Med Biol Eng Comput - Predicting termination of paroxysmal atrial fibrillation using empirical mode decomposition of the atrial activity and statistical features of the heart rate variability.

Tópicos

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Resumo

This paper presents an algorithm for predicting termination of paroxysmal atrial fibrillation (AF) attacks using features extracted from the atrial activity (AA) and heart rate variability (HRV) signals. First, AA signal was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition method. Then, power spectrums of the AA and its IMFs (second, third, and forth components) were obtained, and the peak frequency of the power spectral densities were extracted. These features were complemented with three additional features consisting of mean, skewness, and kurtosis of the HRV signal. These seven features were then reduced to only two features by the generalized discriminant analysis technique. This not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, a linear classifier was used to classify AF episodes from AF termination database. This database consists of three types of AF episodes: N type (non-terminated AF episode), S type (terminated 1?min after the end of the record), and T type (terminated immediately after the end of the record). The obtained sensitivity, specificity, positive predictivity, and negative predictivity were 94, 97, 92, and 96?%, respectively. The important advantage of the proposed method comparing to the other existing approaches is that our algorithm can simultaneously discriminate three types of AF episodes with high accuracy.

Resumo Limpo

paper present algorithm predict termin paroxysm atrial fibril af attack use featur extract atrial activ aa heart rate variabl hrv signal first aa signal decompos set intrins mode function imf use empir mode decomposit method power spectrum aa imf second third forth compon obtain peak frequenc power spectral densiti extract featur complement three addit featur consist mean skew kurtosi hrv signal seven featur reduc two featur general discrimin analysi techniqu reduc number input featur also increas classif accuraci select discrimin featur final linear classifi use classifi af episod af termin databas databas consist three type af episod n type nontermin af episod s type termin min end record t type termin immedi end record obtain sensit specif posit predict negat predict respect import advantag propos method compar exist approach algorithm can simultan discrimin three type af episod high accuraci

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