Improving Flutter Localization Performance by Optimizing the Inverse Dower Transform

International audienceA previous study showed the possibility to localize right or left flutter circuit origin using variability contained in vectorcardiographic loop parameters. The Inverse Dower Transform, used to obtain the vectorcardiograms is based on a very simplistic torso conductor model, and hence not optimized. The present study aims to optimize the transform to maximize classifier accuracy. A parametric optimization model was proposed, as well as an optimization scheme. Model parameters were obtained by iteratively optimizing the linear SVM classifier accuracy until convergence. The goal can be shown to be multimodal and non-smooth. Therefore, a multi-instance and derivative-free method was considered. Previous dataset of 56 flutter recordings (31 right, 25 left) was used, considering only non-overlapped and respiratory motion-corrected F loops. For the SVM classifier, a 3.8% increase in accuracy was observed (max 0.95). When the logistic regression clas-sifier was used, an increase of 7.8% was observed (max 0.98). Comparison to a targeted transform previously developed showed an improvement by 17−19%. Observation of the model parameter values showed amplitude reduction applied to Lead X and rotation applied to Lead Z

Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

<p>Background: T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.</p> <p>Methods: We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.</p> <p>Results: The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.</p&gt

Non-Invasive Localization of Atrial Flutter Circuit using Recurrence Quantification Analysis and Machine Learning

International audienceAtrial flutter presents quasi-periodic atrial activity due to circular depolarization. Given the different structure of right and left atria, spatiotemporal variability should be different. This was analyzed using recurrence quan-tification analysis. Autocorrelation signals were estimated from the unthresholded recurrence plot, calculated with a properly processed ECG to remove variability related to external sources (noise, respiratory motion, T wave overlap). Simple features were considered from the autocorre-lation that attempts to describe the atrial activity in terms of range of recurrence and periodicity. Linear classification using support vector machines and logistic regression both allowed good classification performance (max accuracy 0.8 for both). Feature selection showed that right and left AFL have significantly different cycle lengths (right vs. left: 230.63 ms vs. 206.50 ms, p < 0.01). 1. Introduction The quasi-periodic atrial activity (AA) observed on the electrocardiogram (ECG) during atrial flutter (AFL) is caused by a rotating circular depolarization of the atrium. It has been shown that beat-to-beat variability of the flutter or F waves, quantified using vectorcardiographic parameters , allowed localization of right or left atrial circuit [1]. Different variability was observed for right and left local-ization, inducing a hypothesis of varying circuit stability. With a beat-to-beat approach, instantaneous spatiotem-poral information is not preserved, which may contain information about AA. In addition, both atria are known to be remarkably different in structure. The right atrium contains many large and well-defined cardiac fibers and is relatively thin, whereas the left atrium is thick and multi-layered [2]. It is expected that spatiotemporal variability would be different. The use of recurrence quantification analysis (RQA) has been highlighted for spatiotemporal analysis and characterization of atrial fibrillation (AF) activation propagation [3, 4]. Of particular interest, atrial fibrillation recurrence behavior was characterized, and was shown to be different for recurring and non-recurring persistent AF. In this paper, RQA is employed in order to study the spatiotemporal variability related to the circular propagation of AFL activation in a non-invasive fashion. Several features are extracted from the computed recurrence signal and serves as features for classification of circuit localiza-tion. Machine learning techniques are considered in order to obtain practical classifiers as well as to understand the reason why right and left AFL are different by employing feature selection

Capacitive sensing algorithm for elderly activity detection scheme indoor environment

The aim of this research is to assist elderlies in accomplishing their everyday tasks through facilitating their home atmosphere with sensors, actuators, and computational resources. A significant effort is needed to incorporate them to make them effective in daily life. Particularly, the tracking and detection of elderly people’s daily tasks, associated with the information of the user's location in the home environment signify the key pillars of this work. The detecting and tracking algorithm uses the sensory data to indicate an associated situation in order to allow elderly people with special requirements. Monitoring the elderly’s behaviour over a long period of time will allow prediction of their forthcoming alarming situation which allows the finding of the elderly’s behavioural deviations in their everyday routine. The proposed resolution has been methodically assessed in the laboratory

Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate

Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods

Real time monitoring system for upper arms rehabilitation exercise

Arm rehabilitation activities necessary to be continuously monitored in order to provide information of rehabilitation results to be examined by physical therapist. The determination of monitoring is to enhancing rehabilitation process. Moreover, a portable and simple home-based rehabilitation device can help patients to improve daily rehabilitation process activity. Some earlier studies regarding home-based rehabilitation process have shown improvement in promoting human movement recovery. This paper focuses on the development of a measurement by using arms guard and Smart Glove or Rehabilitation Glove, to assist stroke patient because of some complications such as accident and disease. This paper concourses on the method and application of mechanical equipment, sensors equipped Rehabilitation Glove measurement gripping activities. The devices will move based on a human operator's finger and arms movement using the Rehabilitation Glove. The system development involves a Microcontroller and HyperTerminal as a core processing for the instrumentation, communication and controlling applications. A series of bend or known as flex force sensors are fitted in a Rehabilitation Glove to get reading from the movement of human fingers. The quality of the force feedback is strongly affected by the maximum torque measurable by the Rehabilitation Glove and the performance of the force controller. Finally, the intelligence, learning and experience aspects of the human can be combined with the strength, endurance and speed of the human arms and Rehabilitation sensor in order to generate proper output of this paper. © 2015 IEEE

Sensory Substitution for Visual Impairments: A Technological Review

This work shows the study of the people who are deprived of a sensory modality that affects brain organization and cognition. By studying, the blind or visual impaired helps learn about how sensory experience in each sense is required for specific brain systems to develop, and how the technologies help in plasticity change in their absence. The sensory-motor deprivation model serves to assess the roles of critical developmental periods, compensatory cross-modal plasticity, and sensory-independent (a-modal) processes in the human brain. Sensory substitution enables a new era for blind people. Therefore, this research will commit a complete review on the sensory substitution for visual impairments

The implementation of IoT based smart refrigerator system

Wasted food due to spoilage is a critical resource issue. Food waste or food loss is food that is discarded or lost uneaten. Currently, in the world, according to the Food and Agriculture Organization of the United Nations (FAO), consumers waste about 1.3 billion tons of food annually and consumers in rich countries waste about 222 million tons of food products Once food products are purchased and set aside in a refrigerator, the users do not alert about their food items' expiration date and/or freshness unless they individually examine and track them. Moreover, for food products which are not labeled with an explicit expiration date may lead to significant food spoilage and additional expenditure for the users. However, with the latest trend technology of the Internet of Things (IoT), this problem can be resolved. Combining the idea of Internet of Things and smart kitchen evolution, the smart refrigerator system is developed. The system consists of three main parts which are sensing module, control module and transmission module. Sensing module consists of load cell and odour sensor while control module consists of Arduino UNO and power supply unit and last but not least, the transmission module consists of LCD module and Wi-Fi module. These modules work together to determine contents status inside the refrigerator and notify the user about the condition and quantity of the food via an SMS or an email

Augmented reality an economical solution for engineers and designers

Now a days, with obscene workload and a busy life, many experts face problems which more or less result in loss of their customers or to certain overheads that immobilize the customer's satisfaction process. The software being proposed will facilitates professional from various field like in mechanical designing, interior designing and that of architects. This research proposal most likely acts as an effective tool that can reduce the gap between industrial enterprises and customers in addition to other relevant business groups. It helps to visualize architectural designs and interior designs. Before the physical implementation, a virtual model of a real environment can be designed, enabling designers to virtually implement their idea in the given workspace and then view it in a real environment, enabling designers to view their 3D visualizations on their 2D drawings this help them to initially visualize and reconstruct the design without wasting a large amount of money making it a cost-effective solution. Application is based on my observations of the user's aspirations of an augmented reality in design service, a service that combines different social media functionality, augmented reality (AR) and 3-D modeling that embraces the concept of home design, architecture and mechanical designing process. This study connects all users of relevant concerns to an augmented reality's userintensive design. The paper provides you with the existential complexity of how the AR can be implemented to enhance the work of architects and designers. The proposed application shows two types of reality, marker-less and more marker-based. The new feature of the project is that no expensive devices are required. It can be used easily through smartphones and tablets. This is a cost-effective solution that mainly reduces the overhead accosted daily by, mechanical designers, interior designers and architects. The software displays all the features that mechanical designers, interior designers and architects may need