Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation

Abstract

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

“…Since EMG is an important source of neural information, it has been extensively studied in the field of human-machine interfacing [ 2 , 3 ]. Applications of EMG include the control of neurorehabilitation devices such as prostheses [ 4 , 5 ], rehabilitation robots [ 6 , 7 ], and identification of muscle anatomical structure [ 8 ], but also implementations in leisure activities such as sports [ 9 ] and computer games [ 10 ].…”

Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

“…The proposed multi-scale filter is finally evaluated as a pre-processing step for the segmentation of the MUAP propagation region. Graph cut segmentation as recommended in Reference [28] and discussed in detail in Reference [29] is applied to the enhanced image using the proposed filter. The proposed multi-scale filter is also compared with some of the line-like structure enhancement algorithms, namely Hermite Filter, Gabor filter, and Steerable filters.…”

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

“…Visually inspecting HD-sEMG recordings for propagating waveforms is often used to reveal the approximate location of an IZ [41,54,55]. Other techniques have also been developed to automate the process and improve the estimation of the location [57][58][59]. These techniques rely on knowing the fibre orientation of the muscle and typically only consider one spatial dimension.…”