ECG Signal processing2 University of Alabama心电信号处理2阿拉巴马大学.pptxVIP

ECG Signal processing (2) ECE, UA ECG signal processing - Case [1] Diagnosis of Cardiovascular Abnormalities From Compressed ECG: A Data Mining-Based Approach[1] IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 15, NO. 1, JANUARY 2011 Company Logo Contents Introduction Home monitoring and real-time diagnosis of CVD is important ECG signals are enormous in size Decomposing ECG produces time delay High algorithm complex and hard to maintain Application Scenario Real time CVD diagnosis Processing on compressed ECG Efficient data mining method Simple to implement Application Scenario The work ?ow till our proposed CVD recognition system is being utilized System and Method Mobile phone compress the ECG signal Transmit by blue-tooth to hospital server Use data mining methods to extract the attributes and get the cluster range Detect and classify the CVD and make alert by the SMS system System and Method Attribute Subset Selection on Hospital Server a correlation-based feature subset (CFS) selection technique: extract relevant attributes Reduce redundant attributes System and Method B. Cluster Formation on Hospital Server System and Method C. Rule-Based System on a Mobile Phone System and Method C. Rule-Based System on a Mobile Phone Implementation Results Implementation programmed with J2EE using the Weka library Use NetBeans Interface to develop a system on mobile phone as Nokia E55 Java Wireless Messaging API Java Bluetooth API Implementation Results Results Implementation Results Results Conclusion Faster diagnosis is absolutely crucial for patient’s survival CVD detection mechanism directly from compressed ECG Using data mining techniques CFS,EM A rule-based system was implemented on mobile platform Case (2) Multiscale Recurrence Quanti?cation Analysis of Spatial Cardiac Vectorcardiogram Signals IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 58, NO. 2, FEBRUARY 2011 Content Introduction Research Method