Relationship between white matter alterations and contamination subgroup in obsessive compulsive disorder: A diffusion tensor imaging study.

Approximately 2%-3% of the world population suffers from obsessive-compulsive disorder (OCD). Several brain regions have been involved in the pathophysiology of OCD, but brain volumes in OCD may vary depending on specific OCD symptom dimensions. The study aims to explore how white matter structure changes in particular OCD symptom dimensions. Prior studies attempt to find the correlation between Y-BOCS scores and OCD patients. However, in this study, we separated the contamination subgroup in OCD and compared directly to healthy control to find regions that exactly related to contamination symptoms. To evaluate structural alterations, diffusion tensor imaging was acquired from 30 OCD patients and 34 demographically matched healthy controls. Data were processed using tract-based spatial statistics (TBSS) analysis. First, by comparing all OCD to healthy controls, significant fractional anisotropy (FA) decreased in the right anterior thalamic radiation, right corticospinal tract, and forceps minor observed. Then by comparing the contamination subgroup to healthy control, FA decreases in the forceps minor region. Consequently, forceps minor plays a central role in the pathophysiology of contamination behaviors. Finally, other subgroups were compared to healthy control and discovered that FA in the right corticospinal tract and right anterior thalamic radiation is reduced.

An automated method for sleep apnoea detection using HRV.

The purpose of this article is to diagnose respiratory apnoea in order to help the person avoid further possible risks. In this article, the ECG signal of 70 patients with sleep apnoea in the Physionet database with a sampling rate of 100 Hz is used. Data recording time is 7 to 10 h, the age range is 27 to 60 years, and weighs between 53 to 135 kg. In this article, using electrocardiogram signal processing, the time of occurrence of a respiratory attack on the patient during sleep is predicted. In order to achieve this goal, after generating the HRV signal from the ECG, time and frequency domain properties are extracted from the HRV signal. In the next step, according to statistical analysis, principal component analysis algorithm, and genetic algorithm, the best combination of features is selected in terms of differentiation between two different groups. In order to evaluate the capability of each feature in distinguishing between two attack and non-attack event intervals, the features are compared separately and in combination. The results show that in the HRV signal of people at risk for sleep apnoea, there are features in the vicinity of the attack that distinguish them from times far away from the attack. It was also shown that the feature combination method has a much greater ability to reveal this difference. The results of specificity, sensitivity, and accuracy obtained by combining the features were 99.77%, 97.38%, and 98.25%, respectively, which has a much higher performance than previous studies. Early detection enables the physician and the intensive care unit to take steps to prevent this from happening, which will save the patient's life.

A Novel Clinical Method for Detecting Obstructive Sleep Apnea using of Nonlinear Mapping.

BACKGROUND: Due the long-time admission of patients in the ICU, it is very expensive. Therefore, solutions, which can increase the quality of care and decrease costs, can be helpful. OBJECTIVE: Separation of the patients based on the acute conditions can be useful in providing appropriate therapy. In this study, we present a classifier to predict the OSA based on heart rate variability of patients. MATERIAL AND METHODS: In this analytical study, we used the recorded ECG signals from PhysioNet Database. At first, in the preprocessing stage, the noise from the ECG signal was removed, and R spikes were detected to generate the HRV. The next stage was related to linear and non-linear features extraction. We used the paired sample t-test that is a statistical technique to compare two periods (apnea and non-apnea). These features were applied as the inputs of two different classifiers, including MLP and SVM to find the best method and distinguish patients with higher death risk. RESULTS: The results showed that the SVM classifier is more capable to separate the four periods seperated from each other. The sensitivity for detecting the OSA event was 95.46% and the specificity was 97.57% for the non-OSA period. CONCLUSION: Accurate and timely diagnosis of the disease can ensure the health of the individual, family, and community. Based on the proposed algorithm, the HRV signal and novel feature, presented in this study, had the highest specificity and sensitivity for the detection of the OSA event of the non-OSA, respectively.

Mathematical model to predict COVID-19 mortality rate.

Objective: Covid-19 is a highly contagious viral infection that has recently become a pandemic. Since the beginning of the pandemic, the disease has affected millions of people and taken many people's lives. The purpose of this paper is to predict and compare the number of cases and mortality rate due to Covid-19 every quarter in 2020 and 2021 in three countries: Iran, the United States, and South Korea. Materials and methods: The data of this study include the mortality rate of different countries of the world due to Covid-19, which has been approved by the World Health Organization (WHO). In this paper, to develop the mathematical model for mortality rate prediction, the data of the countries of Iran, the United States, and South Korea during the last two years from March 1, 2020, to March 1, 2022, have been used. In addition, the mortality trend was modeled using the MATLAB software toolbox version 2022b. During modeling, six methods including Fourier, Interpolant, Gaussian, Polynomial, Sum of Sine, and Smoothing Spline were implemented. Root Mean square error (RMSE) and final prediction error were used to evaluate the performance of these proposed methods. Results: As a result of the analysis, it was shown that the Smoothing Spline model with the lowest error rate was capable of accurately evaluating and predicting Covid-19 incidence and mortality rate. Using RMSE, a prediction of the Covid-19 mortality rate for three countries is 3.76498 × 10-5. The values of R-Square and Adj R-sq were 1 in all the experiments, which indicates the full compliance of the prediction model. Conclusion: Using the proposed method, the incidence rate and mortality rate can be properly assessed and compared with each other in three countries. This provides a better view of the progression of the coronavirus outbreak in spring, summer, autumn, and winter. By using the proposed method, governments will be able to prevent disease and alert people to follow health guidelines more closely, thereby reducing infection numbers and mortality rates.

Time-Frequency Analysis of Photopic Negative Response in CRVO Patients.

PURPOSE: The PhNR is driven by retinal ganglion cells (RGCs). Therefore, the function of RGCs could be objectively evaluated by analyzing the PhNR. The aim of this article is to determine the effect of central retinal vein occlusion (CRVO) on PhNR and RGCs performances. METHODS: Seventeen patients with CRVO were included. Full-field photopic ERGs, including PhNR, were recorded and compared with the fellow normal eyes. ERG signals were analyzed based on the standard time-domain analyses of the PhNR as well as a continuous wavelet transform (CWT) to extract time-frequency components that correspond to the PhNR using MATLAB. We obtained the main frequencies and their occurrence time from CWT. RESULTS: All a-wave, b-wave, and PhNR amplitudes of CRVO eyes showed a significant reduction compared to those of the fellow eyes (P < .01, P < .001, and P < .001, respectively). The peak times of a-wave, b-wave, and PhNR were increased significantly in the CRVO eyes (P = .04, P = .04, and P = .003, respectively). The dominant f3 frequency, which corresponds to the PhNR in CRVO patients, showed a more significant decrease (P < .001) compared to other dominant frequencies (f0, f1, and f2). The occurrence time of f3 (t3) was significantly higher in the CRVO eyes (P < .001). Time-domain of the PhNR was also affected in CRVO patients (P < .001). CONCLUSION: CWT allows quantifications of ERG responses, especially for PhNR. The PhNR was severely affected in CRVO eyes implicating loss of RGCs. CWT might demonstrate the severity of CRVO more precisely and identify diagnostically significant changes of ERG waveforms that are not resolved when the analysis is only limited to the time-domain measurements.

Presenting an efficient approach based on novel mapping for mortality prediction in intensive care unit cardiovascular patients.

Intensive care unit (ICU) experienced and skillful people in this field should be employed because the equipment, facilities, and admitted patients have more special conditions than other departments. Our goal provides the best quality according to the condition each patient and prevent many unnecessary costs for preventive treatment. In this paper, the proposed system will first receive the patient's vital signs, which are recorded by the ICU monitoring. After the necessary processing, in case of observing changes in the normal state, risk alarms are transmitted to the nursing station so that nurses become aware of this condition and take all equipment to return the patient to normal condition and prevent his death. The applied graph in this study examines patients at any moment and displays the patient's future condition in a schematic manner after precise analyses. In this algorithm, after calculating the R-R intervals in the electrocardiogram signal, RRIs are thrown into a risk plot (RP) by a projectile. Given the amount of projectile RRI, one of the stairs can host that amount. After a few moments by springs embedded under the stairs, the drain of RRIs is done by the kinetic energy stored in the springs towards the valley of life. If the accumulation of quantities in a stair is too much, the spring will not be able to project those RRIs. By examining this situation, we will introduce an index to determine the risk of death for all patients. The results of this paper show that when a person is in normal condition, there is no density in a certain stair and the ball or the projected RRIs are not limited to a stair. In general, the results of this paper show that the lower amount of RRI dispersion in the RP leads to greater risk of entry into the death range and as this amount decrease, an immediate consideration is required. In conclusion, if the precise prediction of the future condition of ICU patients is available to nurses and doctors, more facilities and equipment could be provided to save their lives. •We focused on nonlinear methods with new aspects to extract mentioned dynamics.•This method can reduce the number of ICU nurses and give the special facilities for high-risk patients.•Our results confirm that it is possible to predict mortality based on the dynamical characteristics of HRV.

Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient.

Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.