Proarrhythmic Effect of ICD Function

The implantable cardioverter ??? defibrillator (ICD) is a device that treats ventricular tachyarrhythmias (VT), when they appear in a sustained form. The device???s programming can deliver the following therapies: 1) antitachycardia pacing (ATP), 2) cardioversion and 3) defibrillation. Today???s devices also have the capability of every mode of cardiac pacing, i.e. atrial, ventricular, atrio-ventricular and biventricular pacing.It is well known that the antiarrhythmic drug therapy can exhibit proarrhythmic effects. Likewise, the antiarrhythmic apparatus can possibly aggravate an existing VT or cause the appearance of a new arrhythmia, attempting to convert the clinical tachyarrhythmia. A case of proarrhythmic effect related to the therapeutic sequences delivered by an ICD, is delineated in the following continuous recording of an arrhythmic event,as it was stored in the Holter function of the device

Proarrhythmic Effect of Implantable Defibrillator Function

Proarrhythmia usually refers to the worsening of an arrhythmia from an antiarrhythmic medication. However, implantable cardioverter defibrillator (ICD) devices can also be proarrhythmic as is shown in the case herein presented

Pulmonary Embolism following Endovenous Laser Ablation (EVLA) of the Great Saphenous Vein

A 70yr old lady presented to accident and emergency with sudden onset pleuritic chest pain. A pulmonary embolus (PE) was diagnosed by CTPA. Ten days earlier she had bilateral EVLA for recurrent great saphenous vein disease. Confounding risk factors for pulmonary embolism included bilateral ligation and stripping of the great saphenous vein a year earlier, malignancy, EVLA and phlebitic tributary varices. EVLA has been shown to be an effective treatment for superficial venous insufficiency with low morbidity and high patient satisfaction. The investigation of confounding risk factors and possible causes should not compromise the initial treatment of PE

Tracking and identifying floating marine debris

<p>It is estimated that a minimum of 5.25 trillion plastic objects weighing 268,940 tons are found in the world’s oceans [1]. Techniques such as ship observations, net trawling and water filtration help estimate the amount of plastic debris at the local scale [2].<br> The main goal of this research is to devise an automatic method for detecting and classifying marine debris. To this end, we are using two machine learning methods:<br> A. Bag of Features<br> B. CNN using Bottleneck</p> <p>to create classifiers able to classify images of marine debris in respective categories.</p

Energy harvesting for sensors in infrastructure monitoring and maintenance

Infrastructure monitoring and maintenance needs various kinds of sensors; all these sensors are expected to have long lifetime and self- maintenance and not be replaced. For non-destructive infrastructure monitoring, these sensors should be wireless, however, wireless sensors have an inherent problem on energy efficiency and energy consumption. Thus, how to power sensors efficiently or how to design a self-powered sensor is a key issue to this problem. Energy harvesting technique emerges as a new direction on getting power from environment. Piezoelectric and electromagnetic harvesting methods on vibration are analysed in this paper, and a low cost self-powered conversion circuit is modelled and simulated. Other kinds of energy harvesting are also briefly compared with those two methods; the solar-electric, vibration-electric, thermal-electric and electromagnetic-electric energy harvesting methods are briefly compared in this paper

Energy harvesting for sensors in infrastructure monitoring and maintenance

Infrastructure monitoring and maintenance needs various kinds of sensors; all these sensors are expected to have long lifetime and self- maintenance and not be replaced. For non-destructive infrastructure monitoring, these sensors should be wireless, however, wireless sensors have an inherent problem on energy efficiency and energy consumption. Thus, how to power sensors efficiently or how to design a self-powered sensor is a key issue to this problem. Energy harvesting technique emerges as a new direction on getting power from environment. Piezoelectric and electromagnetic harvesting methods on vibration are analysed in this paper, and a low cost self-powered conversion circuit is modelled and simulated. Other kinds of energy harvesting are also briefly compared with those two methods; the solar-electric, vibration-electric, thermal-electric and electromagnetic-electric energy harvesting methods are briefly compared in this paper

Harvesting energy from vibrations of the underlying structure

The use of wireless sensors for structural health monitoring offers several advantages such as small size, easy installation and minimal intervention on existing structures. However the most significant concern about such wireless sensors is the lifetime of the system, which depends heavily on the type of power supply. No matter how energy efficient the operation of a battery operated sensor is, the energy of the battery will be exhausted at some point. In order to achieve a virtually unlimited lifetime, the sensor node should be able to recharge its battery in an easy way. Energy harvesting emerges as a technique that can harvest energy from the surrounding environment. Among all possible energy harvesting solutions, kinetic energy harvesting seems to be the most convenient, especially for sensors placed on structures that experience regular vibrations. Such micro-vibrations can be harmful to the long-term structural health of a building or bridge, but at the same time they can be exploited as a power source to power the wireless sensors that are monitoring this structural health. This paper presents a new energy harvesting method based on a vibration driven electromagnetic harvester. By using an improved Maximum Power Point Tracking technique on the conversion circuit, the proposed method is shown to maximize the conversion coefficient from kinetic energy to applicable electrical energy. Reprints and permissions: sagepub.co.uk/journalsPermissions.nav