Heart Signal Recognition by Hidden Markov Models: The ECG Case

Thoraval, Laurent; Carrault, Guy; Bellanger, Jean-Jacques

doi:10.1055/s-0038-1634971

Cited by 14 publications

(12 citation statements)

References 5 publications

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“…[5]). A novel approach to waveform recognition was developed by Thoraval, Carrault and Bellanger: Heart signal recognition by hidden Markov models: the ECG case [2]. In this method, the ECG is "decomposed" or segmented into different wave elements by a so-called hidden Markov model (HMM), modified by the authors and called "modified continuous variable duration" HMM.…”

Section: Detection and Parameter Estimation Imentioning

confidence: 99%

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Detection and Parameter Estimation I

Bemmel¹

1994

Methods Inf Med

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Section: Detection and Parameter Estimation Imentioning

confidence: 99%

“…on median and statistical filtering [1] and by Thoraval et aI. on Markov models [2]), and, on the other, innovative techniques are sometimes developed within the realm of biosignal processing itself (e.g., Kors on an incremental estimation technique [3] and Mizuta et aI. on the continuous estimation of transfer functions [4]).…”

mentioning

confidence: 99%

Detection and Parameter Estimation I

Bemmel¹

1994

Methods Inf Med

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“…HSMESMs are a special instance of hidden semi-Markov models (HSMMs) dedicated to the modeling and analysis of event-based random processes [40]. They have been first described by the authors in [41]. HSMESMs, like HSMMs, belong to the wide range of hidden Markov modeling (HMM) techniques [42].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models

Faisan

Thoraval

Armspach

et al. 2005

IEEE Trans. Med. Imaging

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In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal. The brain activation HSMESM is built from the timing information of the input stimulation protocol. The rich mathematical framework of HSMESMs makes these models an effective and versatile approach for fMRI data analysis. Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function or prior shape assumption for the fMRI response. Solving for the HSMESM Decoding problem allows to enrich brain mapping with activation lag mapping, activation mode visualizing, and hemodynamic response function analysis. Activation detection results obtained on synthetic and real epoch-related fMRI data demonstrate the superiority of the HSMESM mapping method with respect to a real application case of the statistical parametric mapping (SPM) approach. In addition, the HSMESM mapping method appears clearly insensitive to timing variations of the hemodynamic response, and exhibits low sensitivity to fluctuations of its shape.

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“…The HMM can be used to solve classification problems associated with time series input data such as speech signals or plant process signals, and can provide appropriate solutions by its modeling and learning capabilities, even though it does not have the exact knowledge to solve the problems. Most of the HMM applications for pattern classification in dynamic processes have a typical architecture to solve spatial-temporal problems, but the target systems are different, as in dynamic obstacle avoidance of mobile robot navigation, 17 radar target, 18 human action, 19 American sign language, 20 heart signals, 21 sonar signals, 22 two-handed actions, 23 conditions of an electrical machine, 24 deep space network antennae, 25 moving light displays, 26 environmental noise, 27 and human genes in DNA. 28 But the HMM has never been applied for transient identifications in NPPs.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov model-based real-time transient identifications in nuclear power plants

Kwon

Kim

Seong

2002

Int. J. Intell. Syst.

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In this article, a transient identification method based on a stochastic approach with the hidden Markov model (HMM) has been suggested and evaluated experimentally for the classification of nine types of transients in nuclear power plants (NPPs). A transient is defined as when a plant proceeds to an abnormal state from a normal state. Identification of the types of transients during an early accident stage in NPPs is crucial for proper action selection. The transient can be identified by its unique time-dependent patterns related to the principal variables. The HMM, a double-stochastic process, can be applied to transient identification that is a spatial and temporal classification problem under a statistical pattern-recognition framework. The trained HMM is created for each transient from a set of training data by the maximum-likelihood estimation method which uses a forward-backward algorithm and the Baum-Welch re-estimation algorithm. The transient identification is determined by calculating which model has the highest probability for given test data using the Viterbi algorithm. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. There are also a few experimental tests that have been performed, including superimposing random noise, adding systematic error, and adding untrained transients to verify its performance and robustness. The proposed real-time transient identification system has been proven to have many advantages, although there are still some problems that should be solved before applying it to an operating NPP. Further efforts are being made to improve the system performance and robustness in order to demonstrate reliability and accuracy to the required level.

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Heart Signal Recognition by Hidden Markov Models: The ECG Case

Cited by 14 publications

References 5 publications

Detection and Parameter Estimation I

Detection and Parameter Estimation I

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models

Hidden Markov model-based real-time transient identifications in nuclear power plants

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