Detection of transient ST segment episodes during ambulatory ECG monitoring
Computers and Biomedical Research
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Incremental ML estimation of HMM parameters for efficient training
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Combining wavelet transform and hidden Markov models for ECG segmentation
EURASIP Journal on Applied Signal Processing
A study on speaker adaptation of the parameters of continuousdensity hidden Markov models
IEEE Transactions on Signal Processing
Dimensionality reduction oriented toward the feature visualization for ischemia detection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual. The adaptation training strategy is based on incremental versions of the expectation-maximization, segmental k-means and Bayesian approaches. Performance of the training methods was assessed through experiments considering the QT and ST-T databases. The results obtained show that the incremental training improves beat segmentation and ischemia detection performance with the advantage of low computational effort.