10/23/2015© Mohamed Medhat Gaber1 Adaptive Mobile ECG Analysis Dr Mohamed Medhat Gaber School of Computing University of Portsmouth

Slides:



Advertisements
Similar presentations
1 Integrating ChemAxon and Linguamatics to provide Agile, Chemistry-enabled Text Mining Dr Paul Milligan Senior Application Specialist, Linguamatics ChemAxon.
Advertisements

1 Integrating ChemAxon and Linguamatics to provide Agile, Chemistry-enabled Text Mining Dr Jeffrey L. Nauss Application Specialist, Linguamatics ChemAxon.
Indexing Time Series Based on original slides by Prof. Dimitrios Gunopulos and Prof. Christos Faloutsos with some slides from tutorials by Prof. Eamonn.
SAX: a Novel Symbolic Representation of Time Series
Symbols and Motifs Mr. Parker Treasure Mountain International School.
Onboard Contextual Classification of 3-D Point Clouds with Learned High-order Markov Random Fields Daniel Munoz Nicolas Vandapel Martial Hebert.
Overview of Anomaly Detection in Time Series Data
Mining Time Series.
The Circle Hypothesis Revisited: Modelling Property Offenders’ Spatial Behaviour Using Sketch Maps Dr Karen Shalev Lecturer University of Portsmouth Institute.
Numbers
Research Topics Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2009.
Disk Aware Discord Discovery:
18th ICA WORKSHOP on Generalisation and Multiple Representation, A Coruña, July 7-8th, 2005 Web Services for an Open Generalisation Research Platform Moritz.
© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,
Jessica Lin, Eamonn Keogh, Stefano Loardi
Databases and Data Mining
Detecting Time Series Motifs Under
1 Dot Plots For Time Series Analysis Dragomir Yankov, Eamonn Keogh, Stefano Lonardi Dept. of Computer Science & Eng. University of California Riverside.
What Is Bluetooth? How Does It Differ from a Wired Connection?
Time-Series Data Kaitlin Duck Sherwood CS 533c. Why do you care? Time-series data is all over the place.
Time Series Data Analysis - II
Motif Discovery in Protein Sequences using Messy De Bruijn Graph Mehmet Dalkilic and Rupali Patwardhan.
Social Networking and On-Line Communities: Classification and Research Trends Maria Ioannidou, Eugenia Raptotasiou, Ioannis Anagnostopoulos.
Deepak Bangalore. About the app idea: Every day we receive call to our mobile phones we don’t know the exact location of the person from where he is calling.
Discovering the Intrinsic Cardinality and Dimensionality of Time Series using MDL BING HU THANAWIN RAKTHANMANON YUAN HAO SCOTT EVANS1 STEFANO LONARDI EAMONN.
1 Dr Na Yao Phone apps, Computer Software Teaching EBU5502 Database (JP) EBU714U Security and Authentication (JP) ECS608U Distributed systems and Security.
computer
By Mahmoud Moustafa Zidan Basic Sciences Department Faculty of Computer and Information Sciences Ain Shams University Under Supervision of Prof. Dr. Taymoor.
OCR Cambridge National in ICT (Level 1/2) Unit 2 Using ICT to create business solutions.
Mining Time Series.
© Copyright 2008 STI INNSBRUCK August 2, 2012 – Carmen Brenner.
University of Macau, Macau
Adaptive Mining Techniques for Data Streams using Algorithm Output Granularity Mohamed Medhat Gaber, Shonali Krishnaswamy, Arkady Zaslavsky In Proceedings.
Recent Results in Combined Coding for Word-Based PPM Radu Rădescu George Liculescu Polytechnic University of Bucharest Faculty of Electronics, Telecommunications.
Fast Shapelets: All Figures in Higher Resolution.
The Keogh Lab 1 Presented by Abdullah Mueen. Overview of our work Our Goal: Extract information from raw, noisy, massive, unstructured data. We develop.
A Multiresolution Symbolic Representation of Time Series Vasileios Megalooikonomou Qiang Wang Guo Li Christos Faloutsos Presented by Rui Li.
NSF Career Award IIS University of California Riverside Eamonn Keogh Efficient Discovery of Previously Unknown Patterns and Relationships.
1 Web Search Who was Michael Faraday? What were his accomplishments? 2 Web Search What is Faraday’s law about? What does it describe? 3 Web Search.
Explorer’s Name Explorer’s Picture Student’s Name.
ITree: Exploring Time-Varying Data using Indexable Tree Yi Gu and Chaoli Wang Michigan Technological University Presented at IEEE Pacific Visualization.
Technology at UNLV Office of Information Technology (OIT) Dr. Lori Temple Vice Provost for Information Technology.
10-1 人生与责任 淮安工业园区实验学校 连芳芳 “ 自我介绍 ” “ 自我介绍 ” 儿童时期的我.
Company Profile Eclaxy Software Co., Ltd is a professional mobile application development outsourcing service provider, headquartered in China. We are.
Feature learning for multivariate time series classification Mustafa Gokce Baydogan * George Runger * Eugene Tuv † * Arizona State University † Intel Corporation.
Free PowerPoint Templates Android Assignment Help BookMyEssay.
Mining and Processing Biomedical Data
Visually Mining and Monitoring Massive Time Series
SUPERVISED BY: Dr. Saed Tarapiah PREPARED BY:
Hire Toyota Innova in Delhi for Outstation Tour
ციფრული მარკეტინგი ნიკოლოზ არჩვაძე.
Yahoo Mail Customer Support Number
Most Effective Techniques to Park your Manual Transmission Car
Android Mobile apps development services company in India
How do Power Car Windows Ensure Occupants Safety
Android App Online Training in Hyderabad Android App Online Training in Hyderabad.
Yahoo Customer Service Toll-Free
نظام الفارابي لإدارة جودة التعليم والتعلم
19 Dec.2010-Self Controlling-Ali Mohamed
THANK YOU!.
Introduction to Mapping
Thank you.
Thank you.
MATH 2140 Numerical Methods
Unit 2 (Reading) F 2015 Unit 2 (Reading) H 2015
COMPUTER NETWORKS AND THE INTERNET Chapter 6
MATH 2140 Numerical Methods
Presentation transcript:

10/23/2015© Mohamed Medhat Gaber1 Adaptive Mobile ECG Analysis Dr Mohamed Medhat Gaber School of Computing University of Portsmouth

Symbolic Approximation of ECG  Symbolic ApproXimation (SAX) has been invented by Prof. Eamonn Keogh and his colleagues at UCR.  SAX representation of time series is considered the state-of-the-art.  SAX has been successfully used with notable efficiency in: Discord discovery Motif discovery 10/23/2015© Mohamed Medhat Gaber2

Real-time Classification of ECG  SAX uses alphabet letters for time series representation.  We have used SAX to represent ECG onboard Google Android for real-time classification using K-NN  We have used adaptation to ensure the continuity of the process 10/23/2015© Mohamed Medhat Gaber3

10/23/2015© Mohamed Medhat Gaber4 Current Development  Discord and Motif Discovery Real-time discord and motif discovery of time series Mapping discovered discords and motifs to real ECG problems Identifying other problems that we have termed as regular patters Using SAX and point-based clustering for time series representation

10/23/2015© Mohamed Medhat Gaber5 Thanks for listening Dr Mohamed Medhat Gaber School of Computing Faculty of Technology University of Portsmouth Web: