Electrocardiogram Signal Processing using Hidden Markov Models
نویسنده
چکیده
The processing of electrocardiogram signals (ECG) using Hidden Markov Models (HMM) methodology is presented. The proposed framework enables complex ECG signal processing with all the necessary steps resolved by HMM approach. Firstly, general description and comprehensive survey of actual HMM state of art is carried out. Secondly, the ECG processing methods as noise removal, characteristic points detection and baseline wandering are proposed. Next, further methods as ECG modelling, classification and clustering are carried out. The methods are applied both to artificial data and on MIT/BIH Arrhythmia ECG database. The results are particularly efficient promising that this new approach can be useful both for modelling, denoising, detection of important patterns, classification and clustering of biological signals. Finally in conclusions the up-to-now realized work is summarized and several future directions are suggested for further work. However, perhaps the main value of the thesis is its catholic presentation of Hidden Markov Models theory and its applications in the field of biological signal processing.
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