Multimodal insights into granger causality connectivity: Integrating physiological signals and gated eye-tracking data for emotion recognition using convolutional neural network.

This study introduces a groundbreaking method to enhance the accuracy and reliability of emotion recognition systems by combining electrocardiogram (ECG) with electroencephalogram (EEG) data, using an eye-tracking gated strategy. Initially, we propose a technique to filter out irrelevant portions of emotional data by employing pupil diameter metrics from eye-tracking data. Subsequently, we introduce an innovative approach for estimating effective connectivity to capture the dynamic interaction between the brain and the heart during emotional states of happiness and sadness. Granger causality (GC) is estimated and utilized to optimize input for a highly effective pre-trained convolutional neural network (CNN), specifically ResNet-18. To assess this methodology, we employed EEG and ECG data from the publicly available MAHNOB-HCI database, using a 5-fold cross-validation approach. Our method achieved an impressive average accuracy and area under the curve (AUC) of 91.00 % and 0.97, respectively, for GC-EEG-ECG images processed with ResNet-18. Comparative analysis with state-of-the-art studies clearly shows that augmenting ECG with EEG and refining data with an eye-tracking strategy significantly enhances emotion recognition performance across various emotions.

Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI).

The rotator cuff tear is a common situation for basketballers, handballers, or other athletes that strongly use their shoulders. This injury can be diagnosed precisely from a magnetic resonance (MR) image. In this paper, a novel deep learning-based framework is proposed to diagnose rotator cuff tear from MRI images of patients suspected of the rotator cuff tear. First, we collected 150 shoulders MRI images from two classes of rotator cuff tear patients and healthy ones with the same numbers. These images were observed by an orthopedic specialist and then tagged and used as input in the various configurations of the Convolutional Neural Network (CNN). At this stage, five different configurations of convolutional networks have been examined. Then, in the next step, the selected network with the highest accuracy is used to extract the deep features and classify the two classes of rotator cuff tear and healthy. Also, MRI images are feed to two quick pre-trained CNNs (MobileNetv2 and SqueezeNet) to compare with the proposed CNN. Finally, the evaluation is performed using the 5-fold cross-validation method. Also, a specific Graphical User Interface (GUI) was designed in the MATLAB environment for simplicity, which allows for testing by detecting the image class. The proposed CNN achieved higher accuracy than the two mentioned pre-trained CNNs. The average accuracy, precision, sensitivity, and specificity achieved by the best selected CNN configuration are equal to 92.67%, 91.13%, 91.75%, and 92.22%, respectively. The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder MRI.

Effects of COVID-19-related psychological distress and anxiety on quality of sleep and life in healthcare workers in Iran and three European countries.

Introduction: The COVID-19 pandemic has considerably affected human beings most of whom are healthcare workers (HCWs) combating the disease in the front line. Methods: This cross-sectional study aims to explore the effects of stress and anxiety caused by COVID-19 on the quality of sleep and life in HCWs, including physicians, nurses, and other healthcare staff. In this global study, we asked 1,210 HCWs (620 and 590 volunteers from Iran and European countries, including Germany, the Netherlands, and Italy, respectively), who age 21-70, to participate in the test. Several measures of COVID-related stress, anxiety, sleep, and life quality, including the 12-item General Health Questionnaire (GHQ-12), Fear of COVID-19 scale (FCV-19S), Beck Anxiety Inventory (BAI), the Pittsburgh Sleep Quality Index (PSQI), and World Health Organization Quality of Life-BREF (WHOQOL-BREF) are recorded. Results: Volunteers reported high rates of stress and anxiety and poor sleep quality as well as lower quality of life. The correlation analysis between the measures is reported. According to the results, regardless of the location, HCWs, predominantly female nurses, developed anxiety and stress symptoms which consequently resulted in lower sleep and life quality. Both for Iranian and the European HCWs, significant differences existed between nurses and the other two groups, with the p-values equal to 0.0357 and 0.0429 for GHQ-12, 0.0368, and 0.714 for BAI measure. Even though nurses reported the most stress, anxiety, fear of COVID-19, lower quality of life and sleep in both countries, and also an increase in other measures as well, there existed no statistically significant difference in FCV-19S, PSQI, and WHOQOL-BREF. Discussion: This study helps to expand our knowledge the effects of pandemics on HCWs and also for healthcare management to predict HCW's mental health conditions in similar situations.

EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms.

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.

Epileptic seizure prediction based on features extracted from lagged Poincaré plots.

OBJECTIVE: The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR). METHODS: In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points. RESULTS: The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor. CONCLUSION: The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.

Prediction of obstructive sleep apnea using ensemble of recurrence plot convolutional neural networks (RPCNNs) from polysomnography signals.

Obstructive Sleep Apnea (OSA) is a common disorder characterized by periodic cessation of breathing during sleep. OSA affects daily life and poses a severe threat to human health. The standard clinical method for identifying and predicting OSA events is the use of Polysomnography signals. In this paper, a novel scheme based on an ensemble of recurrence plots (RPs) and pre-trained convolutional neural networks (RPCNNs) is proposed to improve the prediction rate of OSA. First, RPs were used to represent the dynamic behavior of single electroencephalogram (EEG) and electrocardiogram (ECG) signals for 60 s before and during OSA events. Then, using RPs, three prompt CNNs named ResNet-50 were fine-tuned, and their classification results were fused via the Majority Voting (MV) method to produce a final result concerning prediction. Next, the subject-independent Leave-One-Subject-Out Cross-Validation (LOSO-CV) and subject-dependent 10-fold Cross-Validation (10-fold CV) methods were used to validate the prediction rate from signals derived from the University College Dublin Sleep Apnea Database. Finally, the highest achieved average accuracy for the fusion level was 91.74% and 89.45% at the 10-fold CV and LOSO-CV. Additionally, our results outperformed state-of-the-art findings and could be recommended to predict and detect other biomedical signals. As a result, this predictive system can also be used to adjust the air pressure in sleep apnea patients' Automatic Positive Airway Pressure (APAP) devices.

Intelligent Oscillometric System for Automatic Detection of Peripheral Arterial Disease.

Peripheral arterial disease (PAD) is a progressing arterial disorder that is associated with significant morbidity and mortality. The conventional PAD detection methods are invasive, cumbersome, or require expensive equipment and highly trained technicians. Here, we propose a new automated, noninvasive, and easy-to-use method for the detection of PAD based on characterizing the arterial system by applying an external varying pressure using a cuff. The superposition of the internal arterial pressure and the externally applied pressure were measured and mathematically modeled as a function of cuff pressure. A feature-based learning algorithm was then designed to identify PAD patterns by analyzing the parameters of the derived mathematical models. Genetic algorithm and principal component analysis were employed to select the best predictive features distinguishing PAD patterns from normal. A RUSBoost ensemble model using neural network as the base learner was designed to diagnose PAD from genetic algorithm selected features. The proposed method was validated on data collected from 14 PAD patients and 19 healthy individuals. It achieved a high accuracy, sensitivity, and specificity of 91.4%, 90.0%, and 92.1%, respectively, in detecting PAD. The effect of age, a confounding factor that may have impacted our analyzes, was not considered in this study. The proposed method shows promise toward noninvasive and accurate detection of PAD and can be integrated into routine oscillometric blood pressure measurements.

Is It Possible to Determine the Level of Spiritual Well-Being by Measuring Heart Rate Variability During the Reading of Heavenly Books?

A previous study stated that reading holy books can make a meaningful change in heart rate variability (HRV). The purpose of this study was to test the effect of reading the Quran, the heavenly religious book of the Muslim people in the Arabic language, on the Farsi (Persian)-speaking Muslims with various levels of spiritual well-being (SWB). In addition, novel to this study was the assessment of whether or not it is possible to use HRV features to distinguish individuals with high SWB from those with medium SWB. First, a questionnaire was completed by 31 volunteers to measure their SWB. Baseline ECG measurements were recorded during the resting stage. The volunteers were then asked to read the Quran for 5 min while ECG was recorded again. HRV indexes were calculated and four features were extracted and analyzed based on their correlation with the different levels of SWB. Independent t-tests were conducted and the results established a significant difference in these four features between high SWB and medium SWB groups, during the reading stage. Subsequently, with the use of these four HRV features, an artificial neural network and a decision tree were designed to classify the levels of SWB in volunteers. The outcome of this study demonstrated that it is possible to evaluate the level of SWB in individuals while they are reading the Quran.

Gender-Related Differences in Heart Rate Variability of Epileptic Patients.

Recently, heart rate variability (HRV) analysis has been used as an indicator of epileptic seizures. As women have a lower sudden, unexpected death in epilepsy risk and greater longevity than men, the authors postulated that there are significant gender-related differences in heart rate dynamics of epileptic patients. The authors analyzed HRV during 5-minute segments of continuous electrocardiogram recording of age-matched populations. The middle-aged epileptic patients included males ( n = 12) and females ( n = 12), ranging from 41 to 65 years of age. Relatively high- (0.15 Hz-0.40 Hz) and low-frequency (0.01 Hz-0.15 Hz) components of HRV were computed using spectral analysis. Poincaré parameters of each heart rate time series were considered as nonlinear features. The mean heart rate markedly differed between gender groups including both right- and left-sided seizures. High-frequency heart rate power and the low-frequency/high-frequency ratio increased in the pre-ictal phase of both male and female groups ( p < .01), but men showed more increase especially in right-sided seizures. The standard deviation ratio, SD2/ SD1, of pre-ictal phase was greater in males than females ( p < .01). High-frequency spectral power and parasympathetic activity were higher in the female group with both right- and left-sided seizures. Men showed a sudden increase in sympathetic activity in the pre-ictal phase, which might increase the risk of cardiovascular disease in comparison to women. These complementary findings indicate the need to account for gender, as well as localization in HRV analysis.