Hybrid methods in pattern recognition
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Detection of the P and T waves in an ECG
Computers and Biomedical Research
Regular attribute grammars and finite state machines
ACM SIGPLAN Notices
Syntactic Pattern Recognition of the ECG
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy beat labeling for intelligent arrhythima monitoring
Computers and Biomedical Research
Feature extraction in computerized approach to the ECG analysis
Pattern Recognition
An algorithm for polygonal approximation of digitized curves
Pattern Recognition Letters
A syntactic algorithm for peak detection in waveforms with applications to cardiography
Communications of the ACM
Transition network grammars for natural language analysis
Communications of the ACM
An Algebraic Approach to Automatic Construction of Structural Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two Algorithms for Piecewise-Linear Continuous Approximation of Functions of One Variable
IEEE Transactions on Computers
IEEE Transactions on Computers
An Interpreter of Attribute Grammars and Its Application to Waveform Analysis
IEEE Transactions on Software Engineering
Modeling waveform shapes with random effects segmental hidden Markov models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Computers in Biology and Medicine
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
Computer Methods and Programs in Biomedicine
ECG segmentation in a body sensor network using adaptive hidden Markov models
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Cardiac arrhythmia help - diagnosis system using wavelets and hidden Markov models
BEBI'09 Proceedings of the 2nd WSEAS international conference on Biomedical electronics and biomedical informatics
Analyzing ECG for cardiac arrhythmia using cluster analysis
Expert Systems with Applications: An International Journal
Real-Time remote ECG signal monitor and emergency warning/positioning system on cellular phone
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
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In this paper, we have studied the use of continuous probability density function hidden Markov models for the ECG signal analysis problem. Our previous work has focused on syntactic pattern recognition methods in signal processing. Hidden Markov model is basically a non-deterministic probabilistic finite state machine, which can be constructed inductively. It has been widely used in speech recognition and DNA modelling. We have found that hidden Markov models are very suitable for ECG recognition and analysis problems and that they are able to model accurately segmented ECG signals.