Journal of Medical Signals and Sensors
Volume:12 Issue: 2, Apr-Jun 2022

The world is experiencing another pandemic called COVID‑19. Several mathematical models have been proposed to examine the impact of health interventions in controlling pandemic growth.

In this study, we propose a fractional order distributed delay dynamic system, namely, EQIR model. In order to predict the outbreak, the proposed model incorporates changes in transmission rate, isolation rate, and identification of infected people through time varying deterministic and stochastic parameters. Furthermore, proposed stochastic model considers fluctuations in population behavior and simulates different scenarios of outbreak at the same time. Main novelty of this model is its ability to incorporate changes in transmission rate, latent periods, and rate of quarantine through time varying deterministic and stochastic assumptions. This model can exactly follow the disease trend from its beginning to current situation and predict outbreak future for various situations.

Parameters of this model were identified during fitting process to real data of Iran, USA, and South Korea. We calculated the reproduction number using a Laplace transform‑based method. Results of numerical simulation verify the effectiveness and accuracy of proposed deterministic and stochastic models in current outbreak.

Justifying of parameters of the model emphasizes that, although stricter deterrent interventions can prevent another peak and control the current outbreak, the consecutive screening schemes of COVID‑19 plays more important role. This means that the more diagnostic tests performed on people, the faster the disease will be controlled.

Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumor segmentation.

We present weight pruning (WP)‑UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating‑point computational complexity.

We trained and evaluated the model with CT images from 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e floating‑point operations, three times lower than those for other network models.

The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging

One of the most prevalent methods in noninvasive blood pressure (BP) measurement with cuff is oscillometric, which has two different types of deflation, including linear and step deflation. With this approach, in addition to designing a novel algorithm by the step deflation method, a sample of its module was constructed and validated during clinical tests in different hospitals.

In this study, by controlling the valve, the pressure would be deflated through optimized steps. By real‑time processing on the obtained signal from the pressure sensor, pulses in each step would be extracted. After that, in offline mode, mean arterial pressure is estimated based on curve fitting.

A BP simulator, various modules, and an auditory method were used to validate the algorithm and its results. During clinical tests, 80 people (men and women), 11 dialysis patients, and 69 non-dialysis (healthy or with other diseases) in the age range of 17–85 years participated.

The obtained results compared with the BP simulator are in the standard range according to the international medical standards of the British Hypertension Society (BHS) and the US Association for the Advancement of Medical Instrumentation (AAMI), which are the global standard of comparison in this field.

Breast cancer is a type of cancer that starts in the breast tissue and affects about 10% of women at different stages of their lives. In this study, we applied a new method to predict recurrence in biological networks made from gene expression data.

The method includes the steps such as data collection, clustering, determining differentiating genes, and classification. The eight techniques consist of random forest, support vector machine and neural network, randomforest + k‑means, hidden markov model, joint mutual information, neural network + k‑means and suportvector machine + k‑menas were implemented on 12172 genes and 200 samples.

Thirty genes were considered as differentiating genes which used for the classification. The results showed that random forest + k‑means get better performance than other techniques. The two techniques including neural network + k‑means and random forest + k‑means performed better than other techniques in identifying high risk cases.

Thirty of 12,172 genes are considered for classification that the use of clustering has improved the classification techniques performance.

Background: The objective of this study was to design and construct a CO2 incubator with nonmetallic walls and to investigate the viability of the cells and microwave irradiance inside this incubator. Methods: Because the walls of conventional incubators are made of metal, this causes scattering, reflection, and absorption of electromagnetic waves. We decided to build a nonmetallic wall incubator to examine cells under microwave radiation. Incubator walls were made using polyvinyl chloride and Plexiglas and then temperature, CO2 pressure, and humidity sensors were placed in it. Atmel® ATmega1284, a low‑power CMOS 8‑bit microcontroller, collects and analyzes the sensor information, and if the values are less or more than the specified limits, the command to cut off or connect the electric current to the heater or CO2 solenoid valve is sent. Using a fan inside the incubator chamber, temperature and CO2 are uniforms. The temperature of the points where the cell culture plates are placed was measured, and the temperature difference was compared. Ovarian cancer cells (A2780) were cultured in the hand‑made and commercial incubators at different times, and cell viability was compared by the MTT method. Microwave radiation in the incubator was also investigated using a spectrum analyzer. The survival of cells after microwave irradiation in the incubator was measured and compared with control cells. Results: The data showed that there was no significant difference in temperature of different points in hand‑made incubator and also there was no significant difference between the viability of cells cultured in the hand‑made and commercial incubators. The survival of irradiated cells in the incubator was reduced compared to control cells, but this reduction was not significant. Conclusion: This incubator has the ability to maintain cells and study the effects of electromagnetic radiations on the desired cells, which becomes possible by using this device

Auditing the treatment planning system (TPS) software for a radiotherapy unit is of paramount importance in any radiation therapy department. A Plexiglas phantom was proposed to measure the ionization of 60Co high dose rate (HDR) source and compare dose points in the planning system for auditing and verifying TPS.

Auditing was performed using a Plexiglas phantom in an end‑to‑end test, and relative dose points were detected by a farmer‑type ionization chamber and compared with the relative dose of similar points in TPS. The audit results were determined as pass optimal level (<3.3%), pass action level (between 3.3% and 5%), and out of tolerance (>5%).

The comparison of the collected data revealed that 80% of the measured values were ≤5% in the pass level, and 20% of the points were out of tolerance (between 5% and 6.99%).

This study documented the appropriateness of the dosimetry audit test and this phantom design for the HDR brachytherapy TPS.

The aim of the present study was to detect the prevalence of accidental pathological findings in asymptomatic maxillary sinuses in patients referred for head and neck cone‑beam computed tomography (CBCT) examination for varied reasons.

The present cross‑sectional study included a detailed analysis of CBCT scans of 150 patients aged between 18 and 70 years reporting for varied dental complaints for detecting accidental pathological findings in maxillary sinuses while the patients did not have any complaint pertaining to sinuses.

The findings of the present study revealed 58% patients to have pathological findings in maxillary sinuses while they were asymptomatic for sinuses. Furthermore, the prevalence of mucosal thickening was found in 29.3% of the patients while 36.7% patients presented with polypoidal mucosal thickening.

Higher prevalence of pathologies in asymptomatic maxillary sinuses found in the present study emphasized significance of a thorough examination of routine dental patients by dento‑maxillofacial radiologists with necessary investigations to be advised in the form of higher imaging modalities like CBCT, if necessary

When an epileptic seizure occurs, the neuron’s activity of the brain is dynamically changed, which affects the connectivity between brain regions. The connectivity of each brain region can be quantified by electroencephalography (EEG) coherence, which measures the statistical correlation between electrodes spatially separated on the scalp. Previous studies conducted a coherence analysis of all EEG electrodes covering all parts of the brain. However, in an epileptic condition, seizures occur in a specific region of the brain then spreading to other areas. Therefore, this study applies an energy‑based channel selection process to determine the coherence analysis in the most active brain regions during the seizure. This paper presents a quantitative analysis of inter‑ and intrahemispheric coherence in epileptic EEG signals and the correlation with the channel activity to glean insights about brain area connectivity changes during epileptic seizures. The EEG signals are obtained from ten patients’ data from the CHB‑MIT dataset. Pair‑wise electrode spectral coherence is calculated in the full band and five sub‑bands of EEG signals. The channel activity level is determined by calculating the energy of each channel in all patients. The EEG coherence observation in the preictal (Cohpre) and ictal (Cohictal) conditions showed a significant decrease of Cohictal in the most active channel, especially in the lower EEG sub‑bands. This finding indicates that there is a strong correlation between the decrease of mean spectral coherence and channel activity. The decrease of coherence in epileptic conditions (Cohictal <Cohpre) indicates low neuronal connectivity. There are some exceptions in some channel pairs, but a constant pattern is found in the high activity channel. This shows a strong correlation between the decrease of coherence and the channel activity. The finding in this study demonstrates that the neuronal connectivity of epileptic EEG signals is suitable to be analyzed in the more active brain regions.

Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions – Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA’s result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.

At image‑guided radiotherapy, technique, different imaging, and monitoring systems are utilized for (i) organs border detection and tumor delineation during the treatment planning process and (ii) patient setup and tumor localization at pretreatment step and (iii) real‑time tumor motion tracking for dynamic thorax tumors during the treatment. In this study, the effect of fuzzy logic is quantitatively investigated at different steps of image‑guided radiotherapy. Fuzzy logic‑based models and algorithms have been implemented at three steps, and the obtained results are compared with commonly available strategies. Required data are (i) real patients treated with Synchrony Cyberknife system at Georgetown University Hospital for real‑time tumor motion prediction, (ii) computed tomography images taken from real patients for geometrical setup, and also (iii) tomography images of an anthropomorphic phantom for tumor delineation process. In real‑time tumor tracking, the targeting error averages of the fuzzy correlation model in comparison with the Cyberknife modeler are 4.57 mm and 8.97 mm, respectively, for a given patient that shows remarkable error reduction. In the case of patient geometrical setup, the fuzzy logic‑based algorithm has better influence in comparing with the artificial neural network, while the setup error averages is reduced from 1.47 to 0.4432 mm using the fuzzy logic‑based method, for a given patient.Finally, the obtained results show that the fuzzy logic based image processing algorithm exhibits much better performance for edge detection compared to four conventional operators. This study is an effort to show that fuzzy logic based algorithms are also highly applicable at image‑guided radiotherapy as one of the important treatment modalities for tumor delineation, patient setup error reduction, and intrafractional motion error compensation due to their inherent properties.

The purpose of this study is to assess a rare case of fetal radiation absorbed dose here through 18F‑Fludeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in early pregnancy (5‑week‑old fetus). The fetal absorbed dose due to the radiation emitted from the mother’s body, the fetus self‑dose, and the dose received from CT were computed. The 35‑year‑old patient, weighing 85 kg, was injected with 370 MBq of 18F‑FDG. Imaging started at 1 h with CT acquisition followed by PET imaging. The photon and positron self‑dose was calculated by applying the Monte Carlo (MC) GATE (GEANT 4 Application for Tomographic Emission) code. The volume of absorbed dose from the mother’s body organs and the absorbed dose from the CT were added to the self‑dose to obtain the final dose. The volume of self‑dose obtained through MC simulation for the fetus was 3.3 × 10‑2 mGy/MBq, of which 2.97 × 10‑2 mGy/MBq was associated with positrons and 0.33 × 10‑2 mGy/MBq was associated with photons. Biologically, the absorbed dose from CT, 7.3 mGy, had to be added to the total dose. The absorbed dose by the fetus during early pregnancy was higher than the standard value of 2.2 × 10‑2 mGy/MBq (MIRD DER) because, during the examinations, the mother’s bladder was full. This issue was a concern during updating standards.