Comput Methods Programs Biomed - Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal.

Tópicos

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Resumo

In this paper, an effective paroxysmal atrial fibrillation (PAF) prediction algorithm is presented, which is based on analysis of the heart rate variability (HRV) signal. The proposed method consists of a preprocessing step for QRS detection and HRV signal extraction. In the next step, several features which can be used as markers for the prediction of PAF are extracted from the HRV signal. These features consist of spectrum features, bispectrum features, and non-linear features including sample entropy and Poincar? plot-extracted features. The spectrum features are able to discriminate the sympathetic and parasympathetic contents of the HRV signal, which are affected before PAF attacks. The bispectrum features are used in order to reveal information not presented on the spectral domain, and to detect quadratic phase coupled harmonics arising from non-linearities of the HRV signal. Moreover, the non-linear analysis can map the heart rate irregularities in the feature space and it leads to better understanding of the system dynamics before PAF attacks. In the final step, a support vector machine (SVM)-based classifier has been used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB). The obtained sensitivity, specificity, and positive predictivity were 96.30%, 93.10%, and 92.86%, respectively. The proposed methodology presents better results than the other existing approaches. The other important advantage of the proposed method when compared to the other approaches is that we do not need the both records of a subject to specify which episode preceding PAF events.

Resumo Limpo

paper effect paroxysm atrial fibril paf predict algorithm present base analysi heart rate variabl hrv signal propos method consist preprocess step qrs detect hrv signal extract next step sever featur can use marker predict paf extract hrv signal featur consist spectrum featur bispectrum featur nonlinear featur includ sampl entropi poincar plotextract featur spectrum featur abl discrimin sympathet parasympathet content hrv signal affect paf attack bispectrum featur use order reveal inform present spectral domain detect quadrat phase coupl harmon aris nonlinear hrv signal moreov nonlinear analysi can map heart rate irregular featur space lead better understand system dynam paf attack final step support vector machin svmbase classifi use paf predict perform propos method predict paf episod evalu use atrial fibril predict databas afpdb obtain sensit specif posit predict respect propos methodolog present better result exist approach import advantag propos method compar approach need record subject specifi episod preced paf event

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