthy control subject performed 12 weeks of the CAT. The subject’s task was to imagine reaching and grasping a book with the hand. Subjects were provided a visual animation of that movement and sensory feedback to facilitate the mental engagement to accomplish the task. High-density electromyography (HD-EMG; 64-electrode) were collected from the trapezius muscle during a shrug isometric contraction before and after 4, 8, 12 weeks of the training. After training, we observed in our patient changes in the spatial distribution of the activation, and the increased average intensity of the EMG maps and maximal force. Conclusions: These results, although from only one patient, suggest that mental training supported by computer-generated visual and sensory stimuli leads to beneficial changes in muscle strength and activity. The increased muscle activation and changed spatial distribution of the EMG activity after mental training may indicate the training-induced functional plasticity of the motor activation strategy within the trapezius muscle in individual with bilateral upper-limb congenital transverse deficiency. Marked changes in spatial distribution during the submaximal contraction in the patient after training could be associated with changes of the neural drive to the muscle, which corresponds with specific (unfamiliar for patient) motor task. These findings are relevant to neuromuscular functional rehabilitation in patients with a bilateral upper-limb congenital transverse deficiency especially before and after upper limb transplantation and to development of the EMG based prostheses

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence. Keywords: Breast cancer, Cancer recurrence, Computer-assisted diagnosis, Machine learning, Prognosi

surface electromyographic data simulation

<p>The model proposed by Farina <i>et al</i> was used to generate surface EMG signals [<a href="#_ENREF_1">1</a>].  In this model, the volume conductor was described as an anisotropic multilayered cylinder and the source was a spatio-temporal function describing the generation, propagation, and extinction of the intracellular action potential at the end-plate, along the fiber, and at the tendons, respectively. The Inter-Electrode-Distance (IED) was set to 5 mm as recommended in [<a href="#_ENREF_2">2</a>] to locate IZs. The remainder of the model parameters used in our study were in principle the same as  those used by Mesin <i>et al</i>  [<a href="#_ENREF_3">3</a>].  Finally, the number of active MUs in each 60-ms simulated signal interval was between 1 and 5. Signals were zero-phase digitally band-pass filtered [<a href="#_ENREF_4">4</a>] using an overall eighth-order Butterworth filter with cut-off frequencies 20 and 500 Hz.</p> <p> </p> <p>            For each MU number category (1 to 5), sEMG signals with SNR values of -5, 0, 5, 10 and 15 dB were simulated to include very low to moderate quality sEMG signals. Twenty Single-Differential (SD) channels were simulated along the muscle fiber direction and sampling frequency was 4096 Hz. Thirty frames (or images) with up to 5 IZs were simulated for each SNR value. The temporal location of the IZs was created randomly in each frame. The signal SNR for each simulated 60-ms epoch was defined as the RMS of the raw sEMG divided by the standard deviation of the added Gaussian noise, expressed in dB [<a href="#_ENREF_5">5</a>]. Thus, a total of 750 1-D linear array sEMG signals were simulated, considering five SNR values and maximum five MUs . We also provided the gold standard data for the IZ channels and CV values.</p> <p> </p> <p> </p> <p><a>1.         Farina D, Mesin L, Martina S, Merletti R. A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE transactions on bio-medical engineering. 2004;51(3):415-26. doi: 10.1109/tbme.2003.820998.</a></p> <p><a>2.         Afsharipour B, Ullah K, Merletti R. Spatial Aliasing and EMG Amplitude in Time and Space: Simulated Action Potential Maps. In: Roa Romero ML, editor. XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013: MEDICON 2013, 25-28 September 2013, Seville, Spain. Cham: Springer International Publishing; 2014. p. 293-6.</a></p> <p><a>3.         Mesin L, Gazzoni M, Merletti R. Automatic localisation of innervation zones: A simulation study of the external anal sphincter. Journal of Electromyography and Kinesiology. 2009;19(6):e413-e21. doi: 10.1016/j.jelekin.2009.02.002.</a></p> <p><a>4.         Gustafsson F. Determining the initial states in forward-backward filtering. IEEE Transactions on Signal Processing. 1996;44(4):988-92. doi: 10.1109/78.492552.</a></p> <p><a>5.         Kay SM. Fundamentals of statistical signal processing. Englewood Cliffs, N.J.: Prentice-Hall PTR; 1993.</a></p> <p> </p> <p> </p

An example of the propagating region identification procedure (stage IV of the proposed algorithm) and feature extraction.

<p>The slope parameters found by center/edge coordinates are shown by triangles and pentagons, respectively. Bold triangles show the closest distance of edges. The center is defined as the center of each propagating region. The edges are the upper and lower boundaries of such regions. The slope is calculated based on the angle between a virtual line representing the propagation region (bold line) and the horizontal line.</p

The structure of the IZ detection program including: (I) Pre-Processing, (II) Segmentation, (III) Pruning, (IV) Region identification and (V) Innervation points detection.

<p>EMG matrix preparation extracts an appropriate epoch of data for image conversion. Graph-Cut algorithm was used for image segmentation. Parameters (Slope, center/edge coordinates) in step IV were estimated to consider the interaction between regions in the image.</p

The absolute and relative muscle fiber conduction velocity error in m/s, and percentage, respectively of the proposed algorithm (MEAN±SD).

<p>The absolute and relative muscle fiber conduction velocity error in m/s, and percentage, respectively of the proposed algorithm (MEAN±SD).</p

Examples of the simulated sEMG signals with 20 Single Differential (SD) channels and 60-ms epochs.

<p>The image frames A to D contained 2 IZs (-5 dB SNR), 3 IZs (0 dB), 4 IZs (5 dB) and 5 IZs (10 dB), respectively. The location of the simulated IZs is shown by circles. The developed program automatically identified the location of IZs as the crossing of the ‘v’ shape propagation lines (upper lines in blue and lower lines in red color). The CV of the identified propagation pattern was then estimated by the proposed algorithm.</p

The morphological processing (pruning) steps on the result of Fig 3 consist of (a) Dilating image with pair mode structure, (b) Opening region via disk structure, (c) Region erosion via line structure and (d) reconstructing region according to the original image.

<p>In such procedures, the irrelevant structures i.e. non-propagating parts were suppressed while the detectability of the propagating regions close to the innervation zones was improved.</p