sEMG of the 6-minute walk test in patients with Duchenne Muscular Dystrophy

Abstract

Duchenne Muscular Dystrophy (DMD) is a neuromuscular disorder caused by mutations in the DMD gene, resulting in progressive weakening and impairment of muscular function. Patients affected by DMD experience continuous motor deterioration, needing objective assessment tools to quantify muscular effort and evaluate compensatory gait strategies during functional activities. Surface electromyography (sEMG) provides a non-invasive method for recording and analyzing muscle activation, making it an ideal technique for clinical and research purposes. However, reliable interpretation of sEMG signals requires advanced signal processing algorithms capable of accurately detecting and analyzing muscle activation patterns, particularly during dynamic tasks such as the 6-minute walk test (6MWT).

This study aimed to develop and validate a robust signal processing algorithm specifically designed for accurate detection and detailed analysis of muscle activation patterns from sEMG recordings obtained during the 6MWT in ambulatory DMD patients. A novel, comprehensive signal processing approach was created using bilateral sEMG data collected from the quadriceps and gluteal muscles with portable biosignal monitors (PLUX Biosignal).

The algorithm used discrete wavelet decomposition with a db4 mother wavelet to decompose each sEMG signal into approximation and detail coefficients, retaining only the approximation coefficients to remove baseline drift while preserving low-frequency components. Following reconstruction, envelope detection was performed by further wavelet decomposition to obtain a smoothed signal representing the amplitude modulation envelope, which was scaled to enhance the signal-to-noise ratio. Amplitude thresholding was then applied using defined window lengths and thresholds specific to each muscle to detect activation events with temporal resolution. The algorithm’s performance was evaluated by calculating Pearson’s correlation coefficient between the mean amplitude values derived from the processed sEMG signals and the total distance walked during the 6MWT of 8 DMD ambulatory patients (8.77 ± 2.58 years old), providing quantitative validation of its output.

Results demonstrated that the newly developed algorithm effectively identified distinct muscle activation patterns with high accuracy and consistency. Enhanced signal visualization clearly illustrated increased muscle activation amplitudes corresponding directly with greater walking distances in the quadriceps (r = 0.918; p < 0.01) and gluteal muscles (r = 0.815; p = 0.014). These visual representations were reproducible and provided a detailed understanding of muscle activation dynamics, significantly enhancing the interpretability of sEMG signals collected during the functional task.

The developed signal processing algorithm offers precise, reproducible detection and detailed visualization of muscle activation in ambulatory DMD patients during functional walking assessments. By providing objective and robust analyses of muscle function, this methodological advancement holds substantial clinical utility. It can serve as an effective tool for clinicians to objectively evaluate motor impairment, optimize therapeutic interventions, and potentially monitor disease progression more accurately in patients with DMD.