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Diagnostic performance of a dual-chamber cardioverter defibrillator programmed with nominal settings: A European prospective study

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JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY 2002年第1期13卷 25-32页

作者： Sadoul, N Jung, W Jordaens, L Leenhardt, A Santini, M Wolpert, C Aliot, E CHU Nancy Brabois Serv Cardiol F-54511 Vandoeuvre Les Nancy France Univ Bonn Klin Bonn Germany Univ Ziekenhuis Ghent Belgium Hop Univ Lariboisiere Paris France Osped S Filippo Neri Rome Italy

Introduction: Despite technologic developments, accurate discrimination of ventricular tachyarrhythmia from rapid rhythms of nonventricular origin remains a challenge. We sought to examine the sensitivity and specificity of a dual-chamber arrhythmia detection algorithm, the PARAD algorithm, incorporated in a dual-chamber implantable cardioverter defibrillator, the Defender (ELA Medical). Methods and Results : All detailed tachycardia episodes (i.e., with stored atrial and ventricular channel markers and electrograms) retrieved from the Holter memory of the device were analyzed from 95 patients (86 men and 9 women;age 62 +/- 12 years) implanted with the Defender with the algorithm programmed at nominal settings. Over a follow-up of 15 +/- 8 months, 559 detailed sustained tachycardia episodes detected in the tachycardia zone were gathered in 62 patients. Of the 300 ventricular tachycardia (VT) episodes, 298 were appropriately classified as VT by the algorithm. Of the 259 supraventricular tachycardia (SVT) episodes, 231 were appropriately classified as SVT. In 8 patients, 28 episodes of SVT were misclassified as VT: 25 atrial fibrillation episodes (6 patients), 2 sinus tachycardia (1 patient), and 1 atrial tachycardia. Calculated sensitivity and specificity were 99.3% and 89.2% on a per episode basis and 99.8% (95% confidence interval: 97.8 to 100) and 91.6% (95% confidence interval: 86.0 to 97.3) on a per patient basis, respectively. Conclusion: In this selected population of patients, the PARAD algorithm was safe and reliable for detection of a wide spectrum of tachyarrhythmias. Its specificity was high, particularly with respect to sinus tachycardia detection, but it must be improved with respect to detection of atrial fibrillation.

关键词： implantable cardioverter defibrillator dual chamber detection algorithm

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Detecting and quantifying stress granules in tissues of multicellular organisms with the *** analysis tool

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TRAFFIC 2019年第9期20卷 697-711页

作者： De Graeve, Fabienne Debreuve, Eric Rahmoun, Somia Ecsedi, Szilvia Bahri, Alia Hubstenberger, Arnaud Descombes, Xavier Besse, Florence Univ Cote dAzur Inst Biol Valrose INSERM CNRS Nice France Univ Cote dAzur CNRS I3S INRIA Sophia Antipolis France

Stress granules (SGs) are macromolecular assemblies induced by stress and composed of proteins and mRNAs stalled in translation initiation. SGs play an important role in the response to stress and in the modulation of signaling pathways. Furthermore, these structures are related to the pathological ribonucleoprotein (RNP) aggregates found in neurodegenerative disease contexts, highlighting the need to understand how they are formed and recycled in normal and pathological contexts. Although genetically tractable multicellular organisms have been key in identifying modifiers of RNP aggregate toxicity, in vivo analysis of SG properties and regulation has lagged behind, largely due to the difficulty of detecting SG from images of intact tissues. Here, we describe the object detector software *** and show how it overcomes the limits of classical object analyzers to extract the properties of SGs from wide-field and confocal images of Caenorhabditis elegans and Drosophila tissues, respectively. We demonstrate that *** enables the identification of genes modulating the assembly of endogenous and pathological SGs, and thus that it will be useful in the context of future genetic screens and in vivo studies.

关键词： detection algorithm image analysis marked point process stress granules

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Principle of an innovative visual sensor based on combined laser structured lights and its experimental verification

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OPTICS AND LASER TECHNOLOGY 2019年 111卷 35-44页

作者： Guo Jichang Zhu Zhiming Sun Bowen Yu Yingfei Minist Educ China Key Lab Adv Mat Proc Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

To promote development of sensing technology applied in the welding process and improvement of production automation in the welding manufacturing industry, an innovative multi-functional monocular visual sensor based on combined laser structured lights (single-line & cross-lines) is proposed and devised. It can realize the detection of cross-sectional size parameters of welding groove, space position and posture of welding torch relative to the weldment or welding groove by using monocular vision. Its calibration makes full use of the intrinsic parameters, such as its internal structure parameters and the focal length of industrial CCD camera. Therefore, it is unnecessary to re-calibrate when the position and posture of welding torch have some variations during the welding process or the visual sensor reassembles with the welding torch, which availably improves the adaptability of this sensor in engineering application, so that it can apply to the welding process with complex space welding trajectory. Furthermore, with the use of combined laser structured lights, it effectively eliminates the problem of information loss in depth commonly existing in usual monocular visual sensor. Meanwhile, all the detection functions of this sensor are derived from the processing results of a single image captured by industrial CCD camera, which avoids the problems of time-consuming and large amount of calculation resulting from multi-images processing (multi-vision method or multi-angle time-sharing imaging in monocular vision), and thus significantly improves its real-time performance in engineering application. Its functional detection algorithms for the cross-sectional size parameters of welding groove and the height of welding torch derived from the principle of optical triangulation are given. After establishing a test platform, we carried out a series of detecting experiments for different types or sizes of V-type butt groove. By image processing, recognition and extraction

关键词： Visual sensor Laser structured lights detection algorithm Cross-sectional size parameters of welding groove Height of welding torch

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O-Net: Dangerous Goods detection in Aviation Security Based on U-Net

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IEEE ACCESS 2020年 8卷 206289-206302页

作者： Kim, Woong Jun, Sungchan Kang, Sumin Lee, Chulung Korea Univ Dept Ind Management Engn Seoul 02841 South Korea Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA Korea Univ Sch Ind Management Engn Seoul 02841 South Korea

Aviation security X-ray equipment currently searches objects through primary screening, in which the screener has to re-search a baggage/person to detect the target object from overlapping objects. The advancements of computer vision and deep learning technology can be applied to improve the accuracy of identifying the most dangerous goods, guns and knives, from X-ray images of baggage. Artificial intelligence-based aviation security X-rays can facilitate the high-speed detection of target objects while reducing the overall security search duration and load on the screener. Moreover, the overlapping phenomenon was improved by using raw RGB images from X-rays and simultaneously converting the images into grayscale for input. An O-Net structure was designed through various learning rates and dense/depth-wise experiments as an improvement based on U-Net. Two encoders and two decoders were used to incorporate various types of images in processing and maximize the output performance of the neural network, respectively. In addition, we proposed U-Net segmentation to detect target objects more clearly than the You Only Look Once (YOLO) of Bounding-box (Bbox) type through the concept of a "confidence score". Consequently, the comparative analysis of basic segmentation models such as Fully Convolutional Networks (FCN), U-Net, and Segmentation-networks (SegNet) based on the major performance indicators of segmentation-pixel accuracy and mean-intersection over union (m-IoU)-revealed that O-Net improved the average pixel accuracy by 5.8%, 2.26%, and 5.01% and the m-IoU was improved by 43.1%, 9.84%, and 23.31%, respectively. Moreover, the accuracy of O-Net was 6.56% higher than that of U-Net, indicating the superiority of the O-Net architecture.

关键词： X-ray imaging Feature extraction Image segmentation Search problems Convolution Image recognition Explosives Artificial intelligence security system aviation security detection algorithm image segmentation U-Net X-ray detection

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Cloud-Based Advanced Shuffled Frog Leaping algorithm for Tasks Scheduling

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BIG DATA 2024年第2期12卷 110-126页

作者： Kumar, Dipesh Mandal, Nirupama Kumar, Yugal Indian Inst Technol ISM Dept Elect Engn Dhanbad India Jaypee Univ Informat Technol Dept Comp Sci & Engn Solan India

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-

关键词： cloud computing task scheduling detection algorithm multi-objective model frog optimization algorithm

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COALINLAS, a software for detecting coal beds in well-logs

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EARTH SCIENCE INFORMATICS 2019年第1期12卷 129-142页

作者： Yusefi, Amir Ramazi, Hamidreza Amirkabir Univ Technol Tehran Polytech Dept Min & Met Engn Tehran Iran

In this paper, the algorithm and working method of the software are described, which allows the detection of depth and thickness of coal beds by digital information in well-logs. The software known as COALINLAS is designed and developed in Visual Studio using C# by the authors. In this software, a boundary value of each log is defined for detection of coal and non-coal layers, by importing the data of a borehole, called the reference borehole. Inputs of the software are the reference borehole data including digital files of values of well logs in .las format, and core-sampling data;and target functions for accepting a layer as coal. The main engine of software is an algorithm called the detection algorithm. Importing the well-logging and core-sampling data, this algorithm calculates the boundary values to separate the coal bed from the other by the frequency distribution function for values of well logs near coal beds. The frequency distribution of well logs follows the generalized extreme value (GEV) function. The location of distribution depends on the mode and the scale depends on the standard deviation and the software calculates three boundary values in conditions where the cumulative density function (CDF) is equal to 50%, 70 and 90%. The case study used to test the performance of software shows that the boundary limit calculated for CDF of 70% separates the layers more precisely. In this case study, it is concluded that the software has the ability to detect all coal beds in the boring path using well-logs data. Moreover, COALINLAS can identify fine-scale changes in the characteristics of layers and detect dispersed thin layers neglected in core-sampling.

关键词： Coal Well logging Software detection algorithm Probability density function Generalized extreme value function

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An automated meal detector and bolus calculator in combination with closed-loop blood glucose control

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IFAC-PapersOnLine 2018年第27期51卷 168-173页

作者： Mahmoudi, Zeinab Boiroux, Dimitri Cameron, Faye Poulsen, Niels Kjølstad Bequette, B. Wayne Jørgensen, John Bagterp Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby2800 Kgs. Denmark Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute Troy NY United States

The aim of this study is to develop an algorithm for detection of unannounced meals and an insulin bolus calculator (BC) to work in combination with the meal detector. The input of the meal detector are the continuous glucose monitoring (CGM) data and the insulin infusion rate. During daytime, the automated meal detector and the BC control the blood glucose concentration. During nighttime, a model predictive control (MPC) algorithm regulates the basal insulin rate. The meal detector detects the occurrence of a meal, estimates the amount of carbohydrate (CHO) in the meal, and estimates the meal onset time. The BC computes a bolus dose to cover the detected meal. We test the meal detector and the BC on nine virtual type 1 diabetes (T1D) patients. The meal detection algorithm, applied on the virtual patients, has a median detection delay of 40 min, detection sensitivity of 80% and a median meal onset estimation bias of 15 min. The algorithm does not have false positive. © 2018

关键词： Glucose Blood Insulin Kalman filters Mathematical instruments Model predictive control Predictive control systems Blood glucose concentration Blood glucose controls Bolus calculator Continuous glucosemonitoring (CGM) detection algorithm detection sensitivity Insulin infusion rate Type 1 diabetes

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An algorithm for the identification of heparin-induced thrombocytopenia using a medical information database

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JOURNAL OF CLINICAL PHARMACY AND THERAPEUTICS 2013年第5期38卷 423-428页

作者： Hanatani, T. Sai, K. Tohkin, M. Segawa, K. Kimura, M. Hori, K. Kawakami, J. Saito, Y. Natl Inst Hlth Sci Div Med Safety Sci Tokyo 1588501 Japan Nagoya City Univ Dept Regulatory Sci Grad Sch Pharmaceut Sci Nagoya Aichi Japan Hamamatsu Univ Sch Med Dept Med Informat Shizuoka Japan Hamamatsu Univ Sch Med Dept Hosp Pharm Shizuoka Japan

What is known and objective Using effective algorithms for extracting relevant drug and patient information from electronic medical information data systems is likely to form an increasingly important aspect of pharmacovigilance. To this end, we aimed to develop and validate a novel algorithm for detecting heparin-induced thrombocytopenia (HIT) using a medical information database (MID) and for identifying possible risk factors for HIT. Methods We developed a new algorithm for detecting HIT based on platelet count at distinct time-points and diagnostic information from patients treated with unfractionated heparin (UFH) from April 2008 through March 2012 at Hospital of Hamamatsu University School of Medicine, Japan. Definitive diagnoses of HIT were made through reviews of the medical records by a skilled haematologist. The performance of the algorithm was assessed using the positive predictive value (PPV). Multivariate logistic regression analysis was used to identify possible risk factors for HIT. Results and discussion This algorithm detected 47 patients with suspected HIT in the source population (n=2875). Of these, 41 were identified as patients with definitive HIT after review of the medical records. The PPV for the algorithm was 87.2%, and the frequency of definitive HIT was 14%. Longer-term treatment (>= 4 days) with UFH was identified as a risk factor for HIT, with an odds ratio of 5.38 (95% CI: 2.35-12.32) for definitive HIT. What is new and conclusion We developed a novel, high-PPV detection algorithm for HIT and identified longer-term treatment with UFH as a risk factor for HIT. Our results support the utility of MIDs for improving pharmacovigilance.

关键词： detection algorithm heparin-induced thrombocytopenia medical information database pharmacovigilance risk factor validation

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Application of peak based-Bayesian statistical method for isotope identification and categorization of depleted, natural and low enriched uranium measured by LaBr3:Ce scintillation detector

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第10期55卷 3913-3923页

作者： Yucel, Haluk Tuzuner, Selin Saatci Massey, Charles Ankara Univ Inst Nucl Sci Besevler 10 Yil Campus TR-06100 Ankara Turkiye Int Atom Energy Agcy IAEA Vienna Int Ctr VIC Div Nucl Secur Detect Sci & Technol Dept Nucl Safety & SecurNucl Secur Mat Outside Re POB 100 A-1400 Vienna Austria

Todays, medium energy resolution detectors are preferably used in radioisotope identification devices(RID) in nuclear and radioactive material categorization. However, there is still a need to develop or enhance << automated identifiers >> for the useful RID algorithms. To decide whether any material is SNM or NORM, a key parameter is the better energy resolution of the detector. Although masking, shielding and gain shift/stabilization and other affecting parameters on site are also important for successful operations, the suitability of the RID algorithm is also a critical point to enhance the identification reliability while extracting the features from the spectral analysis. In this study, a RID algorithm based on Bayesian statistical method has been modified for medium energy resolution detectors and applied to the uranium gamma-ray spectra taken by a LaBr3:Ce detector. The present Bayesian RID algorithm covers up to 2000 keV energy range. It uses the peak centroids, the peak areas from the measured gamma-ray spectra. The extraction features are derived from the peak-based Bayesian classifiers to estimate a posterior probability for each isotope in the ANSI library. The program operations were tested under a MATLAB platform. The present peak based Bayesian RID algorithm was validated by using single isotopes(Am-241,Co- 57, Cs-137,Mn-54, Co-60), and then applied to five standard nuclear materials(0.32-4.51% at.U-235), as well as natural U- and Th-ores. The ID performance of the RID algorithm was quantified in terms of F-score for each isotope. The posterior probability is calculated to be 54.5-74.4% for U-238 and 4.7-10.5% for U-235 in EC-NRM171 uranium materials. For the case of the more complex gamma-ray spectra from CRMs, the total scoring (S-T) method was preferred for its ID performance evaluation. It was shown that the present peak based Bayesian RID algorithm can be applied to identify U-235 and U-238 isotopes in LEU or natural U-Th samples if a medium ene

关键词： LaBr 3 (Ce) Scintillation detectors Gamma ray spectrum Isotope identification Bayesian statistical method detection algorithm Nuclear security U-235

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Bayesian Inference and Prediction of Wave-induced Ship Motion based on Discrete-frequency Model Approximations ⁎

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IFAC-PapersOnLine 2018年第29期51卷 104-109页

作者： Kennedy, Justin M. Ford, Jason J. Valentinis, Francis Perez, Tristan Electrical Eng. & Comp. Sc Queensland University of Technology Brisbane Australia Defence Science and Technology Group Australian Department of Defence Australia Institute for Future Environments Queensland University of Technology Brisbane Australia

In this paper, we investigate the use of a discrete-frequency approximation for stochastic processes, modelling wave-induced ship motion and assess its prediction performance. The proposed estimator is obtained by adapting Bayesian spectral inference methods. We study the relationship between the lag window and the prediction performance and suggest a minimum requirement on window length for minimising the prediction error. We show an application to prediction of the pitch, heave, and roll motion from experiments of a scale-model container ship. © 2018

关键词： Inference engines Bayesian networks Forecasting Random processes Ships Stochastic systems Bayesian inference detection algorithm Discrete frequencies Inference methods Minimum requirements Prediction errors Prediction performance Statistical inference

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