Atrial activity selection for atrial fibrillation ECG recordings

作者：Donoso Felipe I^*; Figueroa Rosa L; Lecannelier Eduardo A; Pino Esteban J; Rojas Alejandro J

来源：Computers in Biology and Medicine, 2013, 43(10): 1628-1636.

DOI：10.1016/j.compbiomed.2013.08.002

• Atrial fibrillation
• Atrial activity
• ECG
• Blind source separation
• Kurtosis
• Power spectral density
• Correlation
• Selection parameters

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 CV! 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 p = 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.

• 出版日期2013-10-1