Biomedical subjects

Mohamed Kamel

Publications and source records attributed to Mohamed Kamel.

4 recordsLinked to original sources

PubMed2006

Adaptive certainty-based classification for decomposition of EMG signals.

An adaptive certainty-based supervised classification approach for electromyographic (EMG) signal decomposition is presented and evaluated. Similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit (MU) firing pattern information: passive and active. Performance of the developed classifier was evaluated using synthetic signals of known properties and real signals and compared with the performance of the certainty classifier (CC). Across the sets of simulated and real EMG signals used for comparison, the adaptive certainty classifier (ACC) had both better average performance and lower performance variability. For simulated signals of varying intensity, the ACC had an average correct classification rate (CCr ) of 83.7% with a mean absolute deviation (MAD) of 5.8% compared to 78.3 and 8.7%, respectively, for the CC. For simulated signals with varying amounts of shape and/or firing pattern variability, the ACC had a CCr of 79.7% with a MAD of 4.7% compared to 76.6 and 6.9%, respectively, for the CC. For real signals, the ACC had a CCr of 70.0% with a MAD of 6.3% compared to 64.9 and 6.4%, respectively, for the CC. The test results demonstrate that the ACC can manage both MUP shape variability as well as MU firing pattern variability. The ACC adapts to EMG signal characteristics to create dynamic data driven classification criteria so that the number of MUP assignments made reflects the signal complexity and the number of erroneous assignments is kept sufficiently low. The ability of the ACC to adjust to specific signal characteristics suggests that it can be successfully applied to a wide variety of EMG signals.

Computer Simulation↗

PubMed2006

Adaptive fuzzy k-NN classifier for EMG signal decomposition.

An adaptive fuzzy k-nearest neighbour classifier (AFNNC) for EMG signal decomposition is presented and evaluated. The developed classifier uses an adaptive assertion-based classification approach for setting a minimum classification threshold. The similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit firing pattern information: passive and active. The performance of the developed classifier was evaluated using synthetic signals with specific properties and experimental signals and compared with the performance of an adaptive template matching classifier, the adaptive certainty classifier (ACC). Across the sets of simulated and experimental EMG signals used for comparison, the AFNNC had better average classification performance overall, but due to the assignment of higher numbers of MUPs it made relatively more errors. Nonetheless, these increased error rates would still be acceptable for most clinical uses of decomposed EMG data. An independent and a related set of simulated signals were used for testing. For the independent simulated signals of varying intensity, the AFNNC had on average an improved correct classification rate (CCr) (8.1%) but an increased error rate (Er) (1.5%) compared to ACC. For the related simulated signals with varying amounts of shape and/or firing pattern variability, the AFNNC on average had an improved CCr (5%) but a slightly increased Er (0.3%) compared to ACC. For experimental signals, the AFNNC on average had improved CCr (6%) but an increased Er (2.1%) compared to ACC. The greatest gains in AFNNC performance relative to that of the ACC occurred when the variability of MUP shapes within motor unit potential trains was high suggesting that compared to a template matching assignment strategy the NN assignment paradigm is better able to ameliorate the classification problems caused by MUP instability.

Algorithms↗

PubMed2006

Wavelet approximation-based affine invariant shape representation functions.

In this paper, new wavelet-based affine invariant functions for shape representation are presented. Unlike the previous representation functions, only the approximation coefficients are used to obtain the proposed functions. One of the derived functions is computed by applying a single wavelet transform; the other function is calculated by applying two different wavelet transforms with two different wavelet families. One drawback of the previously derived detail-based invariant representation functions is that they are sensitive to noise at the finer scale levels, which limits the number of scale levels that can be used. The experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail-based functions.

Algorithms↗

PubMed2003

Ultrasound detection of heel enthesitis: a comparison with magnetic resonance imaging.

OBJECTIVE: Seronegative arthropathies are associated with inflammatory enthesopathy. The involvement of Achilles tendon and plantar aponeurosis is common, with strong tendency toward fibrosis and calcification. This study tests the diagnostic efficacy of ultrasound (US) in depicting enthesitis, and compares sonographic images with magnetic resonance images (MRI). METHODS: We studied 32 patients with a diagnosis of seronegative arthropathies, 22 men, 10 women, mean age 29 years. They had heel enthesopathy without typical conventional radiographic evidence. T1 and T2 weighted and short-tau inversion recovery (STIR) MRI sequences were obtained in axial and sagittal planes. An HDI 3000 ATL US device equipped with 12 MHz linear transducer was used to examine the enthesis. Three independent observers assessed the reliability of sonographic images by using video recording of the US examinations. RESULTS: US images of enthesitis showed loss of normal fibrillar echotexture of tendon (100%), lacking the homogeneous pattern, with blurring of tendon margins (56.2%) and irregular fusiform thickening (84.3%). The affected tendons showed intratendinous lesions with ill defined focal tendon defects filled with a mixture of fluid, fat, and/or granulation tissue, with loss of their tightly packed echogenic dots. MRI showed tendon enlargement (62.5%) with loss of the normal flattened hypointense appearance, focal thickening and rounded configuration at the insertion site (31.2%), intermediate T1 and high T2 signals, and diminished signals within the pre-Achilles fat pad due to inflammatory edema. Among all patients, 40.6% developed osteitis. CONCLUSION: MRI was not sensitive compared to US in detecting early changes of enthesopathy. Fatty degeneration appeared late in MRI, while it was detected earlier using US. MRI was not able to detect any calcification process at the insertion site, while US images clearly showed the very early signs of the calcification process. We recommend use of US for early diagnosis and in treatment and followup of patients with tendon enthesopathy, to accurately identify and diagnose different pathologic and biomechanical changes.

Achilles Tendon↗