Non-Invasive Electrophysiological Mapping Entropy Predicts Atrial Fibrillation Ablation Efficacy Better Than Clinical Characteristics

[EN] Success rate of atrial fibrillation (AF) ablation remains far from satisfactory. In this study, a 6 months AF freedom predictive model based on Fuzzy Entropy of non-invasive body surface potential maps is compared with clinical predictors. The study included 29 patients referred for pulmonary vein isolated catheter ablation procedure. Non-invasive electrocardiographic mapping with 54 ECG electrodes was recorded for all patients during the ablation procedure. Six months follow up was used to evaluate the efficacy of the ablation procedure. Predictions based on non-invasive electrocardiographic mappings during adenosine infusion (accuracy: 90%, AUC: 0.93) showed a clear improvement over standard-of-care clinical parameter models (accuracy: 62.1%, AUC: 0. 54). Our results indicate that measurements of electrophysiological complexity of AF signals could improve the clinical practice by predicting the efficacy of AF ablation procedures.This work was supported by the Instituto de Salud Carlos III FEDER (DTS16/00160; PI16/01123; PI17/01059; PI17/01106; EIT-Health 19600 AFFINE)De La Nava, AS.; Fabregat, MC.; Rodrigo, M.; Hernández, I.; Liberos, A.; Fernández-Avilés, F.; Guillem Sánchez, MS.... (2019). Non-Invasive Electrophysiological Mapping Entropy Predicts Atrial Fibrillation Ablation Efficacy Better Than Clinical Characteristics. IEEE. 1-4. https://doi.org/10.22489/CinC.2019.299S1

Unstructured Handwashing Recognition using Smartwatch to Reduce Contact Transmission of Pathogens

Current guidelines from the World Health Organization indicate that the SARS-CoV-2 coronavirus, which results in the novel coronavirus disease (COVID-19), is transmitted through respiratory droplets or by contact. Contact transmission occurs when contaminated hands touch the mucous membrane of the mouth, nose, or eyes so hands hygiene is extremely important to prevent the spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation of wearable devices, such as smartwatches, containing acceleration, rotation, magnetic field sensors, etc., together with the modern technologies of artificial intelligence, such as machine learning and more recently deep-learning, allow the development of accurate applications for recognition and classification of human activities such as: walking, climbing stairs, running, clapping, sitting, sleeping, etc. In this work, we evaluate the feasibility of a machine learning based system which, starting from inertial signals collected from wearable devices such as current smartwatches, recognizes when a subject is washing or rubbing its hands. Preliminary results, obtained over two different datasets, show a classification accuracy of about 95% and of about 94% for respectively deep and standard learning techniques

IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation

Feature selection is an important tool to deal with high dimensional data. In unsupervised case, many popular algorithms aim at maintaining the structure of the original data. In this paper, we propose a simple and effective feature selection algorithm to enhance sample similarity preservation through a new perspective, topology preservation, which is represented by persistent diagrams from the context of computational topology. This method is designed upon a unified feature selection framework called IVFS, which is inspired by random subset method. The scheme is flexible and can handle cases where the problem is analytically intractable. The proposed algorithm is able to well preserve the pairwise distances, as well as topological patterns, of the full data. We demonstrate that our algorithm can provide satisfactory performance under a sharp sub-sampling rate, which supports efficient implementation of our proposed method to large scale datasets. Extensive experiments validate the effectiveness of the proposed feature selection scheme

An Optimal k Nearest Neighbours Ensemble for Classification Based on Extended Neighbourhood Rule with Features subspace

To minimize the effect of outliers, kNN ensembles identify a set of closest observations to a new sample point to estimate its unknown class by using majority voting in the labels of the training instances in the neighbourhood. Ordinary kNN based procedures determine k closest training observations in the neighbourhood region (enclosed by a sphere) by using a distance formula. The k nearest neighbours procedure may not work in a situation where sample points in the test data follow the pattern of the nearest observations that lie on a certain path not contained in the given sphere of nearest neighbours. Furthermore, these methods combine hundreds of base kNN learners and many of them might have high classification errors thereby resulting in poor ensembles. To overcome these problems, an optimal extended neighbourhood rule based ensemble is proposed where the neighbours are determined in k steps. It starts from the first nearest sample point to the unseen observation. The second nearest data point is identified that is closest to the previously selected data point. This process is continued until the required number of the k observations are obtained. Each base model in the ensemble is constructed on a bootstrap sample in conjunction with a random subset of features. After building a sufficiently large number of base models, the optimal models are then selected based on their performance on out-of-bag (OOB) data.Comment: 12 page

Inference of Biogeographical Ancestry Under Resource Constraints

We study the problem of predicting human biogeographical ancestry using genomic data. While continental level ancestry prediction is relatively simple using genomic information, distinguishing between individuals from closely associated sub-populations (e.g., from the same continent) is still a difficult challenge. In particular, we focus on the case where the analysis is constrained to using single nucleotide polymorphisms (SNPs) from just one chromosome. We thus propose methods to construct ancestry informative SNP panels analyzing variants from a single chromosome, and evaluate the performance of such panels for both continental-level and sub-continental level ancestry prediction.;Efficient selection of ancestry informative SNPs is the key to successful ancestry prediction. The removal of redundant and noisy SNP features is essential prior to applying a learning algorithm. Here we propose two distinct methods of SNP selection: one is correlation-based SNP selection which uses a correlation metric to evaluate the usefulness of SNP features, while the other is random subspace projection based SNP selection which uses the learning algorithm itself to evaluate the worth of the SNP features. Correlation-based SNP selection approach can construct a small panel of useful SNPs for both continental level classification as well as binary classification of sub-populations. Unlike the correlation-based selection, random subspace projection based selection can construct efficient panel of SNP markers to address the difficult task of multinomial classification with multiple closely related sub-populations. We include results that demonstrate the performance of both methods, including comparison with other recently published related methods

Analysis and segmentation of MRI datasets of prostate cancer for the development of computer-based algorithms

Objective: Prostate carcinoma (PCa) occurs in approximately one in nine men throughout a lifetime. The histopathological Gleason score (GS) plays a crucial role in the choice of therapy for PCa. As an alternative to invasive biopsy sampling, radiomics analysis, which extracts large amounts of quantitative features from imaging, has been introduced in recent years for grading PCa. In this work, we investigated whether radiomics can reliably detect PCa in biparametric magnetic resonance imaging (bpMRI) and T1 mapping and distinguish between PCas with a GS of 6, 7, and ≥ 8. Materials and methods: In this retrospective study, a radiomics analysis of MRI (T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and T1 mapping) from 66 patients with histopathologically confirmed PCa was performed. Tumor lesions, the transitional zone, and the peripheral zone were manually segmented pixel by pixel. MR images were augmented tenfold to increase the size of the dataset, and 1390 features were extracted per image. After excluding highly correlating features, 876 features were used to train three models for the prediction of the GS (GS 6, GS 7, GS ≥ 8) by different machine learning algorithms: one model was trained with the original images, one with the augmented images, and one with the augmented images without features from T1 mapping. Subsequently, the models were evaluated by an independent test dataset. Results: The overall best performance with an accuracy of 92 % (95 % confidence interval (CI): 0.62 - 1.00) was obtained by the L2 Regularized Support Vector Machines (SVM) followed by the Random Forest (RF) (83 %; 95 % CI: 0.52 - 0.98), Stochastic Gradient Boosting (SGB) (75 %; 95 % CI: 0.43 - 0.95) and K-Nearest Neighbors (KNN) (50 %; 95 % CI: 0.21 - 0.79). For all four algorithms, prediction models trained with the augmented image dataset performed better than models trained with the original images. Excluding features from T1 mapping was associated with no change of accuracy for the SVM and KNN and decreased accuracy for the RF (- 0.16) and SGB (- 0,25). Conclusion: In this novel radiomics analysis of MRI from PCa patients, the utility of features from T1 mapping was investigated for the first time. It was shown that PCa could be reliably detected by radiomics in bpMRI and T1 mapping. It was possible to distinguish between PCa with a GS of 6, 7, and ≥ 8 with an accuracy up to 92%. In addition, the results suggest that augmenting image data in radiomics analyses can lead to better performance of predictive models.Hintergrund: Das Prostatakarzinom (PCa) tritt im Laufe des Lebens bei rund einem von neun Männern auf (1). Der histopathologische Gleason-Score (GS) spielt dabei eine entscheidende Rolle in der Wahl der Therapie des PCas (2, 3). Als Alternative zur invasiven Biopsieentnahme wurden in den letzten Jahren Radiomics-Analysen, bei denen große Mengen quantitativer Features aus der Bildgebung extrahiert werden, zum Grading des PCas eingeführt. In dieser Arbeit wurde untersucht, ob das PCa mit Hilfe von Radiomics zuverlässig in der biparametrischen Magnetresonanztomographie (bpMRT) und im T1-Mapping detektiert und zwischen PCas mit einem GS von 6, 7 und ≥ 8 unterschieden werden kann. Material und Methoden: In dieser retrospektiven Arbeit wurde eine Radiomics-Analyse von MRT-Bildern (T2-weighted imaging (T2WI), diffusion weighted imaging (DWI) und T1-Mapping) von 66 Patienten mit histopathologisch bestätigtem PCa durchgeführt. Die Tumorläsionen, die Transitionalzone und die periphere Zone wurden pixelweise manuell segmentiert. Die MRT-Bilder wurden um das zehnfache augmentiert, um die Größe des Datensatzes zu erhöhen, und 1390 Features wurden pro Bild extrahiert. Nach Ausschluss stark korrelierender Features wurden anhand von 876 Features drei Modelle zur Vorhersage des GS (GS 6, GS 7, GS ≥ 8) durch verschiedene Machine-Learning-Algorithmen trainiert: Ein Modell wurde mit dem Originaldatensatz trainiert, eins mit dem augmentierten Datensatz und eins mit dem augmentierten Datensatz unter Ausschluss von Features aus dem T1-Mapping. Anschließend wurden die Modelle durch einen unabhängigen Testdatensatz evaluiert. Ergebnisse: Die insgesamt beste Leistung mit einer Genauigkeit von 92 % (95% Konfidenzintervall (KI): 0,62 - 1,00) wurde durch den L2 Regularized Support Vector Machines (SVM) gefolgt von dem Random Forest (RF) (83 %; 95 % KI: 0,52 - 0,98), Stochastic Gradient Boosting (SGB) (75 %; 95 % KI: 0,43 - 0,95) und K-Nearest Neighbors (KNN) (50 %; 95 % KI: 0,21 – 0,79) erzielt. Vorhersagemodelle, die mit dem augmentierten Bilddatensatz trainiert wurden, wiesen bei allen vier Algorithmen eine bessere Leistung auf als Modelle, welche mit den Originalbildern trainiert wurden. Der Ausschluss von Features des T1-Mappings ging bei den SVM und dem KNN mit einer unveränderten und bei dem RF (- 0,16) und dem SGB (- 0,25) mit einer abnehmenden Genauigkeit einher. Schlussfolgerung: In dieser neuartigen Radiomics-Analyse von MRT-Bildern von PCa-Patienten wurde erstmals der Nutzen von Features aus dem T1-Mapping untersucht. Es wurde gezeigt, dass das PCa durch Radiomics zuverlässig in der bpMRT und im T1-Mapping detektiert werden kann. Es konnte mit einer Genauigkeit von bis zu 92 % zwischen PCas mit einem GS von 6, 7 und ≥ 8 unterschieden werden. Zudem deuten die Ergebnisse darauf hin, dass das Augmentieren von Bilddaten in Radiomics-Analysen zu einer besseren Leistung von Vorhersagemodellen führen kann

Making national forest inventory data relevant for local forest management

University of Minnesota Ph.D. dissertation. July 2018. Major: Natural Resources Science and Management. Advisor: Joseph Knight. 1 computer file (PDF); vi, 131 pages.The national forest inventory conducted by the United States Forest Service Forest Inventory and Analysis (FIA) program provides information for strategic level decisions regarding national and regional management of forest ecosystem goods and services. However, the sampling intensity typically limits the application of traditional direct estimators to areas the size of a large county, if not larger. This dissertation describes methods for combining FIA data with auxiliary information to enhance its relevance for local forest management. Background information is provided on the way population estimates are currently produced, and how precision can be improved via satellite imagery. A study is described that uses features extracted from dense time series of Landsat imagery with a model-assisted direct estimator. The study examined the relative predictive power of land cover models incorporating extracted spectro-temporal features versus composite imagery alone. Non-parametric models were fitted for multiple attributes measured on FIA plots using all archived Landsat scenes for Minnesota from 2009-2013. The estimated coefficients developed by harmonic regression of the time series imagery were shown to be moderately to highly correlated with tree-level and land cover attributes. When comparing results for spectro-temporal features to monthly image composites, regression models had greater explained variance and classification models had greater overall and individual class accuracies. Finally, a study is presented that tested the performance of a proposed variant of the k-nearest neighbors algorithm for areas too small to use a direct estimator. Spectro-temporal features were extracted for one ecological unit in Minnesota. A simulated population of tree canopy cover was sampled at FIA plot locations. The proposed algorithm was used to fit a non-parametric model to predict tree canopy cover that incorporates the spectro-temporal features. The model was used to construct predictive intervals for spatial domains over a range of domain sizes, and the resultant tests showed the coverage probability approached the theoretical value for areas as small as 1200 hectares. The study suggests that, given good auxiliary data and models, the scale of valid inference using FIA data can approach what is needed for local decision makers

Bibliography of communication and research products 2011

I. Journal articles -- II. Books or book chapters -- III. NIOSH numbered publications -- IV. Proceedings -- V. Abstracts -- VI. Control technology reports -- VII. Fatality assessment and control evaluation reports -- VIII. Fire fighter fatality investigation and prevention reports -- IX. Health hazard evaluation reports -- X. Author index-- XI. Keyword index -- XII. National Occupational Research Agenda (NORA) index"April 2012."Also available via the World Wide Web as an Acrobat .pdf file (3.13 MB, 143 p.)