An automated drug dependence detection system based on EEG.

OBJECTIVE: Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser. METHODS: EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification. RESULTS: The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score. CONCLUSIONS AND SIGNIFICANCE: The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.

Evaluation of Drug Abuse on Brain Function using Power Spectrum Analysis of Electroencephalogram Signals in Methamphetamine, Opioid, Cannabis, and Multi-Drug Abuser Groups.

Background: The effect of different types of substances on brain function is still challenging; however, many studies have shown the functional and structural damage to the brain under influence of substance abuse. Objective: This study aimed to quantitatively compare the effect of opioid (Op), methamphetamine (Meth), cannabis (Can), and simultaneous methamphetamine and opioid (Multi-Drug (MD)) abuse on brain function. Furthermore, the impacts of pure Op and Meth abuse were considered with simultaneous substance abuse. Material and Methods: In this descriptive study, the electroencephalogram (EEG) signal was recorded from 52 participants in the Meth, Op, Can, and MD abusers, and the Healthy Control (HC) groups at rest state. EEG data were analyzed on the frequency domain with electrode-based, cortex-based, and hemisphere-based approaches. Results: However, the power spectrum in the delta band in the Op group, the gamma band in the Can group, and the gamma and beta bands in the MD group more significantly increased compared to the HC group, the power spectrum values in the Meth group reduced in the alpha, beta, and gamma bands. Moreover, the power spectrum values in the MD group more significantly higher than the Meth and Op groups in the beta and gamma bands. Conclusion: Since substance abuse in different types caused various changes in frequency components, the different power spectrum bands analysis in abusers can be reasonable to apply as a biomarker to detect the drug types.

Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities.

An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design.

Complex network based models of ECoG signals for detection of induced epileptic seizures in rats.

The automatic detection of seizures bears a considerable significance in epileptic diagnosis as it can efficiently lead to a considerable reduction of the workload of the medical staff. The present study aims at automatic detecting epileptic seizures in epileptic rats. To this end, seizures were induced in rats implementing the pentylenetetrazole model, with the electrocorticogram (ECoG) signals during, before and after the seizure periods being recorded. For this purpose, five algorithms for transforming time series into complex networks based on visibility graph (VG) algorithm were used. In this study, VG based methods were used for the first time to analyze ECoG signals in rats. Afterward, Standard measures in network science (graph properties) were made to examine the topological structure of these networks produced on the basis of ECoG signals. Then these measures were given to a classifier as input features so that the ECoG signals could be classified into seizure periods and seizure-free periods. Artificial Neural Network, considered a popular classifier, was used in this work. The experimental results showed that the method managed to detect epileptic seizure in rats with a high accuracy of 92.13%. Our proposed method was also applied to the recorded EEG signals from Bonn database to show the efficiency of the proposed method for human seizure detection.

Analysis of heart rate signals during meditation using visibility graph complexity.

In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.

Epileptic seizure detection using cross-bispectrum of electroencephalogram signal.

PURPOSE: The automatic detection of epileptic seizures in EEG data from extended recordings can make an important contribution to the diagnosis of epilepsy as it can efficiently reduce the workload of medical staff. METHODS: This paper describes how features based on cross-bispectrum can help with the detection of epileptic seizure activity in EEG data. Features were extracted from multi-channel intracranial EEG (iEEG) data from the Freiburg iEEG recordings of 21 patients with focal epilepsy. These features were used as a support vector machine classifier input to discriminate ictal from inter-ictal states. A post-processing method was applied to the classifier output in order to improve classification accuracy. RESULTS: A sensitivity of 95.8%, specificity of 96.7%, and accuracy of 96.8% were achieved. The false detection rate (FDR) was zero for 10 patients and very low for the rest. CONCLUSIONS: The results show that the proposed method distinguishes better between ictal and inter-ictal iEEG epochs than other seizure detection methods. The proposed method has a higher accuracy index than achievable with a number of previously described approaches. Also, the method is rapid and easy and may be helpful in online epileptic seizure detection and prediction systems.

Higher-order spectral analysis of spontaneous speech signals in Alzheimer's disease.

An early and accurate diagnosis of Alzheimer's disease (AD) has been progressively attracting more attention in recent years. One of the main problems of AD is the loss of language skills. This paper presents a computational framework for classifying AD patients compared to healthy control subjects using information from spontaneous speech signals. Spontaneous speech data are obtained from 30 AD patients and 30 healthy controls. Because of the nonlinear and dynamic nature of speech signals, higher order spectral features (specifically bispectrum) were used for analysis. Four classifiers (k-Nearest Neighbor, Support Vector Machine, Naïve Bayes and Decision tree) were used to classify subjects into three different levels of AD and healthy group based on their performance in terms of the HOS-based features. Ten-fold cross-validation method was used to test the reliability of the classifier results. The results showed that the proposed method had a good potential in AD diagnosis. The proposed method was also able to diagnose the earliest stage of AD with high accuracy. The method has the great advantage of being non-invasive, cost-effective, and associated with no side effects. Therefore, the proposed method can be a spontaneous speech directed test for pre-clinical evaluation of AD diagnosis.

Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms.

BACKGROUND: Mammography is the most common screening method for diagnosis of breast cancer. MATERIALS AND METHODS: In this study, a computer-aided system for diagnosis of benignity and malignity of the masses was implemented in mammogram images. In the computer aided diagnosis system, we first reduce the noise in the mammograms using an effective noise removal technique. After the noise removal, the mass in the region of interest must be segmented and this segmentation is done using a deformable model. After the mass segmentation, a number of features are extracted from it. These features include: features of the mass shape and border, tissue properties, and the fractal dimension. After extracting a large number of features, a proper subset must be chosen from among them. In this study, we make use of a new method on the basis of a genetic algorithm for selection of a proper set of features. After determining the proper features, a classifier is trained. RESULTS: To classify the samples, a new architecture for combination of the classifiers is proposed. In this architecture, easy and difficult samples are identified and trained using different classifiers. Finally, the proposed mass diagnosis system was also tested on mini-Mammographic Image Analysis Society and digital database for screening mammography databases. CONCLUSION: The obtained results indicate that the proposed system can compete with the state-of-the-art methods in terms of accuracy.

Epileptic seizure detection in EEGs signals based on the weighted visibility graph entropy.

PURPOSE: Epileptic seizure detection has been a complex task for both researchers and specialist in that the assessment of epilepsy is difficult because, electroencephalogram (EEG) signals are chaotic and non-stationary. METHOD: This paper proposes a new method based on weighted visibility graph entropy (WVGE) to identify seizure from EEG signals. Single channel EEG signals are mapped onto the WVGs and WVGEs are calculated from these WVGs. Then some features are extracted of WVGEs and given to classifiers to investigate the performance of these features to classify the brain signals into three groups of normal (healthy), seizure free (interictal) and during a seizure (ictal) groups. Four popular classifiers namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree (DT) and, Naïve Bayes (NB) are used in this work. RESULT: Experimental results show that the proposed method can classify normal, ictal and interictal groups with a high accuracy of 97%. CONCLUSIONS: This high accuracy index, which is obtained using just three features, is higher than those obtained by several previous works in which more nonlinear features were employed. Also, our method is fast and easy and may be helpful in different applications of automatic seizure detection such as online epileptic seizure detection.

A novel method for early diagnosis of Alzheimer's disease based on higher-order spectral estimation of spontaneous speech signals.

One main challenge for medical investigators is the early diagnosis of Alzheimer's disease (AD) because it provides greater opportunities for patients to be eligible for more clinical trials. In this study, higher order spectra of human speech signals during AD were explored to analyze and compare the quadratic phase coupling of spontaneous speech signals for healthy and AD subjects using bispectrum and bicoherence. The results showed that the quadratic phase couplings of spontaneous speech signal of persons with Alzheimer's were reduced compared to healthy subject. However, the speech phase coupled harmonics shifted to the higher frequencies in Alzheimer's than healthy subjects. In addition, it was shown not only are there significant differences between Alzheimer's and control subjects in parameters estimated, but also the speech patterns appeared to have fluctuated in both types of spontaneous speech.

Crowd behavior representation: an attribute-based approach.

In crowd behavior studies, a model of crowd behavior needs to be trained using the information extracted from video sequences. Most of the previous methods are based on low-level visual features because there are only crowd behavior labels available as ground-truth information in crowd datasets. However, there is a huge semantic gap between low-level motion/appearance features and high-level concept of crowd behaviors. In this paper, we tackle the problem by introducing an attribute-based scheme. While similar strategies have been employed for action and object recognition, to the best of our knowledge, for the first time it is shown that the crowd emotions can be used as attributes for crowd behavior understanding. We explore the idea of training a set of emotion-based classifiers, which can subsequently be used to indicate the crowd motion. In this scheme, we collect a large dataset of video clips and provide them with both annotations of "crowd behaviors" and "crowd emotions". We test the proposed emotion based crowd representation methods on our dataset. The obtained promising results demonstrate that the crowd emotions enable the construction of more descriptive models for crowd behaviors. We aim at publishing the dataset with the article, to be used as a benchmark for the communities.

A Model for Diagnosing Breast Cancerous Tissue from Thermal Images Using Active Contour and Lyapunov Exponent.

BACKGROUND: The segmentation of cancerous areas in breast images is important for the early detection of disease. Thermal imaging has advantages, such as being non-invasive, non-radiation, passive, quick, painless, inexpensive, and non-contact. Imaging technique is the focus of this research. METHODS: The proposed model in this paper is a combination of surf and corners that are very resistant. Obtained features are resistant to changes in rotation and revolution then with the help of active contours, this feature has been used for segmenting cancerous areas. RESULTS: Comparing the obtained results from the proposed method and mammogram show that proposed method is Accurate and appropriate. Benign and malignance of segmented areas are detected by Lyapunov exponent. Values obtained include TP=91.31%, FN=8.69%, FP=7.26%. CONCLUSION: The proposed method can classify those abnormally segmented areas of the breast, to the Benign and malignant cancer.

Erratum to: Crowd behavior representation: an attribute-based approach.

[This corrects the article DOI: 10.1186/s40064-016-2786-0.].

Segmenting breast cancerous regions in thermal images using fuzzy active contours.

Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171 ± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845 ± 0.0619, 0.0710 ± 0.0381 mm respectively. Furthermore, the sensitivity in recognizing the thermal pattern in breast tissue masses is 85 % and its accuracy is 91.98 %.A thermal imaging system has been proposed that is able to recognize abnormal breast tissue masses. This system utilizes fuzzy active contours to extract the abnormal regions automatically.

Assessing the Potential of Thermal Imaging in Recognition of Breast Cancer.

BACKGROUND: Breast cancer is a common disorder in women, constituting one of the main causes of death all over the world. The purpose of this study was to determine the diagnostic value of the breast tissue diseases by the help of thermography. MATERIALS AND METHODS: In this paper, we applied non-contact infrared camera, INFREC R500 for evaluating the capabilities of thermography. The study was conducted on 60 patients suspected of breast disease, who were referred to Imam Khomeini Imaging Center. Information obtained from the questionnaires and clinical examinations along with the obtained diagnostic results from ultrasound images, biopsies and thermography, were analyzed. The results indicated that the use of thermography as well as the asymmetry technique is useful in identifying hypoechoic as well as cystic masses. It should be noted that the patient should not suffer from breast discharge. RESULTS: The accuracy of asymmetry technique identification is respectively 91/89% and 92/30%. Also the accuracy of the exact location of identification is on the 61/53% and 75%. The approach also proved effective in identifying heterogeneous lesions, fibroadenomas, and intraductal masses, but not ISO-echoes and calcified masses. CONCLUSIONS: According to the results of the investigation, thermography may be useful in the initial screening and supplementation of diagnostic procedures due to its safety (its non-radiation properties), low cost and the good recognition of breast tissue disease.

A novel model for smart breast cancer detection in thermogram images.

BACKGROUND: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrievalwas tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.

Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems.

BACKGROUND: Accurate Diagnosis of Breast Cancer is of prime importance. Fine Needle Aspiration test or "FNA", which has been used for several years in Europe, is a simple, inexpensive, noninvasive and accurate technique for detecting breast cancer. Expending the suitable features of the Fine Needle Aspiration results is the most important diagnostic problem in early stages of breast cancer. In this study, we introduced a new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration (FNA). METHODS: We studied the Features of Wisconsin Data Base Cancer which contained about 569 FNA test samples (212 patient samples (malignant) and 357 healthy samples (benign)). In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples. Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure. This research proposed a new algorithm for an accurate diagnosis of breast cancer. RESULTS: According to Wisconsin Data Base Cancer (WDBC) data base, 62.75% of samples were benign, and 37.25% were malignant. After applying the proposed algorithm, we achieved high detection accuracy of about "96.579%" on 205 patients who were diagnosed as having breast cancer. It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively. If done by experts, Fine Needle Aspiration (FNA) can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. FNA can be the first line of diagnosis in women with breast masses, at least in deprived regions, and may increase health standards and clinical supervision of patients. CONCLUSION: Such a smart, economical, non-invasive, rapid and accurate system can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer. Another advantage of this method is the possibility of diagnosing breast abnormalities. If done by experts, FNA can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples.