Small-world networks propensity in spontaneous speech signals of Alzheimer's disease: visibility graph analysis.

Exploiting complex network methods to describe dynamical behavior based on speech time series can provide fundamental insights into the function of underlying dynamical processes in Alzheimer's disease (AD). This study scrutinizes the dynamic alterations in Alzheimer's speech through abstract concepts of small-world networks. The visibility graph (VG) of the time series of spontaneous speech is introduced as a quantitative method to differentiate between healthy individuals and those with Alzheimer's.

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.

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).

A Semi-Automated Algorithm for Segmentation of the Left Atrial Appendage Landing Zone: Application in Left Atrial Appendage Occlusion Procedures.

Background: Mechanical occlusion of the Left atrial appendage (LAA) using a purpose-built device has emerged as an effective prophylactic treatment in patients with atrial fibrillation at risk of stroke and a contraindication for anticoagulation. A crucial step in procedural planning is the choice of the device size. This is currently based on the manual analysis of the "Device Landing Zone" from echocardiographic images.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier.

Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer's and stroke, it is the third widespread nervous disorder.

Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed.

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.

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.

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.

A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia among older people. The number of patients with AD will grow rapidly each year and AD is the fifth leading cause of death for those aged 65 and older. In recent years, one of the main challenges for medical investigators has been the early diagnosis of patients with AD because an early diagnosis can provide greater opportunities for patients to be eligible for more clinical trials and they will have enough time to plan for future, medical and financial decisions.

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

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.

Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform.

Background: Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians.

Objective: This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages.

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.