Balance-energy of resting state network in obsessive-compulsive disorder.

Stability of the brain functional network is directly linked to organization of synchronous and anti-synchronous activities. Nevertheless, impact of arrangement of positive and negative links called links topology requires to be well understood. In this study, we investigated how topology of the functional links reduce balance-energy of the brain network in obsessive-compulsive disorder (OCD) and push the network to a more stable state as compared to healthy controls. Therefore, functional associations between the regions were measured using the phase synchrony between the EEG activities. Subsequently, balance-energy of the brain functional network was estimated based on the quality of triadic interactions. Occurrence rates of four different types of triadic interactions including weak and strong balanced, and unbalanced interactions were compared. In addition, impact of the links topology was also investigated by looking at the tendency of positive and negative links to making hubs. Our results showed although the number of positive and negative links were not statistically different between OCD and healthy controls, but positive links in OCDs' brain networks have more tendency to make hub. Moreover, lower number of unbalanced triads and higher number of strongly balanced triad reduced the balance-energy in OCDs' brain networks that conceptually has less requirement to change. We hope these findings could shed a light on better understanding of brain functional network in OCD.

Automatic detection of code-modulated visual evoked potentials using novel covariance estimators and short-time EEG signals.

BACKGROUND AND OBJECTIVE: Over the last years, code-modulated visual evoked potentials (cVEP)-based brain-computer interfaces (BCIs) have been developed as robust and non-invasive tools to construct high-speed communication systems. Recently, beamforming-based algorithms have extensively been used in cVEP-based BCI systems because of the need for short-time stimulation and less training data. One of the main drawbacks of the beamforming-based approaches is that their performance highly depends on estimating data covariance matrix and calculating activation patterns. METHODS: In the present study, two novel covariance estimators (i.e., the modified convex combination (MCC) and the maximum likelihood (ML) techniques) are proposed to estimate a robust and more reliable covariance matrix. In the ML method, a new sparsity constraint is considered to express the specific eigendecomposition of the covariance matrix as a sparse matrix transform (SMT). Then, the SMT is calculated using the product of pairwise coordinate rotations. These rotations can be constructed by a cross-validation method. Two stimulation presentation rates of 60 and 120 Hz are used for the coding sequence. RESULTS: Both of the suggested approaches (i.e., the MCC and SMT-based techniques) can efficiently improve the performance of the conventional spatiotemporal beamforming-based methods by providing a robust estimate of the covariance matrix in short stimulation times. Based on the experimental results, it can be concluded that the proposed SMT and MCC methods achieve the best results for the 60 and 120 Hz stimulus presentation rates, respectively. However, for both stimulus presentation rates, the proposed SMT and MCC-based methods remarkably outperform other state-of-the-art methods in cVEP-based BCI, such as conventional spatiotemporal beamforming and optimized support vector machines (SVM). Also, the results showed that the 120 Hz stimulus presentation rate provided faster communication. This procedure is performed by obtaining a maximal Information Transfer Rate (ITR) of 187.38 bits/minute. CONCLUSION: Finally, the present study suggested that the proposed MCC and SMT-based techniques could automatically detect the gazed targets. Also, these methods could be used as non-invasive alternatives over conventional methods.

Classification of code-modulated visual evoked potentials using adaptive modified covariance beamformer and EEG signals.

OBJECTIVE: In general, brain computer interface (BCI) studies based on code-modulated Visual Evoked Potentials (c-VEP) use m-sequences to decode EEG responses to visual stimuli. BCI systems based on the c-VEP paradigm can simultaneously present a large number of commands, which results in a significantly high information transfer rate (ITR). Spatiotemporal beamforming (STB) is one of the commonly used approaches in c-VEP-based BCI systems. APPROACH: In the current work, a novel STB-based technique is proposed to detect the gazed targets. The proposed method improves the performance of conventional STB-based techniques by providing a robust estimation of the covariance matrix in short stimulation times. Different user parameter-free methods, including the convex combination (CC), the general linear combination (GLC), and the modified versions of these techniques, are used to estimate a reliable and robust covariance matrix when a small number of repetitions are available. MAIN RESULTS: The stimulus presentation rate of 120 Hz is used to assess the performance of the proposed structures. Our proposed methods improved the classification accuracy by an average of 20% compared to the conventional STB method at the shortest stimulation time. The proposed method achieves an average ITR of 157.07 bits/min by using only two repetitions of the m-sequences. SIGNIFICANCE: The results show that our proposed methods perform significantly better than the conventional STB technique in all stimulation times.

Automatic detection of epileptic seizures using Riemannian geometry from scalp EEG recordings.

This paper proposes a new framework for epileptic seizure detection using non-invasive scalp electroencephalogram (sEEG) signals. The major innovation of the current study is using the Riemannian geometry for transforming the covariance matrices estimated from the EEG channels into a feature vector. The spatial covariance matrices are considered as features in order to extract the spatial information of the sEEG signals without applying any spatial filtering. Since these matrices are symmetric and positive definite (SPD), they belong to a special manifold called the Riemannian manifold. Furthermore, a kernel based on Riemannian geometry is proposed. This kernel maps the SPD matrices onto the Riemannian tangent space. The SPD matrices, obtained from all channels of the segmented sEEG signals, have high dimensions and extra information. For these reasons, the sequential forward feature selection method is applied to select the best features and reduce the computational burden in the classification step. The selected features are fed into a support vector machine (SVM) with an RBF kernel to classify the feature vectors into seizure and non-seizure classes. The performance of the proposed method is evaluated using two long-term scalp EEG (CHB-MIT benchmark and private) databases. Experimental results on all 23 subjects of the CHB-MIT database reveal an accuracy of 99.87%, a sensitivity of 99.91%, and a specificity of 99.82%. In addition, the introduced algorithm is tested on the private sEEG signals recorded from 20 patients, having 1380 seizures. The proposed approach achieves an accuracy, a sensitivity, and a specificity of 98.14%, 98.16%, and 98.12%, respectively. The experimental results on both sEEG databases demonstrate the effectiveness of the proposed method for automated epileptic seizure detection, especially for the private database which has noisier signals in comparison to the CHB-MIT database. Graphical Abstract Block diagram of the proposed epileptic seizure detection algorithm.

Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals.

BACKGROUND AND OBJECTIVE: Epilepsy is a prevalent disorder that affects the central nervous system, causing seizures. In the current study, a novel algorithm is developed using electroencephalographic (EEG) signals for automatic seizure detection from the continuous EEG monitoring data. METHODS: In the proposed methods, the discrete wavelet transform (DWT) and orthogonal matching pursuit (OMP) techniques are used to extract different coefficients from the EEG signals. Then, some non-linear features, such as fuzzy/approximate/sample/alphabet and correct conditional entropy, along with some statistical features are calculated using the DWT and OMP coefficients. Three widely-used EEG datasets were utilized to assess the performance of the proposed techniques. RESULTS: The proposed OMP-based technique along with the support vector machine classifier yielded an average specificity of 96.58%, an average accuracy of 97%, and an average sensitivity of 97.08% for different types of classification tasks. Moreover, the proposed DWT-based technique provided an average sensitivity of 99.39%, an average accuracy of 99.63%, and an average specificity of 99.72%. CONCLUSIONS: The experimental findings indicated that the proposed algorithms outperformed other existing techniques. Therefore, these algorithms can be implemented in relevant hardware to help neurologists with seizure detection.

Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal.

BACKGROUND AND OBJECTIVE: This paper addresses the automated recognition of obstructive sleep apnea (OSA) from the analysis of single-lead ECG signals. This is one of the most important problems that is, critical to the realization of monitoring patients with sleep apnea. METHODS: In the present study, a novel solution based on autoregressive (AR) modeling of the single-lead ECG, and spectral autocorrelation function as an ECG feature extraction method is presented. The more effective features are opted by sequential forward feature selection (SFFS) technique and fed into the random forest for binary classification between the apnea and normal events. RESULTS: Experimental results on Apnea-ECG database proved that the introduced algorithm resulted in an accuracy of 93.90% (sensitivity of 92.26% and specificity of 94.92%) in per-segment classification, which outperforms the other cutting-edge automatic OSA recognition techniques. Moreover, the proposed algorithm provided an accuracy of 97.14% (sensitivity of 95.65% and specificity of 100%) in discrimination of apnea patients from the normal subjects, which is comparable to the traditional and existing approaches. CONCLUSIONS: This study suggests that automatic OSA recognition from single-lead ECG signals is possible, which can be used as an inexpensive and low complexity burden alternative to more conventional methods such as Polysomnography.

Overcoming Challenges of Implementing Chronic Care Model in Diabetes Management: An Action Research Approach.

BACKGROUND: Reforming the health care system to improve suitable health care model for diabetic patients is essential. This study aimed to implement, identify, and overcome the challenges of implementing the Chronic Care Model in diabetes management in a clinic. METHODS: This study is a qualitative technical action research with the Kemmis and McTaggart model including planning, action, reflection, observation, and revision plan which was conducted in the specialized polyclinic from 2015 to 2017 in Isfahan city - Iran. Data were gathered through qualitative and quantitative methods. Diabetes team and 17 patients with type 2 diabetes participated in semi-structured interviews that were purposively chosen. Qualitative data were analyzed using content analysis and then quantitative data collected. RESULTS: The qualitative findings of this research are in five main categories: System design upgrade, self-management upgrade, decision support, health care organization, and clinical information system upgrade. Results of quantitative data showed that most metabolic indicators like HbA1c have statistical meaningful changes (P value < 0.05). CONCLUSIONS: Implementing the Chronic Care Model became feasible despite serious challenges and two groups of ready and active team and active patients were developed. The study showed that one important lost link of diabetes management is underestimating the nurses' capabilities in the management of this disease. Inevitably, serious investment on maximum use of nurses' knowledge and skills in improving diabetes management will help diabetes care upgrade significantly.

Modulation of Cortical Activity by Selective Steady-State Somatosensory Stimulation.

Phantom limb pain (PLP) represents a major debilitating condition for amputees. No effective therapy has been reported. Non-painful surface electrical stimulation may induce temporary significant alleviation of PLP. Preliminary results of a study attempting to design a methodology for delivery and evaluation of possible quantifiable effects at the cortical level of steady-state surface stimulation are presented for two healthy subjects. Somatosensory evoked potentials (SEP) were evaluated before and after delivery of a steady-state stimulus applied at wrist along the median nerve. Characterization of evoked sensation induced in hand by the steady-state stimuli was performed. The sensory input artificially generated by the steady-state stimuli influenced cortical activation reflected in changes in N1 and P2 components of SEP. N1 suppression and changes in P2 amplitude after steady state stimulation between 1 to 7 minutes were observed. Analysis of changes in SEP components in a larger population may contribute to defining markers of temporary cortical plastic changes driven by steady-state stimuli possibly assessing the efficacy of these stimuli when attempting to reverse cortical plastic changes following amputation and relief of PLP upon specific delivery through surface electrical stimulation in the periphery.

Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features From Single-Lead ECG Signal.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder and highly affects the quality of human life. Currently, gold standard for OSA detection is polysomnogram. Since this method is time consuming and cost inefficient, practical systems focus on the usage of electrocardiogram (ECG) signals for OSA detection. In this paper, a novel automatic OSA detection method using a single-lead ECG signal has been proposed. A nonlinear feature extraction using wavelet transform (WT) coefficients obtained by an ECG signal decomposition is employed. In addition, different classification methods are investigated. ECG signals are decomposed into eight levels using a Symlet function as a mother Wavelet function with third order. Then, the entropy-based features including fuzzy/approximate/sample/correct conditional entropy as well as other nonlinear features including interquartile range, mean absolute deviation, variance, Poincare plot, and recurrence plot are extracted from WT coefficients. The best features are chosen using the automatic sequential forward feature selection algorithm. In order to assess the introduced method, 95 single-lead ECG recordings are used. The support vector machine classifier having a radial basis function kernel leads to an accuracy of 94.63% (sensitivity: 94.43% and specificity: 94.77%) and 95.71% (sensitivity: 95.83% and specificity: 95.66%) for minute-by-minute and subject-by-subject classifications, respectively. The results show that applying entropy-based features for extracting hidden information of the ECG signals outperforms other available automatic OSA detection methods. The results indicate that a highly accurate OSA detection is attained by just exploiting the single-lead ECG signals. Furthermore, due to the low computational load in the proposed method, it can easily be applied to the home monitoring systems.

Service quality of private hospitals: the Iranian patients' perspective.

BACKGROUND: Highly competitive market in the private hospital industry has caused increasing pressure on them to provide services with higher quality. The aim of this study was to determine the different dimensions of the service quality in the private hospitals of Iran and evaluating the service quality from the patients' perspective. METHODS: A cross-sectional study was conducted between October and November 2010 in Tehran, Iran. The study sample was composed of 983 patients randomly selected from 8 private general hospitals. The study questionnaire was the SERVQUAL questionnaire, consisting of 21 items in service quality dimensions. RESULTS: The result of factor analysis revealed 3 factors, explaining 69% of the total variance. The total mean score of patients' expectation and perception was 4.91(SD = 0.2) and 4.02(SD = 0.6), respectively. The highest expectation and perception related to the tangibles dimension and the lowest expectation and perception related to the empathy dimension. The differences between perception and expectation were significant (p < 0.001). There was a significant difference between the expectations scores based on gender, education level, and previous hospitalization in that same hospital. Also, there was a significant difference between the perception scores based on insurance coverage, average length of stay, and patients' health conditions on discharge. CONCLUSION: The results showed that SERVQUAL is a valid, reliable, and flexible instrument to monitor and measure the quality of the services in private hospitals of Iran. Our findings clarified the importance of creating a strong relationship between patients and the hospital practitioners/personnel and the need for hospital staff to be responsive, credible, and empathetic when dealing with patients.