Multidimensional Biomechanics-Based Score to Assess Disease Progression in Duchenne Muscular Dystrophy
2. Materials and Methods
2.1. Ethical Statements
2.2. Sensor and System Characteristics
2.3. Performed Tests
- Visit 1 (t0). The first visit, tagged as “t0”, was conducted to collect baseline data. During this visit, the participant performed the 6MWT once.
- Visit 2 (t0r). The objective of the second visit, which was labelled “t0r,” was to determine test–retest reliability. A subgroup of participants, made up of those who lived in the hospital area, underwent the test after a break of about 3–4 weeks.
- Visit 3 (t1). The third visit was tagged as “t1” and was planned in order to validate the test’s responsiveness. The test was performed with a time gap between t0 and t1 of approximately 6 months.
2.4. Data Analysis
2.4.1. Pre-Processing and Feature Extraction
2.4.2. First Stage
- Data cleansing. DMD disorder is considered to affect the right and left limbs almost symmetrically . Thus, the variables corresponding to the sensors placed in both right and left parts of the body were averaged into a single measure.
- Feature selection I. Robust variable selection. The most robust and repeatable variables were selected by comparing t0 and t0r sessions for all subjects. These are expected to have similar values between sessions as they were recorded in a short time-window. As the sets of data are used to compare an increase from the same individuals at different sessions, the data are paired. To determine which statistical test was the most suitable for each variable, a Shapiro–Wilk test was applied. If the variable followed a normal distribution for both sessions, a Paired Samples T-Test was applied. If not, the chosen test was the Wilcoxon Signed-Rank Test. The variables that exhibited the same underlying distribution for both sessions (p > 0.05) were considered robust and used for subsequent analysis.
- Feature selection II. Selection of the variables that best characterize the pathology. This step compared how the control and pathological distributions differ between each other. For each one of the variables, the Kruskal–Wallis test for unpaired data was used to assess if control and pathological distributions were significantly different. The values that showed a significant difference (p < 0.01) were selected for further analysis.
- Generation of a unified score for each of the categories. The selected variables, which belonged to one of the five categories, were first normalized (the minimum value was set to 0 and the maximum value to 1). The direction of each variable had to be the same. In other words, it was necessary that the pathological cases always showed the lowest numbers and the control cases the highest. Therefore, if the median value of control cases was lower than the pathological median value, the variable was inverted. Finally, a single variable for each category was computed as the mean of the normalized variables belonging to that category and adjusted between 0 and 100.
2.4.3. Second Stage
- Generation of the physiological gait model. For the control group, the computed scores for each category (Spatio-temporal, Cardiac, Kinematics, EMG, and Plantar pressure scores) were used to define a 5-dimensional gaussian-mixture model, where each dimension corresponded to one category. This model was defined as the physiological gait model (PGM). By using the Bayesian Information Criterion , the number of components for modelling the PGM as well as the covariance type (from spherical, diagonal, tied, and full) were determined.
- Obtaining the global score for controls pathological observations. Both the pathological and control subjects achieved a score for both the 6 min (6M+) and 2 min (2M+) tests. These scores were calculated as the weighted log probability of belonging to the PGM and, subsequently, scaled from 0 to 100. The highest score was 100, which was expected for control subjects. The further an observation was from the model, the lower was its score. Indeed, controls would score better since they are predicted to have a higher likelihood of fitting PGM. Depending on their severity, pathological individuals were predicted to gradually stray from the PGM. Consequently, they are expected to gradually receive lower scores.
2.5. Score Validation
3.1. Generation of the Multidimensional Score
3.2. Generation of the 6M+ and 2M+ Scores
3.3. The Pathological 2M+ and 6M+ Score and Its Comparison with NSAA and Distances
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Sensor Type||Description||Device Used||Fs||Other Specifications||Category|
|Surface EMGs||EMGs were integrated in custom-made, size-appropriate shorts that enabled recording of muscle activity.||Mshorts Myontec||1000 Hz captured|
25 Hz transmitted
|24-bit ADC, sampling rate of 1000 Hz, bandwidth of 40–200 Hz (−3 dB)|
25 Hz (rectified and averaged from 1000 Hz Raw EMG)
Electrodes: silver-coated yarn
|Inertial sensors||Sensors were placed at the thigh, shank, and in the plantar insoles to measure joint kinematics.||Movesense inertial sensors||104 Hz||Gyroscope: ±2000 dps|
Accelerometer: ±8 g
|Heart rate||A strap was applied below the chest to measure cardiorespiratory parameters.||Movesense HR sensor||1 Hz||Cardiac|
|Plantar pressure||Pressure sensors were integrated in insoles, which were size-appropriate and used to obtain information on weight distribution during gait.||Moticon plantar insoles||100 Hz||Gyroscope: ±2000 dps|
Accelerometer: ±16 g
|Plantar pressure, spatio-temporal, kinematics|
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Migliorelli, C.; Gómez-Martinez, M.; Subías-Beltrán, P.; Claramunt-Molet, M.; Idelsohn-Zielonka, S.; Mas-Hurtado, E.; Miralles, F.; Montolio, M.; Roselló-Ruano, M.; Medina-Cantillo, J. Multidimensional Biomechanics-Based Score to Assess Disease Progression in Duchenne Muscular Dystrophy. Sensors 2023, 23, 831. https://doi.org/10.3390/s23020831
Migliorelli C, Gómez-Martinez M, Subías-Beltrán P, Claramunt-Molet M, Idelsohn-Zielonka S, Mas-Hurtado E, Miralles F, Montolio M, Roselló-Ruano M, Medina-Cantillo J. Multidimensional Biomechanics-Based Score to Assess Disease Progression in Duchenne Muscular Dystrophy. Sensors. 2023; 23(2):831. https://doi.org/10.3390/s23020831Chicago/Turabian Style
Migliorelli, Carolina, Meritxell Gómez-Martinez, Paula Subías-Beltrán, Mireia Claramunt-Molet, Sebastian Idelsohn-Zielonka, Eudald Mas-Hurtado, Felip Miralles, Marisol Montolio, Marina Roselló-Ruano, and Julita Medina-Cantillo. 2023. "Multidimensional Biomechanics-Based Score to Assess Disease Progression in Duchenne Muscular Dystrophy" Sensors 23, no. 2: 831. https://doi.org/10.3390/s23020831