Assessment of Noise Impact in Sample Entropy for the Non-invasive Organization Estimation of Atrial Fibrillation

In recent studies, Sample Entropy (SampEn) has demonstrated that can be a very promising non-linear index to assess atrial fibrillation (AF) organization from surface ECG recordings. However, non-linear regularity metrics are notably sensitive to noise. Thereby, in the present work, the effect that noise provokes in AF organization estimation based on SampEn is analyzed. Given that AF organization was estimated by computing SampEn over the atrial activity (AA) signal, to evaluate the noise impact on AA regularity, 25 synthetic signals with different organization degrees were generated following a published model. Noise coming from real ECG recordings with different energy levels was added to the synthesized AA signals to obtain different signal to noise ratios (SNR). Results showed that SampEn, i.e., the AA irregularity, increased with noise, thus hiding the differences between organized and disorganized recordings. Precisely, in the presence of noise, SampEn values were increased, in average, by factors of 1.64, 4.46, 9.46 and 14.23 for SNRs of 24, 15, 9 and 3 dB, respectively. As a conclusion, a successful AF organization evaluation via SampEn requires a proper noise reduction in the AA signal.

[1]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[2]  Silvia G. Priori,et al.  ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association task force on practice guidelines and the European society of cardiology committee for PRAC , 2006 .

[3]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[4]  David E. Haines,et al.  Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy , 2001, IEEE Transactions on Biomedical Engineering.

[5]  E.J. Berbari,et al.  A high-temporal resolution algorithm for quantifying organization during atrial fibrillation , 1999, IEEE Transactions on Biomedical Engineering.

[6]  Leif Sörnmo,et al.  Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties , 2001, IEEE Transactions on Biomedical Engineering.

[7]  Diks,et al.  Efficient implementation of the gaussian kernel algorithm in estimating invariants and noise level from noisy time series data , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[8]  L. Sörnmo,et al.  Sampling rate and the estimation of ensemble variability for repetitive signals , 2006, Medical and Biological Engineering and Computing.

[9]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[10]  Haitham M. Al-Angari,et al.  Atrial fibrillation and waveform characterization. A time domain perspective in the surface ECG. , 2006, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[11]  Leif Sörnmo,et al.  Predicting spontaneous termination of atrial fibrillation using the surface ECG. , 2006, Medical engineering & physics.

[12]  P. Langley,et al.  Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications. , 2006, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[13]  Raúl Alcaraz,et al.  Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation. , 2009, Medical engineering & physics.

[14]  Leif Sörnmo,et al.  Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation , 2001, IEEE Transactions on Biomedical Engineering.

[15]  Haitham M. Al-Angari,et al.  Atrial fibrillation and waveform characterization , 2006, IEEE Engineering in Medicine and Biology Magazine.

[16]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[17]  L. Sörnmo,et al.  Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method. , 1998, Cardiovascular research.