MODEL-BASED ECG ANALYSIS:TOWARDS PATIENT-SPECIFIC WEARABLE ECG MONITORING : MODEL-BASED ECG ANALYSIS:TOWARDS PATIENT-SPECIFIC WEARABLE ECG MONITORING

In this thesis, model-based analysis approach is considered as a possible solution towards a patient-specific point-of-care device for the purpose of electrocardiogram monitoring. Two novel methods are proposed, tested, and quantitatively evaluated. First, a method for estimating the instantaneous heart rate using the morphologicalfeatures of one electrocardiogram beat at a time is proposed. This work is not aimed at introducing an alternative way for heart rate estimation, but rather illustrates the utility of model-basedelectrocardiogram analysis in online individualized monitoring ofthe heart function. The heart rate estimation problem is reduced to fitting one parameter, whose value is related to the nine parameters of a realistic nonlinear model of the electrocardiogram and estimated from data by nonlinear least-squares optimization. The method feasibility is evaluated on synthetic electrocardiogram signals as well as signals acquired from MIT-BIH databases at Physionet website. Moreover, the performance of the method was tested under realistic free-moving conditions using a wearable electrocardiogram and heart monitor with encouraging results. Second, a model-based method for patient-specific detection of deformed electrocardiogram beats is proposed. Five parameters of a patient-specific nonlinear electrocardiogram model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal electrocardiogram beats. The utility of the proposed approach is tested by validating it on annotated arrhythmia data recorded underclinical conditions.