فصلنامه مهندسی پزشکی زیستی
سال شانزدهم شماره 2 (تابستان 1401)

Intuitive examination of retinal layers in Spectral-domain Optical Coherence Tomography (SD-OCT) images is one of the main methods used by physicians to diagnose retinal diseases. This method faces challenges such as noise and image complexity and the proximity of retinal layers. In recent years, the automatic diagnosis of retinal diseases has become an important clinical issue in computer vision. In this research, a new method for efficient multi-class automatic classification of SD-OCT images has been proposed. This method consists of five stages, preprocessing, layer recognition, feature extraction, and image classification. Examination of the shape of the RNFL layer and IS/OS junction as a clinical method is influential in physicians' decisions to diagnose retinal diseases. Therefore, in this study, inspired by this clinical diagnosis method, the RNFL layer, and the IS/OS junction have been detected by a new method based on the Frangi vessel enhancement algorithm and the gradient of the image. Then, by extracting and selecting several efficient features from the curves of the layers, an algorithm based on the ensemble decision tree has been proposed for classifying SD-OCT images of the retina and presented as a MATLAB application. The proposed method has been evaluated using images of two well-known databases of Duke and Kermany. Based on the results, precision, sensitivity, specificity, accuracy, miss rate and F1-score of the proposed method in Duke database were equal to 98.7, 98.8, 99.4, 99.1, 1.3, and 98.7, respectively, and in Kermany database were 96.8, 96.7, 98.9, 98.4, 3.2 and 96.7 respectively. The results show the superiority of the proposed method compared to other comparative methods. In summary, the use of efficient features of retinal effective layers and a powerful algorithm for classification has improved the performance of the proposed method compared to previous more complex methods.

Anxiety disorders are one of the most common and debilitating mental disorders worldwide. On the other hand, since 2019, with the outbreak of Covid-19, anxiety has increased among people, especially the medical staff. Currently, anxiety is diagnosed (when the symptoms are severe enough) using a questionnaire by a specialist. To resolve this shortcoming, researchers have recently paid attention to the use of brain signals. Consequently, the present study aimed to diagnose anxiety using brain signals. The novelty of this study is the use of the Chebyshev chaotic map for the first time in biological signal analysis. It used the DASPS database, which includes a 14-channel electroencephalogram (EEG) of 23 people (10 men and 13 women, with a mean age of 30 years). The self-assessment manikin scores were used to divide anxiety into two and four levels. First, the data were normalized. Then, the chaotic map was reconstructed and divided into 128 strips. The density of points in each of the strips was calculated. Two indicators were considered as features, 1) maximum density and 2) its corresponding sample. Finally, features were applied to Support Vector Machines (SVM) and k-Nearest Neighbors (K-NN) in 5 ways, (1) feature 1 of all channels, (2) feature1 mapping of all channels using principal component analysis (PCA) (3) feature 2 of all channels, (4) feature2 mapping of all channels using PCA and (5) each feature - each channel separately. The results show a maximum accuracy of 93.75 % for diagnosing two levels of anxiety and 96.15 % for diagnosing four levels of anxiety. In addition, K-NN outperformed SVM. Accordingly, the proposed algorithm can be introduced as a suitable approach for diagnosing anxiety.

In recent years, the fabrication of devices that can facilitate the difficulty of communication between deaf people and the general public and translate sign language has attracted interest from researchers. But problems such as low accuracy and calculation speed and the high cost of tools have hindered the commercialization of research. Another challenge in making a practical tool is the necessity of good performance of the methods in the perspective of training by leave-one-subject-out or in other words classifying the data of a new person. Therefore, in this article, an efficient method for detecting hand gestures with the purpose of sign language translation has been presented, so that while using a method with lower dimensions, better performance can be obtained in all kinds of training methods. In the proposed method, the features consisting of the mean absolute value, variance, root mean square, waveform length, kurtosis, and skewness have been extracted from the empirical wavelet transformation of the electromyogram and inertial signals. Then, by the ReliefF method, effective features have been selected and for the classification of hand gestures, a support vector machine classifier has been used. The accuracy percentages of the proposed method on the PSL database and DB2, DB3, DB5, and DB7 datasets of the NinaPro database, have been respectively obtained as follows: 99.31%, 97.11%, 96.58%, 96.12%, and 97.32% in the word-subject training approach, 99.78%, 97.22%, 95.46%, 97.23%, and 97.72% in the word-all-subject training approach, and 97.43%, 94.68%, 89.66%, 91.55%, and 94.81% in the leave-one-subject-out method.

Biomechanical modeling of human joints has been considered for a long time by researchers due to its high importance and application. Therefore, methods of modeling joints, and kinematic and dynamic analysis of human movement have continuously been developing. In this paper, a biomechanical human knee model is developed, and a generic procedure for dynamic analysis of contact problems in combination with the musculoskeletal model is introduced. The development of this knee dynamic model includes the geometric expression of collision curves and an algorithm for determining collision points. This presentation addresses cartilage penetration depth and contact force calculation through nonlinear discontinuous contact law. Therefore, the femur and tibia's relative motion is modeled through the combined collision reactions of cartilage and bone in the knee. Moreover, two knee models, the novel curve fitted-plane contact model, and the spherical-plane contact model, have been compared, and a personalized model has been developed for such cases as knee osteoarthritis. There is a difference (average 12%) between the results of the enhanced model and the sphere on the plane model in the cartilage penetration. In the simulation, maximum penetration depth in a healthy knee is reported to be 0.705mm, while in a 75% KOA is 0.521mm, including 0.5mm cartilage-cartilage contact and 0.021mm bone-bone contact. The contact force is not increased in KOA despite the general belief. The cartilage penetration depth exceeds cartilage thickness, and the bone-bone contact leads to pain. It is a suitable tool for the analysis and control of the auxiliary device in order to control the relative motion of the tibia femur and their separation in patients with osteoarthritis of the knee.

Automatic detection of cardiac arrhythmias is very important for the successful treatment of heart disease and machine learning is used for this purpose. To correctly classify arrhythmic classes, it is important to extract the appropriate features to distinguish between different classes. In this paper, a deep convolutional neural network is used to extract the feature. Due to the fact that the heart rates of different patients are very different, arrhythmia classes will have many intra-class changes. To reduce intra-class changes, each patient's heart rate is mapped with a dedicated function to increase its resemblance to the heart rate of one of the training patient data’s. The proposed specific mapping reduces intra-class changes and significantly increases the classification accuracy of cardiac arrhythmias. To prove the effectiveness of the proposed method, its results were compared with several new studies based on three criteria for accuracy, sensitivity and specificity and on the same data set. The accuracy obtained is about 96.24%, which shows the better performance of the proposed method compared to other works.

In a varus knee, the load balance on two sides of the knee joint is disturbed and the stress applied to the medial side of the joint will be greater than that of a healthy knee. Such a case is often progressive and gradually leads to wear and pain. In this study, the finite element model for two 3D samples of healthy knee joint and varus knee in normal standing position was generated from MRI images and after loading and solving the problem, the stress distribution status in menisci and cartilage is obtained for both modes. The obtained results show the difference in maximum stresses and the difference in the shape of stress distribution areas. The results also show that the maximum values of von Mises stress and also the contact pressure in the inner area of the knee for the varus knee are much higher values compared to a healthy knee. In the standing position, the maximum contact pressure in the inner area of the joint, under a 400 Newtons load applied to the upper end of the femoral head, was obtained 4.527 and 7.821 MPa for a healthy knee and varus, respectively. For maximum values of von Mises stress, 2.821 and 6.501 MPa was obtained respectively.Due to the results and differences in stresses, the need for surgery to balance the stresses and loads on two sides of the knee is essential for a patient with varus knees. The amount of correction can be determined in addition to examining the joint geometry by examining the differences in stresses on both sides of the joint in a more accurate way.

Teeth, as one of the most important organs of the body, play an essential role in beauty and one of the best ways to treat its disorder and damage to date is the use of orthodontics. Smart wires are widely used in orthodontics for tidying teeth and for reasons such as superelastic behavior, corrosion resistance, high fatigue life, good compatibility and reversible strain. In this paper, the finite element method was used to investigate the mechanical behavior of smart orthodontic wires according to the standard by applying tensile force and considering their metallurgical properties. The macroscopic model for describing the properties of matter was based on Helmholtz thermodynamic free energy. The results showed that: With a 10°C changes in clinical temperature from 26°C to 36°C, the upper and lower plateau stresses and the strain elastic energy decreased about 10% and 18.28% respectively. Also with 10°C change in clinical temperature from 36°C to 46°C, the upper and lower plateau stresses and the strain elastic energy increased approximately 18% and 47.26% respectively. In conclusion, the smart orthodontic wire due to the lower level of the difference between the upper and lower plateau stresses, less elastic strain energy, hysteresis loop and complete dependent on superelastic behavior and high correlation of experimental and numerical results related to force-strain changes showed better performance. This numerical study can provide a method to study the mechanical behavior of smart orthodontic wires with respect to the effects of metallurgical and mechanical properties for the effectiveness of the length of treatment in the tooth.