Biomedical Engineering: Applications, Basis and Communications

In order to correctly identify the motor unit action potential trains (MUAPTs) in estimated discharges from dynamic surface electromyogram (EMG), an approach for accurate classification of motor unit (MU) discharges is presented. First, the estimated discharges are obtained manually, then the estimated discharges are classified as MUAPTs based on the MU location, which combines the MU depth with the MU plane position. During verification in dynamic muscle contractions, the advanced tripole model is introduced. At SNRs of 10, 20 and 30dB, the MUAPTs were identified with true positive rate (TPR) of 91.1$\pm$5.5%, 95.2$\pm$3.7% and 96.1$\pm$2.9%. The results also show that the MU location can be used as a simple method for identifying MUAPT from estimated discharges and selecting reliably decomposed discharges. The newly introduced method is a robust and reliable indicator of MUAPT identification accuracy.

Detecting the QRS complex on an ECG signal leads to precious information about the signal under study. Different noises, arrhythmias, and diseases alter the shape and energy of the signal, making it harder to detect the QRS points. Several algorithms for QRS detection have been proposed and most of them merely focus on precision improvement, and therefore certain limitations have emerged with regard to deployment of these algorithms. As a result, while developing the new algorithm, not only efforts have been made to keep the precision at a high level, but also it has been tried to keep an eye on the generality of the algorithm, and to eliminate the end user limitations as much as possible. To this end, we have used an exclusive mother wavelet together with an artificial neural network to develop an algorithm which not only has superior precision, but also does not require changing the tuning parameters for each different signal. In other words, the algorithm extracts the required parameters automatically. In this method, first, an exclusive mother wavelet identical to the input signal is formed. Then, by using the mother wavelet, matrices containing sufficient data to be processed by the neural network are developed. Using these matrices, the existing QRSs will be detected with a sensitivity of 99.81% on MIT-BIH and 99.49% on physiozoo datasets.

Microscopic digital image processing paves the way for study and evaluation of blastomere identification and localization as a preprocessing step for the embryos selection for the In VitroFertilization (IVF) transfer. Computer vision aims at developing automated image system to localize and grade blastomeres before injection. In this paper, we propose a clustering-based system that supports the localization and counting of blastomeres. The dataset, employed in this study, is formed of 50 Images collected at Assisted Reproduction Technology (ART) Unit, International Islamic Center for Population Studies and Research, Al-Azhar University, Egypt. The proposed system is formed of 2 modules named preprocessing and segmentation modules, where different algorithms were investigated for each module. The preprocessing module includes Image denoising and enhancement tasks. Whereas the edge enhancement investigates the performance of Ostu’s thresholding, Canny and Sobel edge detection techniques, while employing Circular Hough Transform (CHT) for the segmentation task. A fusion-based algorithm was then employed to merge the segmented Blastomeres of the previously defined systems to boost the performance through integrated blastomeres, as well the confidence in localization. The fusion-based algorithm showed very promising results reaching an average Precision, sensitivity, and Overall Quality of 87.9%, 92.9%, and 82.3%, respectively.

The retina is the most important part of the eye. Early detection of retinal disease can be done through the passage of the blood vessels of the retina. Enhancement of the quality of retinal images that have both noise and noise is the first step in image processing to help improve the accuracy of the results for image segmentation and extraction. Images store a lot of information, but often there is a decrease in quality or image defects. So that images that have experienced interference or noise are easily interpreted, then the image can be manipulated into other images of better quality using image processing techniques or methods. The neural network-based method that is currently popular is deep learning. The segmentation process is currently a widely used method of deep learning that has grown rapidly used in various studies. One of the popular methods is Convolutional Neural Network (CNN). CNN can handle large-dimensional data such as images because the input to CNN is in the form of a matrix. Since the findings of retinal blood vessel segmentation are often inaccurate and there is always noise, this study will look at how to segment retinal images in blood vessels using CNN U-Net and LadderNet methods. Proper segmentation of retinal blood vessels can be the first step to detecting a disease. Segmentation and analysis of retinal blood vessels can assist medical personnel in detecting the severity of a disease. The stages of image enhancement used are Histogram Equalization and Clahe. Segmentation of blood vessels is done using CNN U-Net and LadderNet Methods. The results of the application of the enhancement and segmentation using the U-Net and LadderNet methods on training and on testing data were tested on the DRIVE dataset. The results of measurement of accuracy, specificity, sensitivity and F1 Score of blood vessel segmentation using the U-Net CNN method were 95.46%, 98.56%, 74.20%, and 80.63%, respectively. While the results of the CNN LadderNet method were 95.47%, 98.42%, 75.19%, and 80.86%, respectively. Based on the results of blood vessel segmentation from two proposed methods, the result of the CNN LaddetNet method is greater than the CNN U-Net method in accuracy, sensitivity, and F1 Score. The proposed approach will be further developed in the future, with the aim of increasing the value of the blood vessel segmentation process evaluation outcomes.

Objectives: This study aims to develop a computer-aided therapy (CAT) application to help children who suffer from delayed language development (DLD) improve their language, especially during the COVID-19 pandemic. Methods: The implemented system teaches the children their body parts using the Egyptian dialect. Two datasets were collected from healthy children (2800 words) and unhealthy children (236 words) who have DLD at the clinic. The model is implemented using a speaker-independent isolated word recognizer based on a discrete-observation hidden Markov model (DHMM) classifier. After the speech signal preprocessing step, K-means algorithm generated a codebook to cluster the speech segments. This task was completed under the MATLAB environment. The graphical user interface was implemented successfully under the C# umbrella to complete the CAT application task. The system was tested on healthy and DLD children. Also, in a small clinical trial, five children who have DLD tested the program in an actual trial to monitor their pronunciation progress during therapeutic sessions. Results: The max recognition rate was 95.25% for the healthy children dataset, while 93.82% for the DLD dataset. Conclusion: DHMM was implemented successfully using nine and five states based on different codebook sizes (160, 200). The implemented system achieved a high recognition rate using both datasets. The children enjoyed using the application because it was interactive. Children who have DLD can use speech recognition applications.

Magnetic Resonance Imaging (MRI) techniques are a fundamental and imperative part of the medical image processing field. The images acquired from the MRI machines are affected by the noise. This noise degrades the quality of the images. Acquisition of MRI with noise may give erroneous results. Hence, to enhance the image quality, it is necessary to reduce or remove this noise. To enhance the image quality of MRI, a plethora of filtering algorithms are available along with the morphological operations. In this paper, we have implemented numerous filters like Adaptive Median filter, Median filter, Mean filter, bilateral filter, NLM filter, Gaussian filter, Weiner filter, and morphological operations to eliminate the noise in the MRI of the spinal cord. The scenarios considered are 1. Application of filters, 2. Application of filters followed by morphological operations, and 3. Morphological operations followed by the application of filters. Statistical parameters like Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are found for all three approaches and are used to analyze the performance of these techniques. NLM filters are found to give the best performance when compared to other filters. Morphological operations affect the performance of the filters. Application of morphological operations before filtering degrades the filter performance while applying them after improves the performance. The dataset comprises of 250 spinal cord MRIs with noise. The author inferred that the performance of the filters is improved by applying the filtering techniques after the morphological operation.

Gene Regulatory Networks (GRNs) reconstruction aims to infer relationships of potential regulation among the genes. With the rapid growth of the biotechnology, such as Ribonucleic acid (RNA)-sequencing and gene chip microarray, the generated high-throughput data provide gene–gene interaction relationships with more opportunities based on gene expression data. Several approaches are introduced to reconstruct the GRNs, but low accuracy is a major drawback. Hence, this paper introduces the hybrid distance measure and the Pearson’s correlation coefficient for reconstructing GRN. The hybrid distance, such as Tversky index, Tanimoto similarity, and Minkowski distance, is employed to connect the edges. The asymmetric partial correlation network is introduced for determining two influence functions for every pair, and edge direction is determined among them. However, the direction of edges is unknown usually and seems difficult to be identified based on gene expression data. Thus, it extends the data processing inequality applying in the directed network for removing the transitive interactions. The influence value of every node is calculated for identifying the significant regulator. The performance of the proposed Hybrid Distance_Entropy based GRN Reconstruction method is analyzed in terms of correlation, reconstruction error, precision, and recall, which provides superior results with values 0.9450, 0.00052, 0.9095, and 0.8913 based on dataset-1.

Micro catheters are thin-walled devices gaining pivotal importance in the field of micro invasive surgeries. The need for an efficient design of a micro catheter with the enhancement of its prime characteristics like-kink resistance, lower bending response, increased perumbular capacities, etc., has been the key parameters for research among biomedical engineers. The article highlights the nuances in the technology in the fabrication of micro active catheters and the procedure and necessity of embolization in the process of catheterization. Efficacies of different designs of micro active catheters were studied based on a variety of clinical data trials by several researchers and doctors. Superior materials capable of enhancing the torque efficacy of the device like auxetic materials and their effect on bending angles were studied. Clinical trials were undertaken based on various designs and approaches for the device and the critical characteristics were studied. The micro active catheter with guide-wire shows maximum bending angle and considerable torque making it ideal for micro invasive procedures in constricted as well as divergent blood vessels.