Primer on Machine Learning in Electrophysiology

Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies

In silico electrocardiogram from simplistic geometric and reaction diffusion model for detection of cardiac ventricular abnormalities through machine learning methods

Millions of people around the globe die each year from ischemic heart disease. While previous work has focused on the detection of myocardial scarring after an ischemic event, there is little to no work that involves the detection of ventricular ischemia in the early stages of an ischemic event. Using simplified geometries and standard electrophysiological modeling, normal and ischemic conditions may be simulated within a left ventricle model. This work highlights the theoretical framework for determining the configuration of myocardial ischemia using analytical and deep learning methods. Using these methods, a two-dimensional model was used to find a theoretical threshold that can dictate when normal transmural myocardial ischemia is occurring. Using a three-dimensional model, 20,000 stochastic ischemic zones were simulated with associated ECGs to train a one-dimensional convolutional neural network to predict the configuration of the early stages of ischemia. In addition, a three-dimensional model was implemented to produce 10,000 stochastically growing ischemic configurations in which a one-dimensional convolutional and long-short term memory neural network was used to predict the future states of ischemia

Clustering Algorithm Comparison for Ellipsoidal Data

The main objective of cluster analysis is the statistical technique of identifying data points and assigning them into meaningful clusters. The purpose of this paper is to compare different types of clustering algorithms to find the clustering algorithm that performs the best for varying complexities in Gaussian data. The clustering algorithms used would include: Partitioning Around Medoids (PAM), K-means, Hierarchical with different linkages (Ward’s linkage, Single linkage, Complete linkage, Average linkage, McQuitty’s method, Gower’s method, and Centroid method). The different types of complexities would include different number of dimensions, average pairwise overlap between clusters, number of points simulated from each cluster. After the data is simulated the Adjusted Rand Index will be used gauge the performance of the clusters. From that a t-test will also be used to see if there are any clustering algorithms that as well as other clustering algorithms

Improving geoscience data access and interoperability through the Flyover Country mobile app

University of Minnesota M.S. thesis. May 2018. Major: Earth Sciences. Advisors: Amy Myrbo, Andrew Wickert. 1 computer file (PDF); v, 69 pages.The Flyover Country mobile app displays geospatial geoscience data on a map-based user interface for use in understanding landscape features from the vantage point of the airplane window seat, hiking trail vista, or remote field research location. These uses require a complex set of features, visualization strategies, and app workflows. In 2017, the app was redesigned from the ground up to incorporate many lessons learned, usability improvements, and a more sustainable and expandable codebase based on two years of feedback and observations from the first version of the app. This thesis outlines lessons learned and a use-case scenario demonstrating the benefits of our new design choices. The app’s effects on increasing interoperability of data in the broader geoscience data community are demonstrated, alongside as recommendations for serving geoscience data for use in mobile apps

Sub-continental-scale carbon stocks of individual trees in African drylands

Abstract The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales 1–14 . This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems 15–18 . We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0–1,000 mm year −1 rainfall zone by coupling field data 19 , machine learning 20–22 , satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha −1 and 63 kg C tree −1 in the arid zone to 3.7 Mg C ha −1 and 98 kg tree −1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies 23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation 24–29 . We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops