Biomedical digital signal processing: C-language examples and laboratory experiments for the IBM PC
Biomedical digital signal processing: C-language examples and laboratory experiments for the IBM PC
Modelling ECG signals with hidden Markov models
Artificial Intelligence in Medicine
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
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This paper reports the development of a help-diagnosis system where the physician is required to analyze some ECG pulses that can not be accurately classified by the system. A confidence measure is estimated on the basis of massive experimental tests on data from MIT-BIH Arrhythmia Database, and was set on a threshold above which no classification errors were obtained. Cardiac arrhythmia detection and classification is performed by using Wavelets and Hidden Markov Models (HMMs). The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF), atrial flutter (AFL), and normal rhythm (N). Experimental results are obtained in real data from MIT-BIH Arrhythmia Database and a developed Data-Acquisition System.