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SVR modelling of mechanomyographic signals predicts neuromuscular stimulation-evoked knee torque in paralyzed quadriceps muscles undergoing knee extension exercise

Ibitoye MO, Hamzaid NA, Abdul Wahab AK, Hasnan N, Olatunji SO, Davis GM

Comput Biol Med, 2020 02;117:103614.
PMID: 32072969 DOI: 10.1016/j.compbiomed.2020.103614

BACKGROUND AND OBJECTIVE: Using traditional regression modelling, we have previously demonstrated a positive and strong relationship between paralyzed knee extensors' mechanomyographic (MMG) signals and neuromuscular electrical stimulation (NMES)-assisted knee torque in persons with spinal cord injuries. In the present study, a method of estimating NMES-evoked knee torque from the knee extensors' MMG signals using support vector regression (SVR) modelling is introduced and performed in eight persons with chronic and motor complete spinal lesions.
METHODS: The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension.
RESULTS: When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R2), with a Gaussian support vector kernel, the R2 value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively.
CONCLUSIONS: These results showed good predictive accuracy for SVR modelling, which can be generalized, and suggested that the MMG signals from paralyzed knee extensors are a suitable proxy for the NMES-assisted torque produced during repeated bouts of isometric knee extension tasks. This finding has potential implications for using MMG signals as torque sensors in NMES closed-loop systems and provides valuable information for implementing this method in research and clinical settings.
Fulltext Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

Ibitoye MO, Hamzaid NA, Abdul Wahab AK, Hasnan N, Olatunji SO, Davis GM

Sensors (Basel), 2016 Jul 19;16(7).
PMID: 27447638 DOI: 10.3390/s16071115

The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.
Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach

Oyehan TA, Alade IO, Bagudu A, Sulaiman KO, Olatunji SO, Saleh TA

Comput Biol Med, 2018 07 01;98:85-92.
PMID: 29777986 DOI: 10.1016/j.compbiomed.2018.04.024

The optical properties of blood play crucial roles in medical diagnostics and treatment, and in the design of new medical devices. Haemoglobin is a vital constituent of the blood whose optical properties affect all of the optical properties of human blood. The refractive index of haemoglobin has been reported to strongly depend on its concentration which is a function of the physiology of biological cells. This makes the refractive index of haemoglobin an essential non-invasive bio-marker of diseases. Unfortunately, the complexity of blood tissue makes it challenging to experimentally measure the refractive index of haemoglobin. While a few studies have reported on the refractive index of haemoglobin, there is no solid consensus with the data obtained due to different measuring instruments and the conditions of the experiments. Moreover, obtaining the refractive index via an experimental approach is quite laborious. In this work, an accurate, fast and relatively convenient strategy to estimate the refractive index of haemoglobin is reported. Thus, the GA-SVR model is presented for the prediction of the refractive index of haemoglobin using wavelength, temperature, and the concentration of haemoglobin as descriptors. The model developed is characterised by an excellent accuracy and very low error estimates. The correlation coefficients obtained in these studies are 99.94% and 99.91% for the training and testing results, respectively. In addition, the result shows an almost perfect match with the experimental data and also demonstrates significant improvement over a recent mathematical model available in the literature. The GA-SVR model predictions also give insights into the influence of concentration, wavelength, and temperature on the RI measurement values. The model outcome can be used not only to accurately estimate the refractive index of haemoglobin but also could provide a reliable common ground to benchmark the experimental refractive index results.
Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector regression model

Alade IO, Bagudu A, Oyehan TA, Rahman MAA, Saleh TA, Olatunji SO

Comput Methods Programs Biomed, 2018 Sep;163:135-142.
PMID: 30119848 DOI: 10.1016/j.cmpb.2018.05.029

BACKGROUND AND OBJECTIVES: The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiology of different diseases. Measurement of the real part of the refractive index remains a challenge due to strong absorption of the hemoglobin especially at relevant high physiological concentrations. So far, only a few studies on direct measurement of refractive index have been reported and there are no firm agreements on the reported values of refractive index of hemoglobin due to measurement artifacts. In addition, it is time consuming, laborious and expensive to perform several experiments to obtain the refractive index of hemoglobin. In this work, we proposed a very rapid and accurate computational intelligent approach using Genetic Algorithm/Support Vector Regression models to estimate the real part of the refractive index for oxygenated and deoxygenated hemoglobin samples.
METHODS: These models utilized experimental data of wavelengths and hemoglobin concentrations in building highly accurate Genetic Algorithm/Support Vector Regression model (GA-SVR).
RESULTS: The developed methodology showed high accuracy as indicated by the low root mean square error values of 4.65 × 10-4 and 4.62 × 10-4 for oxygenated and deoxygenated hemoglobin, respectively. In addition, the models exhibited 99.85 and 99.84% correlation coefficients (r) for the oxygenated and deoxygenated hemoglobin, thus, validating the strong agreement between the predicted and the experimental results CONCLUSIONS: Due to the accuracy and relative simplicity of the proposed models, we envisage that these models would serve as important references for future studies on optical properties of blood.