Multi-scale surface electromyography modeling to identify changes in neuromuscular activation with myofascial pain

Trigger Point Injections

Myofascial pain and myofascial pain syndromes are among some of the most common acute and chronic pain conditions. Many interventional procedures can be performed in both an acute and chronic pain setting to address myofascial pain syndromes. Trigger point injections can be performed with or without imaging guidance such as fluoroscopy and ultrasound; however, the use of imaging in years past has been recommended to improve patient outcome and safety. Injections can be performed using no injectate (dry needling), or can involve the administration of local anesthetics, botulinum toxin, or corticosteroids.

Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

Feasibility of Ultrasound-Guided Trigger Point Injection in Patients with Myofascial Pain Syndrome

We compared the feasibility of ultrasound (US)-guided myofascial trigger point (MTrP) injection with that of a blind injection technique following the use of shear wave elastography (SWE) for the measurement of stiffness at the MTrPs in patients with trapezius myofascial pain syndrome (MPS). A total of 41 patients (n = 41) were randomized to either the trial group (n = 21, SWE combined with US-guided injection) or the control group (n = 20, SWE combined with blind injection). At baseline and four weeks, they were evaluated for the manual muscle test (MMT), the range of motion (ROM), pain visual analogue scale (VAS) scores, Shoulder Pain and Disability Index (SPADI) scores and Neck Disability Index (NDI) scores during the abduction, adduction, flexion, extension, external rotation and internal rotation of the shoulder joint. Differences in changes in pain VAS scores, NDI scores and SPADI scores at four weeks from baseline between the two groups reached statistical significance (p = 0.003, 0.012, and 0.018, respectively). US-guided MTrP injection is a more useful modality as compared with a blind injection in patients with MPS.

Characterizing the SEMG patterns with myofascial pain using a multi-scale wavelet model through machine learning approaches

In this paper, we introduce a newly developed multi-scale wavelet model for the interpretation of surface electromyography (SEMG) signals and validate the model's capability to characterize changes in neuromuscular activation in cases with myofascial pain syndrome (MPS) via machine learning methods. The SEMG data collected from normal (N = 30; 27 women, 3 men) and MPS subjects (N = 26; 22 women, 4 men) were adopted for this retrospective analysis. SMEGs were measured from the taut-band loci on both sides of the trapezius muscle on the upper back while he/she conducted a cyclic bilateral backward shoulder extension movement within 1 min. Classification accuracy of the SEMG model to differentiate MPS patients from normal subjects was 77% using template matching and 60% using K-means clustering. Classification consistency between the two machine learning methods was 87% in the normal group and 93% in the MPS group. The 2D feature graphs derived from the proposed multi-scale model revealed distinct patterns between normal subjects and MPS patients. The classification consistency using template matching and K-means clustering suggests the potential of using the proposed model to characterize interference pattern changes induced by MPS.