Big Data Clustering in Cardeology Based on Modeling of Electrical Dynamics of the Heart in the form of Fermi-Pasta-Ulam Auto-Recurrence as a New Tool for the Study of Cardiac Activity
The mass application of mobile cardiographs already leads to both explosive quantitative growth of the number of patients available for ECG study, registered daily outside the hospital (Big DATA in cardiology), and to the emergence of new qualitative opportunities for the study of long-term oscillatory processes (weeks, months, years) of the dynamics of the individual state of the Cardiovascular system of any patient.
The article demonstrates that new opportunities of long - term continuous monitoring of the Cardiov ascular system state of patients ' mass allow to reveal the regularities (DATA MINING) of Cardiovascular system dynamics, leading to the hypothesis of the existence of an adequate Cardiovascular system model as a distributed nonlinearself - oscillating system of the FPU recurrence model class . The presence of a meaningful mathematical model of Cardiovascular system within the framework of the FPU auto – recurrence , as a refinement of the traditional model of studying black box, further allows us to offer new computational methods for ECG analysis and prediction of Cardiovascular system dynamics for a refined diagnosis and evaluation of the effectiveness of the treatment.
This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing the machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th anniversary of Emmanuil Braverman's decease. The papers present an overview of some of Braverman's ideas and approaches. The collection is divided in three parts. The first part bridges the past and the present. Its main contents relate to the concept of kernel function and its application to signal and image analysis as well as clustering. The second part presents a set of extensions of Braverman's work to issues of current interest both in theory and applications of machine learning. The third part includes short essays by a friend, a student, and a colleague.
Continua Health Alliance is a non-profit, open industry organization of healthcare and technology companies joining together in collaboration to improve the quality of personal healthcare. With more than 200members companies around the world, Continua is dedicated to establishing a system of interoperable personal connected health solutions with the knowledge that extending those solutions into the home fosters independence, empowers individuals and provides the opportunity for truly personalized health and wellness management. When you look at complete list of Continua Certified TM http://www.continuaalliance.org/node/77, you cannot find any ECG device (at the time of writing), but need ECG asa useful screening tool for a variety of cardiac abnormalities. We decided to build the prototype of 3-5-lead ECG with Bluetooth LE and USB interface. The firmware of the device was developed according the ISO/EEE 11073-10406:2011 and this device as a agent can communicate with any manager device on Personal Area Network. The filters of the device (50/60 Hz , low and high pass) are programmable and can be remotely configured. The signals from electrodes can be routed to doctor computer's screen for online diagnostic, can be save in EHR system or sent to special computer with LabView for ECG signal processing with different algorithms. This prototype could be used as a X73 agent for other consumer medical applications.
The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study.
This book constitutes the proceedings of the 23rd International Symposium on Foundations of Intelligent Systems, ISMIS 2017, held in Warsaw, Poland, in June 2017. The 56 regular and 15 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers include both theoretical and practical aspects of machine learning, data mining methods, deep learning, bioinformatics and health informatics, intelligent information systems, knowledge-based systems, mining temporal, spatial and spatio-temporal data, text and Web mining. In addition, four special sessions were organized; namely, Special Session on Big Data Analytics and Stream Data Mining, Special Session on Granular and Soft Clustering for Data Science, Special Session on Knowledge Discovery with Formal Concept Analysis and Related Formalisms, and Special Session devoted to ISMIS 2017 Data Mining Competition on Trading Based on Recommendations, which was launched as a part of the conference.
Technology mining (TM) helps to acquire intelligence about the evolution of research and development (R&D), technologies, products, and markets for various STI areas and what is likely to emerge in the future by identifying trends. The present chapter introduces a methodology for the identification of trends through a combination of “thematic clustering” based on the co-occurrence of terms, and “dynamic term clustering” based on the correlation of their dynamics across time. In this way, it is possible to identify and distinguish four patterns in the evolution of terms, which eventually lead to (i) weak signals of future trends, as well as (ii) emerging, (iii) maturing, and (iv) declining trends. Key trends identified are then further analyzed by looking at the semantic connections between terms identified through TM. This helps to understand the context and further features of the trend. The proposed approach is demonstrated in the field photonics as an emerging technology with a number of potential application areas.
This prototype development explains the challenges encountered during the ISO/IEEE 11073 standard implementation process. The complexity of the standard and the consequent heavy requirements, which have not encouraged software engineers to adopt the standard. The developing complexity evaluation drives us to propose two possible implementation strategies that cover almost all possible use cases and eases handling the standard by non-expert users. The first one is focused on medical devices (MD) and proposes a low-memory and low-processor usage technique. It is based on message patterns that allow simple functions to generate ISO/IEEE 11073 messages and to process them easily. MD act as X73 agent. Second one is focused on more powerful device X73 manager, which do not have the MDs' memory and processor usage constraints. The protocol between Agent and Manager is point-to-point and we can distribute the functionality between devices.
Developed both implementation X73 Agent and Manager will cut developing time for applications based on ISO/EEE 11073.
This article represents a new technique for collaborative filtering based on pre-clustering of website usage data. The key idea involves using clustering methods to define groups of different users.
This is a textbook in data analysis. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. According to this view, two main pathways for data analysis are summarization, for developing and augmenting concepts, and correlation, for enhancing and establishing relations. Visualization, in this context, is a way of presenting results in a cognitively comfortable way. The term summarization is understood quite broadly here to embrace not only simple summaries like totals and means, but also more complex summaries such as the principal components of a set of features or cluster structures in a set of entities.
The material presented in this perspective makes a unique mix of subjects from the fields of statistical data analysis, data mining, and computational intelligence, which follow different systems of presentation.
A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a series of computer experiments on large public collections of web documents and compared to other established methods and software, such as biclustering, on same datasets. Practical efficiency of different algorithms for computing frequent closed sets of attributes is compared.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.