Assessing Neuromuscular System via Patellar Tendon Reflex Analysis using EMG in Healthy Individuals

Thu, 30 Jan 2025 00:00:00 +0000

Patellar tendon reflex tests are essential for evaluating neuromuscular function and identifying abnormalities in nerve conduction and muscle response. This study explored how age, height, weight, and gender influence reflex response times in healthy individuals, providing a reference for future research on different neuromuscular conditions. We analyzed reflex onset, endpoint, and total duration of reflexes using electromyography (EMG) recordings from 40 healthy participants. Reflexes were elicited by striking the patellar tendon, and participants were grouped based on age, height, weight, and gender. We investigated both the individual and combined effects of these factors on reflex response times. Additionally, height and weight-normalized data were analyzed to clarify their roles in influencing reflexes across age groups. Gender-specific analyses were conducted as well to assess potential differences between males and females. Our findings indicated that reflex onset was significantly delayed in elderly individuals, particularly in taller and heavier individuals, and in males compared to females. Even with height normalization, elderly participants showed slower reflexes. Weight-normalized data revealed that younger participants exhibited longer total reflex durations, likely due to their greater height, which impacted nerve conduction time. This trend was consistent across genders, with males generally exhibiting longer duration of reflex response times. These findings provide insights into how different demographic factors, particularly aging, affect neuromuscular reflexes and could serve as a reference for diagnosing and monitoring neuromuscular disorders.

Beyond pixel Superpixel-based MRI Segmentation through Traditional Machine Learning and Graph Convolutional Network

Fri, 01 Nov 2024 00:00:00 +0000

Background and Objective:

Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further enables detailed analysis of specific tendon regions using automated or semi-automated methods. This study specifically aims at the segmentation of Achilles tendon, the largest tendon in the human body.

Methods:

This study proposes a comprehensive end-to-end tendon segmentation module composed of a preliminary superpixel-based coarse segmentation preceding the final segmentation task. The final segmentation results are obtained through two distinct approaches. In the first approach, the coarsely generated superpixels are subjected to classification using Random Forest (RF) and Support Vector Machine (SVM) classifiers to classify whether each superpixel belongs to a tendon class or not (resulting in tendon segmentation). In the second approach, the arrangements of superpixels are converted to graphs instead of being treated as conventional image grids. This classification process uses a graph-based convolutional network (GCN) to determine whether each superpixel corresponds to a tendon class or not.

Results:

All experiments are conducted on a custom-made ankle MRI dataset. The dataset comprises 76 subjects and is divided into two sets: one for training (Dataset 1, trained and evaluated using leave-one-groupout cross-validation) and the other as unseen test data (Dataset 2). Using our first approach, the final test AUC (Area Under the ROC Curve) scores using RF and SVM classifiers on the test data (Dataset 2) are 0.992 and 0.987, respectively, with sensitivities of 0.904 and 0.966. On the other hand, using our second approach (GCN-based node classification), the AUC score for the test set is 0.933 with a sensitivity of 0.899.

Conclusions:

Our proposed pipeline demonstrates the efficacy of employing superpixel generation as a coarse segmentation technique for the final tendon segmentation. Whether utilizing RF, SVM-based superpixel classification, or GCN-based classification for tendon segmentation, our system consistently achieves commendable AUC scores, especially the non-graph-based approach. Given the limited dataset, our graph-based method did not perform as well as non-graph-based superpixel classifications; however, the results obtained provide valuable insights into how well the models can distinguish between tendons and non-tendons. This opens up opportunities for further exploration and improvement.

2 | Zakia Khatun

Assessing Neuromuscular System via Patellar Tendon Reflex Analysis using EMG in Healthy Individuals

Beyond pixel Superpixel-based MRI Segmentation through Traditional Machine Learning and Graph Convolutional Network