Neural Computing and Applications
Design of a system for real-time worm detection
HOTI '04 Proceedings of the High Performance Interconnects, 2004. on Proceedings. 12th Annual IEEE Symposium
A Real-Time Worm Outbreak Detection System Using Shared Counters
HOTI '07 Proceedings of the 15th Annual IEEE Symposium on High-Performance Interconnects
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
FPGA-oriented HW/SW implementation of ECG beat detection and classification algorithm
Digital Signal Processing
An effective feature set for ECG pattern classification
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Real-time CHF detection from ECG signals using a novel discretization method
Computers in Biology and Medicine
Data structure-guided development of electrocardiographic signal characterization and classification
Artificial Intelligence in Medicine
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Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%.