Journal of Medical Signals and Sensors
Volume:12 Issue: 3, Jul-Sep 2022

The most significant motivations for designing multi-biometric systems are high-accuracy recognition, high-security assurances as well as overcoming the limitations like non-universality, noisy sensor data, and large intra-user variations. Therefore, choosing data for fusion is of high significance for the design of a multimodal biometric system. The feature vectors contain richer information than the scores, decisions and even raw data, thereby making feature-level fusion more effective than other levels.

In the proposed method, kernel is used for fusion in feature space. First, the face features are extracted using kernel-based methods, the features of both right and left irises are extracted using Hough Transform and Daugman algorithm methods, and the features of both thumb prints are extracted using the Gabor filter bank. Second, after normalization operations, we use kernel methods to map the feature vectors to a kernel Hilbert space where non-linear relations are shown as linear for the purpose of compatibility of feature spaces. Then, dimensionality reduction algorithms are used to the fusion of the feature vectors extracted from fingerprints, irises and the face. since the proposed system uses face, both right 7and left irises and right and left thumbprints, it is hybrid multi-biometric system. We c8arried out the tests on seven databases.

Our results show that the hybrid multimodal template, while being secure against spoof attacks and making the system robust, can use the dimensionality of only 15 features to increase the accuracy of a hybrid multimodal biometric system to 100%, which shows a significant improvement compared with unibiometric and other multimodal systems.

The proposed method can be used to search large databases. Consequently, a large database of a secure multimodal template could be correctly differentiated based on the corresponding class of a test sample without any consistency error.

Photoplethysmography (PPG) contains information about the health condition of the heart and blood vessels. Cardiovascular system modeling using PPG signal measurements can represent, analyze, and predict the cardiovascular system.

This study aims to make a cardiovascular system model using a Windkessel model by dividing the blood vessels into seven segments. This process involves the PPG signal of the fingertips and toes for further analysis to obtain the condition of the elasticity of the blood vessels as the main parameter. The method is to find the Resistance, Inductance, and Capacitance (RLC) value of each segment of the body through the equivalent equation between the electronic unit and the cardiovascular unit. The modeling made is focused on PPG parameters in the form of stiffness index, the time delay (∆t), and augmentation index.

The results of the model simulation using PSpice were then compared with the results of measuring the PPG signal to analyze changes in the behavior of the PPG signal taken from ten healthy people with an average age of 46 years, compared to ten cardiac patients with an average age of 48 years. It is found that decreasing 20% of capacitance value and the arterial stiffness parameter will close to cardiac patients’ data. Compared with the measurement results, the correlation of the PPG signal in the simulation model is more than 0.9.

The proposed model is expected to be used in the early detection of arterial stiffness. It can also be used to study the dynamics of the cardiovascular system, including changes in blood flow velocity and blood pressure.

Due to imprecise/missing data used for parameterization of ordinary differential equations (ODEs), model parameters are uncertain. Uncertainty of parameters has hindered the application of ODEs that require accurate parameters.

We extended an available ODE model of tumor‑immune system interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. The fuzzy ODE (FODE) model assigns a fuzzy number to the parameters, to capture parametric uncertainty. We used the FODE model to predict tumor and immune cell dynamics and to assess the efficacy of 5‑fluorouracil (5‑FU) chemotherapy.

FODE model investigates how parametric uncertainty affects the uncertainty band of cell dynamics in the presence and absence of 5‑FU treatment. In silico experiments revealed that the frequent 5‑FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and natural killer cells, and decreasing that of myeloid‑derived suppressor cells. The global sensitivity analysis was proved model robustness against random perturbation to parameters.

ODE models with fuzzy uncertain kinetic parameters cope with insufficient/imprecise experimental data in the field of mathematical oncology and can predict cell dynamics uncertainty band.

High radiation dose of patients has become a concern in the computed tomography (CT) examinations. The aim of this study is to guide the radiology technician in modifying or optimizing the underlying parameters of the CT scan to reduce the patient radiation dose and produce an acceptable image quality for diagnosis.

The body mass measurement device phantom was repeatedly scanned by changing the scan parameters. To analyze the image quality, software‑based and observer‑based evaluations were employed. To study the effect of scan parameters such as slice thickness and reconstruction filter on image quality and radiation dose, the structural equation modeling was used.

By changing the reconstruction filter from standard to soft and slice thickness from 2.5 mm to 5 mm, low‑contrast resolution did not change significantly. In addition, by increasing the slice thickness and changing the reconstruction filter, the spatial resolution at different radiation conditions did not significantly differ from the standard irradiation conditions (P > 0.05).

In this study, it was shown that in the brain CT scan imaging, the radiation dose was reduced by 30%–50% by increasing the slice thickness or changing the reconstruction filter. It is necessary to adjust the CT scan protocols according to clinical requirements or the special conditions of some patients while maintaining acceptable image quality

The purpose of this study is to evaluate the effective dose (ED) for computed tomography (CT) examination in different age groups and medical exposure in pediatric imaging centers in Tehran, Iran.

Imaging data were collected from 532 pediatric patients from four age groups subjected to three prevalent procedures. National Cancer Institute CT (NCICT) software was used to calculate the ED value.

The mean ED values were 1.60, 4.16, and 10.56 mSv for patients’ procedures of head, chest, and abdomen–pelvis, respectively. This study showed a significant difference of ED value among five pediatric medical imaging centers (P < 0.05). In head, chest, and abdomen–pelvis exams, a reduction in ED was evident with decreasing patients’ age.

As there were significant differences among ED values in five pediatric medical imaging centers, optimizing this value is necessary to decrease this variation. For head CT in infants and also abdomen–pelvis, further reduction in radiation exposure is required

COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease.

A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020.

We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%).

This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.

Previous research has shown that eye movements are different in patients with attention deficit hyperactivity disorder (ADHD) and healthy people. As a result, electrooculogram (EOG) signals may also differ between the two groups. Therefore, the aim of this study was to investigate the recorded EOG signals of 30 ADHD children and 30 healthy children (control group) while performing an attention‑related task.

Two features of approximate entropy (ApEn) and Petrosian’s fractal dimension (Pet’s FD) of EOG signals were calculated for the two groups. Then, the two groups were classified using the vector derived from two features and two support vector machine (SVM) and neural gas (NG) classifiers.

Statistical analysis showed that the values of both features were significantly lower in the ADHD group compared to the control group. Moreover, the SVM classifier (accuracy: 84.6% ± 4.4%, sensitivity: 85.2% ± 4.9%, specificity: 78.8% ± 6.5%) was more successful in separating the two groups than the NG (78.1% ± 1.1%, sensitivity: 80.1% ± 6.2%, specificity: 72.2% ± 9.2%).

The decrease in ApEn and Pet’s FD values in the EOG signals of the ADHD group showed that their eye movements were slower than the control group and this difference was due to their attention deficit. The results of this study can be used to design an EOG biofeedback training course to reduce the symptoms of ADHD patients.

Magnetic resonance (MR) image is one of the most important diagnostic tools for brain tumor detection. Segmentation of glioma tumor region in brain MR images is challenging in medical image processing problems. Precise and reliable segmentation algorithms can be significantly helpful in the diagnosis and treatment planning.

In this article, a novel brain tumor segmentation method is introduced as a postsegmentation module, which uses the primary segmentation method’s output as input and makes the segmentation performance values better. This approach is a combination of fuzzy logic and cellular automata (CA).

The BraTS online dataset has been used for implementing the proposed method. In the first step, the intensity of each pixel is fed to a fuzzy system to label each pixel, and at the second step, the label of each pixel is fed to a fuzzy CA to make the performance of segmentation better. This step repeated while the performance saturated. The accuracy of the first step was 85.8%, but the accuracy of segmentation after using fuzzy CA was obtained to 99.8%.

The practical results have shown that our proposed method could improve the brain tumor segmentation in MR images significantly in comparison with other approaches.