Comput. Biol. Med. - Atrial activity selection for atrial fibrillation ECG recordings.

Tópicos

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Resumo

In this paper we apply independent component analysis (ICA) followed by second order blind identification (SOBI) to an atrial fibrillation (AF) 12-lead electrocardiogram (ECG) recording in order to extract the source that represents atrial activity (AA) (ICA-SOBI method). Still, there is no assurance that only one source obtained from this method will contain AA, and thus we aim to select the most representative source of AA. The novelty in this paper is the proposal of three parameters to select the most representative source of AA. These parameters are correlation coefficient with lead V1 (CV1), peak factor (PF) and spectral concentration (SC). The first two parameters are introduced as new indicators, addressing features overlooked by the SC even when they are present in AA during AF. For synthesized data, at least two of the three parameters select the same representation of AA in 93.3% of the cases. For real data (218 ECG recordings), we observe that PF presents, in 89.5% of the cases, values between 2 and 4.5 for the selected sources, ensuring a well-defined range of values for AA. The actual values of CV1 and SC were scattered throughout their possible ranges (0-1 for CV1 and 0.08-0.7 for SC), and the correlation coefficient between these variables was found to be =0.58. We compared our results with three known algorithms: QRST cancellation, principal components analysis (PCA) and ICA-SOBI. The results obtained from this comparison show that our proposed methods to select the best representation of AA in general outperform the three above-mentioned algorithms.

Resumo Limpo

paper appli independ compon analysi ica follow second order blind identif sobi atrial fibril af lead electrocardiogram ecg record order extract sourc repres atrial activ aa icasobi method still assur one sourc obtain method will contain aa thus aim select repres sourc aa novelti paper propos three paramet select repres sourc aa paramet correl coeffici lead v cv peak factor pf spectral concentr sc first two paramet introduc new indic address featur overlook sc even present aa af synthes data least two three paramet select represent aa case real data ecg record observ pf present case valu select sourc ensur welldefin rang valu aa actual valu cv sc scatter throughout possibl rang cv sc correl coeffici variabl found compar result three known algorithm qrst cancel princip compon analysi pca icasobi result obtain comparison show propos method select best represent aa general outperform three abovement algorithm

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