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2014 IEEE Healthcare Innovation Conference (HIC)

作者： Nukala, B. T. Shibuya, N. Rodriguez, A. I. Tsay, J. Nguyen, T. Q. Zupancic, S. Lie, D. Y. C. Texas Tech Univ Lubbock TX 79409 USA Texas Tech Univ Hlth Sci Ctr Lubbock TX 79430 USA

ISBN: (纸本)9781467363624

This paper describes our custom-designed wireless gait analysis sensor (WGAS) system developed and tested for real-time fall detection. The WGAS is capable of differentiating falls vs. Activities of Daily Living (ADL) and the Dynamic Gait Index (DGI) performed by young volunteers using a Back Propagation Artificial Neural Network (BP ANN) algorithm. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller is worn by the subjects at either T4 (at back) or the belt-clip positions (in front of the waist) for the various falls, ADL, and Dynamic Gait Index (DGI) tests. The raw data is wirelessly transmitted from the WGAS to a nearby PC for real-time fall classification, where six features were extracted for the BP ANN. Overall fall classification accuracies of 97.0% and 97.4% have been achieved for the data taken at the T4 and at the belt position, respectively. The preliminary data demonstrates an overall sensitivity of 97.0% and overall specificity of 97.2% for this WGAS fall detection system, showing good promise as a real-time low-cost and effective fall detection device for wireless acute care and wireless assisted living.

关键词： accelerometers backpropagation body sensor networks data communication feature extraction gait analysis gyroscopes health care mechanoception microcontrollers neural nets MSP430 microcontroller WGAS fall detection system back propagation artificial neural network algorithm custom-designed wireless gait analysis sensor system tri-axial accelerometer wireless acute care wireless assisted living wireless data transmission algorithm design and analysis Artificial neural networks Classification algorithms Heuristic algorithms Knee Real-time systems Training Gyroscopes Artificial neural networks Feature extraction bioacoustics algorithm design and analysis gait analysis Microcontrollers wireless data transmission Body sensor networks back propagation sorting algorithm Neural network accelerometers Heuristic algorithms data communication

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A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks 13

A hypothesize-and-verify framework for Text Recognition usin...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Ray, Anupama Rajeswar, Sai Chaudhury, Santanu Indian Inst Technol Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781479918058

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors.

关键词： image matching image segmentation image sequences recurrent neural nets text detection tree searching abstraction best first search bidirectional long short term memory deep BLSTM deep recurrent neural network hybrid text recognizer hypothesize-and-verify framework language model line sequences multihypotheses tree architecture recognition errors segmentation algorithms sub-string match unicode sequences Classification algorithms Training Transforms modelling languages recurrent neural nets Printed text recognition Tree searching sorting algorithm Abstraction image matching image sequences Transform best-first search image segmentation segmentation Functional training

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Detecting ECG Limb Lead-wire Interchanges Involving the Right Leg Lead-wire 44

Detecting ECG Limb Lead-wire Interchanges Involving the Righ...

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44th Computing in Cardiology Conference (CinC)

作者： Gregg, Richard E. Hancock, E. William Babaeizadeh, Saeed Philips Healthcare Andover MA USA Stanford Univ Palo Alto CA 94304 USA

ISBN: (纸本)9781538645550;9781538666302

ECG lead-wire interchanges involving the right leg (RL) are not always detected. These RL lead-wire interchanges cannot be simulated in the same way as other lead-wire interchanges making database collection a necessity for algorithm development. Adult 12-lead ECGs from a single teaching hospital taken between January 2008 and July 2012 were reviewed for lead-wire interchanges by an expert electrocardiographer. Lead-wire interchanges were confirmed by comparison of serial ECGs. Positive interchanges included left arm / right leg (flat lead III, n = 134) and right arm / right leg (flat lead II, n = 139). A RL lead-wire interchange algorithm was developed by bootstrap aggregation of decision trees with 5-fold cross validation. Test results were summed over the 5-fold cross validation on the partitions not used for training. ECG features included maximum and minimum QRS and T-wave voltages for ECG leads I, II and III. The Haisty algorithm for RA-RL interchange was tested for comparison. algorithm performance was quantified by sensitivity (SE), specificity (SP) and estimated positive predictive value (PPV) based on SE, SP and realistic prevalence. For a prevalence of 0.2%, performance in SE, SP and PPV was: Haisty, 94, 99.4, 24;tree RA-RL: 84, 99.9, 57;tree LA-RL: 87, 99.9, and 57%. Even though SP was high for all three algorithms, the estimated PPVs were modest due to the low prevalence. Conclusion: Lead-wire interchanges involving the right leg wire can be detected with good sensitivity and high specificity. The higher specificity of the tree based algorithms results in more than twice the PPV of the Haisty algorithm.

关键词： Lead Electrocardiography Wires Legged locomotion Electrodes Signal processing algorithms Classification algorithms Interchange Electrocardiography Signal processing algorithms Biped locomotion sorting algorithm ELECTRIC WIRE Rope Right leg Electrodes Lead electrochemical grinding ecg lead p-phenyl vinyl

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Evolutionary Optimisation of Classifiers and Classifier Ensembles for Cost-Sensitive Pattern Recognition

Evolutionary Optimisation of Classifiers and Classifier Ense...

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8th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Schaefer, Gerald Univ Loughborough Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781467364003;9781467363976

Pattern recognition problems occur in many fields and hence effective classification algorithms are the focus of much research. In various circumstances not classification accuracy but misclassification cost minimsation is the primary goal leading to the development of cost-sensitive classification algorithms. In this paper, we show how evolutionary algorithms, in particular genetic algorithms (GAs), can be employed optimise to cost-sensitive classifiers and classifier ensembles. In particular, we discuss how GAs can be employed to derive a compact set of fuzzy if-then rules with an embedded cost term, and how GAs are able to perform simultaneous classifier selection and fusion for ensemble classifiers.

关键词： Classifier Ensembles Classifiers Pattern recognition sorting algorithm evolutionary algorithm pattern recognition problem

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Multiple Model Analytics for Adverse Event Prediction in Remote Health Monitoring Systems

Multiple Model Analytics for Adverse Event Prediction in Rem...

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2014 IEEE Healthcare Innovation Conference (HIC)

作者： Pourhomayoun, Mohammad Alshurafa, Nabil Mortazavi, Bobak Ghasemzadeh, Hassan Sideris, Konstantinos Sadeghi, Bahman Ong, Michael Evangelista, Lorraine Romano, Patrick Auerbach, Andrew Kimchi, Asher Sarrafzadeh, Majid Univ Calif Los Angeles Los Angeles CA 90024 USA Yale Univ New Haven CT 06520 USA Washington State Univ Pullman WA 99164 USA Univ Calif Irvine Irvine CA 92717 USA Univ Calif Davis Davis CA 95616 USA Univ Calif San Francisco San Francisco CA 94143 USA Cedars Sinai Med Ctr Los Angeles CA 90048 USA

ISBN: (纸本)9781467363624

Remote health monitoring systems (RHMS) are gaining an important role in healthcare by collecting and transmitting patient vital information and providing data analysis and medical adverse event prediction (e.g. hospital readmission prediction). Reduction in the readmission rate is typically achieved by early prediction of the readmission based on the data collected from RHMS, and then applying early intervention to prevent the readmission. Given the diversity of patient populations and the continuous nature of patient monitoring, a single static predictive model is insufficient for accurately predicting adverse events. To address this issue, we propose a multiple prediction modeling technique that includes a set of accurate prediction models rather than one single universal predictor. In this paper, we propose a novel analytics framework based on the physiological data collected from RHMS, advanced clustering algorithms and multiple-model-classification. We tested our proposed method on a subset of data collected through a remote health monitoring system from 600 Heart Failure patients. Our proposed method provides significant improvements in prediction accuracy and performance over single predictive models.

关键词： biomedical telemetry data analysis health care medical disorders patient monitoring RHMS advanced clustering algorithms analytics framework early intervention healthcare heart failure patients medical adverse event prediction multiple model analytics multiple prediction modeling technique multiple-model-classification patient populations patient vital information physiological data prediction accuracy readmission rate reduction remote health monitoring systems single static predictive model single universal predictor Biomedical monitoring Classification algorithms Feature extraction Heart Monitoring Prediction algorithms Predictive models Patient Monitoring Health Care biomedical telemetry Biomedical monitoring Prediction models medical disorders Prediction algorithms Early Intervention sorting algorithm Data analysis health and usage monitoring systems Feature extraction Adverse Event Heart

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Classification algorithms for Virtual Metrology

Classification Algorithms for Virtual Metrology

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IEEE International Conference on Management of Innovation and Technology (ICMIT)

作者： Tilouche, Shaima Bassetto, Samuel Nia, Vahid Partovi Ecole Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada Ecole Polytech Montreal Dept Math & Ind Engn GERAD Res Ctr Montreal PQ Canada

ISBN: (纸本)9781479955299

Virtual metrology in quality control deals with drifts in product quality that occur during non-sampling periods. This approach enables a hundred percent control and improves the precision of statistical control, specially while there is no sampling activity in manufacturing process. The main challenge in virtual metrology is inaccurate predictions. As such, the choice of an appropriate algorithm for prediction is crucial. We compare several algorithms that can be used for prediction in virtual metrology. The comparison over different prediction algorithms is made on a simulated data inspired from virtual metrology application.

关键词： Biological system modeling Manufacturing Metrology Neural networks Prediction algorithms Semiconductor device modeling Support vector machines metrology Prediction algorithms Biological system modeling Semiconductor device models Support Vector Network sorting algorithm Neural network Virtual

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Evaluation of Fall Detection Classification Approaches

Evaluation of Fall Detection Classification Approaches

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4th International Conference on Intelligent and Advanced Systems (ICIAS) and A Conference of World Engineering, Science and Technology Congress (ESTCON)

作者： Kerdegari, Hamideh Samsudin, Khairulmizam Ramli, Abdul Rahman Mokaram, Saeid Univ Putra Malaysia Dept Comp & Commun Syst Comp Syst Lab Serdang Malaysia Univ Putra Malaysia Inst Adv Technol Intelligent Syst & Robot Lab Serdang Malaysia

ISBN: (纸本)9781457719677

As we grow old, our desire for being independence does not decrease while our health needs to be monitored more frequently. Accidents such as falling can be a serious problem for the elderly. An accurate automatic fall detection system can help elderly people be safe in every situation. In this paper a waist worn fall detection system has been proposed. A tri-axial accelerometer (ADXL345) was used to capture the movement signals of human body and detect events such as walking and falling to a reasonable degree of accuracy. A set of laboratory-based falls and activities of daily living (ADL) were performed by healthy volunteers with different physical characteristics. This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. The aim of this paper is to investigate the performance of different classification algorithms for a set of recorded acceleration data. The algorithms are Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR. The acceleration data with a total data of 6962 instances and 29 attributes were used to evaluate the performance of the different classification algorithm. Results show that the Multilayer Perceptron algorithm is the best option among other mentioned algorithms, due to its high accuracy in fall detection.

关键词： sorting algorithm Falling Activities of Daily Living Detection systems Accidental Falls total Information aged person Multilayer perceptrons Autumn Human Body

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DRONE: Predicting Priority of Reported Bugs by Multi-Factor Analysis

DRONE: Predicting Priority of Reported Bugs by Multi-Factor ...

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29th IEEE International Conference on Software Maintenance (ICSM)

作者： Tian, Yuan Lo, David Sun, Chengnian Singapore Management Univ Singapore 178902 Singapore Natl Univ Singapore Singapore 117548 Singapore

Bugs are prevalent. To improve software quality, developers often allow users to report bugs that they found using a bug tracking system such as Bugzilla. Users would specify among other things, a description of the bug, the component that is affected by the bug, and the severity of the bug. Based on this information, bug triagers would then assign a priority level to the reported bug. As resources are limited, bug reports would be investigated based on their priority levels. This priority assignment process however is a manual one. Could we do better? In this paper, we propose an automated approach based on machine learning that would recommend a priority level based on information available in bug reports. Our approach considers multiple factors, temporal, textual, author, related-report, severity, and product, that potentially affect the priority level of a bug report. These factors are extracted as features which are then used to train a discriminative model via a new classification algorithm that handles ordinal class labels and imbalanced data. Experiments on more than a hundred thousands bug reports from Eclipse show that we can outperform baseline approaches in terms of average F-measure by a relative improvement of 58.61%.

关键词： bugs Bugs sorting algorithm precedence TRACKING SYSTEM

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FixingTIM: Interactive exploration of sequence and structural data to identify functional mutations in protein families

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BMC Proceedings 2014年第2-Sup期8卷 1-9页

作者： Luciani, Timothy Wenskovitch, John Chen, Koonwah Koes, David Travers, Timothy Elisabeta Marai, G. Department of Computer Science Brown University Box 1910 Providence 02912 RI United States Department of Computer Science University of Pittsburgh 210 South Bouquet Pittsburgh 15260 PA United States School of Information Science University of Pittsburgh 135 North Bellefield Avenue Pittsburgh 15260 PA United States Department of Computational and Systems Biology University of Pittsburgh 3501 Fifth Avenue Pittsburgh 15260 PA United States Department of Computer Science University of Illinois at Chicago 851 S. Morgan St. Chicago 60607 IL United States

Background: Knowledge of the 3D structure and functionality of proteins can lead to insight into the associated cellular processes, speed up the creation of pharmaceutical products, and develop drugs that are more effective in combating disease. Methods: We present the design and implementation of a visual mining and analysis tool to help identify protein mutations across a family of structural models and to help discover the effect of these mutations on protein function. We integrate 3D structure and sequence information in a common visual interface;multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a novel trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues. Results: We evaluate our approach on the triosephosphate isomerase (TIM) family structural models and sequence data and show that our tool provides an effective, scalable way to navigate a family of proteins, as well as a means to inspect the structure and sequence of individual proteins. Conclusions: The TIM application shows that our tool can assist in the navigation of families of proteins, as well as in the exploration of individual protein structures. In conjunction with domain expert knowledge, this interactive tool can help provide biophysical insight into why specific mutations affect function and potentially suggest additional modifications to the protein that could be used to rescue functionality. © 2014 Luciani et al.;licensee BioMed Central Ltd.

关键词： sorting algorithm Triosephosphate Isomerase Residue Sequence Common Residue Haemophilus Ducreyi

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Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier 44

Detection of Atrial Fibrillation in ECG Hand-held Devices Us...

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44th Computing in Cardiology Conference (CinC)

作者： Zabihi, Morteza Rad, Ali Bahrami Katsaggelos, Aggelos K. Kiranyaz, Serkan Narkilahti, Susanna Gabbouj, Moncef Tampere Univ Technol Tampere Finland Univ Tampere Tampere Finland Northwestern Univ Evanston IL USA Qatar Univ Doha Qatar

ISBN: (纸本)9781538645550;9781538666302

Atrial Fibrillation (AF) is characterized by chaotic electrical impulses in the atria, which leads to irregular heartbeats and can develop blood clots and stroke. Therefore, early detection of AF is crucial for increasing the success rate of the treatment. This study is focused on detection of AF rhythm using hand-held ECG monitoring devices, in addition to three other classes: normal or sinus rhythm, other rhythms, and too noisy to analyze. The pipeline of the proposed method consists of three major components: preprocessing and feature extraction, feature selection, and classification. In total, 491 hand-crafted features are extracted. Then, 150 features are selected in a feature ranking procedure. The selected features are from time, frequency, time-frequency domains, and phase space reconstruction of the ECG signals. In the final stage, a random forest classifier is used to classify the selected features into one of the four aforementioned ECG classes. Using the scoring mechanism provided by PhysioNet/Computing in Cardiology (CinC) Challenge 2017, the overall score (mean +/- std) of 81.9 +/- 2.6% is achieved over the training dataset in 10-fold cross-validation. The proposed algorithm tied for the first place in the PhysioNet/CinC Challenge 2017 with an overall score of 82.6% (rounded to 83%) on the unseen test dataset.

关键词： Electrocardiography Feature extraction Rhythm Cardiology Classification algorithms Atrial fibrillation Time-frequency analysis Electrocardiography atrial fibrillation Rhythms sorting algorithm Feature extraction Time frequency analysis Cardiology electrochemical grinding air feed equipment hand-held equipment

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