Theory and practical based approach to chronic total occlusions

Coronary chronic total occlusions (CTOs) represent the most technically challenging lesion subset that interventional cardiologists face. CTOs are identified in up to one third of patients referred for coronary angiography and remain seriously undertreated with percutaneous techniques. The complexity of these procedures and the suboptimal success rates over a long period of time, along with the perception that CTOs are lesions with limited scope for recanalization, account for the underutilization of CTO Percutaneous Coronary Intervention (PCI). During the last years, dedicated groups of experts in Japan, Europe and United States fostered the development and standardization of modern CTO recanalization techniques, achieving success rates far beyond 90 %, while coping with lesions of increasing complexity. Numerous studies support the rationale of CTO revascularization following documentation of viability and ischemia in the territory distal to the CTO. Successful CTO PCI provide better tolerance in case of future acute coronary syndromes and can significantly improve angina and left ventricular function. Randomized trials are on the way to further explore the prognostic benefit of CTO revascularization. The following review reports on the theory and the most recent advances in the field of CTO recanalization, in an attempt to promote a more balanced approach in patients with chronically occluded coronary arteries

Modeling andsimulationofspeedselectiononleftventricular assist devices

The control problem for LVADs is to set pump speed such that cardiac output and pressure perfusion are within acceptable physiological ranges. However, current technology of LVADs cannot provide for a closed-loop control scheme that can make adjustments based on the patient\u27s level of activity. In this context, the SensorART Speed Selection Module (SSM) integrates various hardware and software components in order to improve the quality of the patients\u27 treatment and the workflow of the specialists. It enables specialists to better understand the patient-device interactions, and improve their knowledge. The SensorART SSM includes two tools of the Specialist Decision Support System (SDSS); namely the Suction Detection Tool and the Speed Selection Tool. A VAD Heart Simulation Platform (VHSP) is also part of the system. The VHSP enables specialists to simulate the behavior of a patient?s circulatory system, using different LVAD types and functional parameters. The SDSS is a web-based application that offers specialists with a plethora of tools for monitoring, designing the best therapy plan, analyzing data, extracting new knowledge and making informative decisions. In this paper, two of these tools, the Suction Detection Tool and Speed Selection Tool are presented. The former allows the analysis of the simulations sessions from the VHSP and the identification of issues related to suction phenomenon with high accuracy 93%. The latter provides the specialists with a powerful support in their attempt to effectively plan the treatment strategy. It allows them to draw conclusions about the most appropriate pump speed settings. Preliminary assessments connecting the Suction Detection Tool to the VHSP are presented in this paper

Language Inference Using Elman Networks with Evolutionary Training

In this paper, a novel Elman-type recurrent neural network (RNN) is presented for the binary classification of arbitrary symbol sequences, and a novel training method, including both evolutionary and local search methods, is evaluated using sequence databases from a wide range of scientific areas. An efficient, publicly available, software tool is implemented in C++, accelerating significantly (more than 40 times) the RNN weights estimation process using both simd and multi-thread technology. The experimental results, in all databases, with the hybrid training method show improvements in a range of 2% to 25% compared with the standard genetic algorithm

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

INTRODUCTION:Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. MATERIALS AND METHODS:Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. RESULTS:A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. DISCUSSION:We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. CONCLUSIONS:Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. TRIAL REGISTRATION:ClinicalTrials.gov NCT01161381

Bolus Intravenous Procainamide in Patients with Frequent Ventricular Ectopics during Cardiac Magnetic Resonance Scanning: A Way to Ensure High Quality Imaging

Acquiring high-quality cardiac magnetic resonance (CMR) images in patients with frequent ventricular arrhythmias remains a challenge. We examined the safety and efficacy of procainamide when administered on the scanner table prior to CMR scanning to suppress ventricular ectopy and acquire high-quality images. Fifty consecutive patients (age 53.0 [42.0–58.0]; 52% female, left ventricular ejection fraction 55 ± 9%) were scanned in a 1.5 T scanner using a standard cardiac protocol. Procainamide was administered at intermittent intravenous bolus doses of 50 mg every minute until suppression of the ectopics or a maximum dose of 10 mg/kg. The average dose of procainamide was 567 ± 197 mg. Procainamide successfully suppressed premature ventricular contractions (PVCs) in 82% of patients, resulting in high-quality images. The baseline blood pressure (BP) was mildly reduced (mean change systolic BP −12 ± 9 mmHg; diastolic BP −4 ± 9 mmHg), while the baseline heart rate (HR) remained relatively unchanged (mean HR change −1 ± 6 bpm). None of the patients developed proarrhythmic changes. Bolus intravenous administration of procainamide prior to CMR scanning is a safe and effective alternative approach for suppressing PVCs and acquiring high-quality images in patients with frequent PVCs and normal or only mildly reduced systolic function

Evaluating the Window Size’s Role in Automatic EEG Epilepsy Detection

Electroencephalography is one of the most commonly used methods for extracting information about the brain’s condition and can be used for diagnosing epilepsy. The EEG signal’s wave shape contains vital information about the brain’s state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals’ classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden–Fletcher–Goldfarb–Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods

Tuning MoS₂ metamaterial with elastic strain

We provide a first demonstration that stress in nanostructured MoS2/Si3N4 membranes can lead to substantial reversible changes (more than 150% of relative change) of its optical properties in the visible part of the spectrum

Mechanochromic Reconfigurable Metasurfaces

The change of optical properties that some usually natural compounds or polymeric materials show upon the application of external stress is named mechanochromism. Herein, an artificial nanomechanical metasurface formed by a subwavelength nanowire array made of molybdenum disulfide, molybdenum oxide, and silicon nitride changes color upon mechanical deformation. The aforementioned deformation induces reversible changes in the optical transmission (relative transmission change of 197% at 654 nm), thus demonstrating a giant mechanochromic effect. Moreover, these types of metasurfaces can exist in two nonvolatile states presenting a difference in optical transmission of 45% at 678 nm, when they are forced to bend rapidly. The wide optical tunability that photonic nanomechanical metasurfaces, such as the one presented here, possess by design, can provide a valuable platform for mechanochromic and bistable responses across the visible and near infrared regime and form a new family of smart materials with applications in reconfigurable, multifunctional photonic filters, switches, and stress sensors.ISSN:2198-384