Use of hidden Markov models for electrocardiographic signal analysis.
Hidden Markov modelling (HMM) is a powerful stochastic modelling technique that has been successfully applied to automatic speech recognition problems. We are currently investigating the application of HMM to electrocardiographic signal analysis with the goal of improving ambulatory ECG analysis. The HMM approach specifies a Markov chain to model a "hidden" sequence that in this case is the underlying state of the heart. Each state of the Markov chain has an associated output function that describes the statistical characteristics of measurement samples generated during that state. Given a measurement sequence and HMM parameter estimates, the most likely underlying state sequence can be determined and used to infer beat classification. Advantages of this approach include resistance to noise, ability to model low-amplitude waveforms such as the P wave, and availability of an algorithm for automatically estimating model parameters from training data. We have applied the HMM approach to QRS complex detection and to arrhythmia analysis with encouraging results.
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Allegheny-Singer Research Institute, Pittsburgh, Pennsylvania 15212-9986.
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Briller
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D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods*; D015716:Electrocardiography, Ambulatory / Q000379:methods; D006801:Humans; D008390:Markov Chains; D012815:Signal Processing, Computer-Assisted