Abstract
Atrial fibrillation is the most common arrhythmia, which substantially increases the risk of stroke and other heart-related complications. Hence, forecasting the onset of paroxysmal atrial fibrillation (PAF) has become increasingly paramount and influential in AF managements and preventive treatments. Previous studies mainly focused on utilizing the morphological-temporal or time-frequency features from the surface electrocardiogram (ECG) to heighten the accuracy of PAF classification but not truely yielded onset predictions. To address this issue, this paper proposes a model that deploys the adaptive neuro-fuzzy inference system (ANFIS) and Unscented Kalman Filter to approximate the nonlinear-system state distribution for PAF onset prediction. The model is based on a combination of Kalman filter algorithm and a neural fuzzy network to predict PAF onset in 70 patients. Initially, we extracted the feature AWSUM to quantify the accumulation of extrasystolic beats within 30-min ECG recordings prior to the PAF onset of 24 patients. The extracted features then were utilized to reconstruct the dynamic-system state space, on which neural fuzzy based UKF algorithm were performed. The forecasting results highlighted low-level prediction errors averaged over 24 patients, by which RMSE = 0.42 ± 0.24 for the training data and RMSE = 0.34 ± 0.46 for the testing data; the testing-error had large variance due to the interpatient variabilities and abrupt changes in the system.
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Bui, C.T., Huynh, P.K., Phan, H.T., Le, T.Q., Van Toi, V. (2020). Developing Neural-fuzzy-based Unscented Kalman Filter Algorithm for Atrial Fibrillation Onset Prediction. In: Van Toi , V., Le, T., Ngo, H., Nguyen, TH. (eds) 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7). BME 2018. IFMBE Proceedings, vol 69. Springer, Singapore. https://doi.org/10.1007/978-981-13-5859-3_20
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DOI: https://doi.org/10.1007/978-981-13-5859-3_20
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