فصلنامه مهندسی پزشکی زیستی
سال شانزدهم شماره 4 (زمستان 1401)

An abdominal aortic aneurysm is a gradual enlargement of the diameter of the aorta, which can threaten the patient's life if it ruptures. Several factors are effective in reducing aneurysm rupture risk and behavior. One of the important factors is the geometric characteristics of the aneurysm. It is necessary to examine the geometric characteristics (shape and maximum diameter) of abdominal aortic aneurysms for each patient to predict the risk of aneurysm rupture and its behavior. Growth and remodeling models based on the finite element method are tools for describing biological characteristics and predicting the progression of diseases such as abdominal aortic aneurysms. In this article, a stress-mediated growth and remodeling model was used to simulate different geometries of abdominal aortic aneurysms with the help of elastin damage function and collagen turnover. The simulation results emphasized the role of elastin damage on the geometrical changes of the aneurysm and the sensitivity of collagen turnover on wall stress distribution and expansion rate, so that with the change of the collagen rate from 0.07 to 0.04, the wall stress increased up to 300 kPa. The results showed that the stress distribution and local expansion correspond to the amount of elastin damage. The elastin damage function plays a key role in determining the location of the maximum diameter and in creating different forms of abdominal aortic aneurysms. Furthermore, time changes have a direct impact on elastin degradation. The remodeling of collagen, which was caused by increasing stress, compensated for the loss of elastin and controlled the expansion rate of the aneurysm. In the future, this computational model will have the ability to depict patient-specific abdominal aortic aneurysm growth with the help of the geometrical changes of the aneurysm, the amount of elastin damage, and collagen remodeling.

Considering that mathematical modeling is helpful in describing and analyzing the behavior of epidemic diseases. On the other hand, the accuracy and degree of freedom in modeling fractional order systems are more than that of integer order systems due to the presence of long-term memory property. This paper extends the existing integer order model of Covid-19 disease to fractional order systems using fractional order calculations.The proposed model’s positivity and bounded answers are proved using the invariant region theorem. Using the fixed point theory in Banach space, the existence and uniqueness of the solution of the proposed fractional order model are proved. The behavior of both integer and fractional order has been simulated and evaluated using real information published for Covid-19 in Thailand. The higher efficiency and accuracy of the proposed model of fractional order are confirmed in the simulation results.keywords: Covid-19, Fractional order Calculus, Mathematical modeling, The existence and uniqueness of the answer

The pathological effects of the tumor on the respiratory airway have always been the focus of researchers. So, these effects will lead to the suffocation of the patient in acute cases. This study presents a computational model to investigate the effect of a tumor on the airflow in the larynx area with the help of Ansys software. The presented model is able to numerically calculate the effect of tumor presence on airspeed and pressure in the upper air system. This study considered the simulation of steady airflow for exhalation in three respiratory flow rates of 15 L/min, 26 L/min, and 30 L/min. The maximum speed limit in the respiratory flow of L/min 15, L/min 26, and L/min 30, respectively, 6.26 m/s, 10.58 m/s, and 12.14 m/s, appears in the larynx. Also, the highest pressure occurs in the trachea, so the maximum pressure in the respiratory rate is 15 L/min, 26 L/min, and 30 L/min, respectively, equal 19.6 Pa, 51.01 Pa, and 65.8 Pa. On the other hand, most deformation occurs in the area of ​​narrowing of the respiratory tract. With the increase in the flow rate, the amount of deformation also increases. The maximum deformation on the wall at the respiratory flow rate of 15 L/min, 26 L/min, and 30 L/min is equal to 0.07mm, 0.2mm, and 0.27mm, respectively. Due to the presence of a tumor in this respiratory model, velocity and WSS reach their maximum in the larynx region. The presence of a tumor can gradually lead to airway obstruction. Moreover, the risk of airway obstruction increases even in a slight reduction in respiratory capacity. Providing a numerical model for the respiratory system can effectively lead to a better treatment approach.

One of the procedures for estimating fetal heart rate (FHR) is the use of an electrocardiogram (ECG). The ECG is a safe, inexpensive, and convenient method that can be used for remote monitoring, so maternal abdominal ECG recording (AECG) is used. The AECG signal, in addition to the fetal ECG (FECG), includes the maternal ECG (MECG), maternal or fetal muscle activity, fetal brain activity, and noise, making it difficult to estimate the fetal heart rate based on the abdominal signal. In this study, the fetal heart rate is estimated from the single-channel AECG signal utilizing non-negative matrix factorization (NMF). In this method, the short-time Fourier transform (STFT) is used to obtain time-frequency information of the abdominal signal. Next, the NMF utilizes the STFT matrix as input. The rows of the non-negative matrix resulting from the NMF contain the content of maternal, fetal, and noise, which are used to detect R-peak and FHR. It performs well when MECG and FECG amplitudes are close together, which is one of the advantages of this method. The robustness and performance of the proposed algorithm have been compared with other state-of-the-art single-channel approaches, including deep learning models, on two databases, ADFECGDB and PCDB. Statistical analysis demonstrates that the proposed method is capable of estimating FHR and R-peak accurately. As a result, the proposed method is suitable for long-term fetal monitoring.

Knee joint contact force (KCF) plays a significant role in the occurrence and progression of knee osteoarthritis (KOA) disease. KCF can be used in monitoring rehabilitation progress after knee arthroplasty surgery and the design of prostheses. Currently, measuring KCF is dependent on the data extracted from gait laboratories. The combination of artificial neural networks (ANNs) and wearable technology can overcome the limitations imposed by lab-based analysis in measuring KCF. Therefore, the present study aimed to investigate the potential of a fully-connected neural network (FCNN) in predicting the KCF via three inertial measurement unit (IMU) sensors attached to the pelvis, thigh, and shank segments. Ten healthy male volunteers participated in this study. The 3D marker trajectories and ground reaction forces (GRF) were captured at 200 Hz and 1000 Hz sampling frequencies during level-ground walking. Using a generic OpenSim model, the KCF was estimated through static optimization. The resultant KCF estimated by the musculoskeletal model was then used as the target of the neural network, while linear acceleration and 3D angular velocity data captured by three IMUs were considered as the network inputs. The network performance was investigated at intra- and inter-subject levels. Based on our findings, the proposed network of this study enables the prediction of KCF with 89% and 79% accuracy (based on the Pearson correlation coefficient) at the intra- and inter-subject levels, respectively. The results of this study promise the possibility of using IMU sensors in predicting KCF outside the lab and during daily activities.

Analysis and examination of sound of organs can be utilized in order to diagnose various diseases and abnormal conditions. Diagnostic methods based on audio signal processing are non-invasive and inexpensive and can be especially useful in under-developed countries, where inadequate medical specialists and equipment has led to high fatality rates. Development of accessible methods based on machine learning can aid with early diagnosis. we used a convolutional network to attain the advantages of transfer learning. In previous studies, models have been proposed that feed spectrograms with frequency characteristics as inputs to the convolutional network. In this article, we propose a model which additionally employs a recurrent representation (Recurrence plot) that reflects the temporal characteristics of the sound. The audio data sequence is investigated by adding the temporal attention mechanism and the bi-directional recurrent gates for weighting data according to its informational value. Data used in this article is from the ICBHI lung sound database. The presented model was able to classify lung sounds into three categories: healthy, chronic obstructive pulmonary disease (COPD), and other diseases with an accuracy of 97%, which shows the superiority of the proposed method compared to results obtained from previous methods on the same database.

The chemical exchange due to saturation transfer by applying an electromagnetic radio frequency (RF) pulse to a magnetic resonance scanner is called the CEST effect. The CEST effect depends mainly on relaxation times, chemical exchange rate, concentration of the contrast agent and RF pulse properties. Dependence of chemical exchange rate on some clinical indicators such as pH, temperature and glucose consumption, allows diagnosis diseases non-invasively. The chemical exchange rate is determined through presenting new objective function of the CEST effect in the mathematical closed form quantitatively. A new description of the optimal amplitude of the rectangular RF pulse is obtained by applying gradient-based methods on the proposed convex objective function. Chemical exchange rate is proposed at the simple representation form independent to contrast agent by reversing the optimal amplitude description for large shift frequency contrast agents. Evaluation of the objective function and the proposed relations are performed by comparing them with valid methods derived solving Bloch-McConnell equations through parametric and real data. The mean relative square error of the objective function based on the parametric data is 7.25% and for the proposed the optimal amplitude and chemical exchange rate based on the real data are 6.3% and 4.2%, respectively.