Atrial activity selection for atrial fibrillation ECG recordings

Abstract 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...

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Bibliographic Details
Published in:Computers in biology and medicine 2013-10, Vol.43 (10), p.1628-1636
Main Authors: Donoso, Felipe I, Figueroa, Rosa L, Lecannelier, Eduardo A, Pino, Esteban J, Rojas, Alejandro J
Format: Article
Language:English
Subjects:
Algorithms
Ambulatory care
Atrial activity
Atrial fibrillation
Atrial Fibrillation - physiopathology
Blind source separation
Classification
Correlation
ECG
Electrocardiography - methods
Heart Atria - physiopathology
Humans
Internal Medicine
Kurtosis
Methods
Mortality
Other
Power spectral density
Principal Component Analysis
Principal components analysis
Random variables
Selection parameters
Signal Processing, Computer-Assisted
Studies
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Summary:Abstract 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 ρ = 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.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2013.08.002