Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Data Processing
2.3. Wavelet Entropy Analysis
2.4. Performance Assessment and Statistical Analysis
3. Results
Metric | SR beats | AF beats | p | Threshold | Accuracy (%) | Sensitivity (%) | Specificity (%) |
---|---|---|---|---|---|---|---|
WE | 0.431 ± 0.135 | 0.952 ± 0.240 | 0.639 | 95.62 | 96.07 | 95.23 | |
0.881 ± 0.051 | 0.601 ± 0.190 | 0.804 | 94.89 | 94.61 | 95.19 | ||
0.089 ± 0.040 | 0.215 ± 0.105 | 0.110 | 87.60 | 94.78 | 81.03 | ||
0.023 ± 0.015 | 0.125 ± 0.096 | 0.038 | 91.04 | 90.69 | 91.35 | ||
0.007 ± 0.006 | 0.060 ± 0.057 | 0.011 | 88.75 | 92.10 | 85.68 |
Metric | SR beats | AF beats | p | Accuracy (%) | Sensitivity (%) | Specificity (%) |
---|---|---|---|---|---|---|
WE | 0.420 ± 0.124 | 1.017 ± 0.208 | 95.28 | 96.47 | 94.19 | |
0.886 ± 0.044 | 0.546 ± 0.183 | 94.48 | 93.19 | 95.89 | ||
0.086 ± 0.032 | 0.249 ± 0.118 | 85.51 | 81.66 | 89.03 | ||
0.022 ± 0.017 | 0.141 ± 0.089 | 90.84 | 88.15 | 93.29 | ||
0.007 ± 0.006 | 0.065 ± 0.060 | 87.69 | 81.80 | 93.03 |
4. Discussion
Algorithm | Sensitivity | Specificity | Accuracy | Delay | Methodology |
---|---|---|---|---|---|
Slocum et al. [23] | 62.80% | 77.46% | – | – | Frequency analysis of the residual signal after QRST cancellation. |
Tateno and Glass [27] | 94.40% | 97.20% | – | – | Kolmogorov–Smirnov test from histograms of difference between two successive RR intervals. |
Dash et al. [28] | 94.40% | 95.10% | – | 18 beats | Analysis of randomness, variability and complexity of RR interval combined by simple rules. |
Babaeizadeh et al. [34] | 92.00% | 95.50% | – | – | Information combined by a decision tree from RR and PR intervals variability and the P-wave similarity. |
Huang et al. [29] | 96.10% | 98.10% | – | 70 beats | Analysis of the density histogram of delta RR intervals with several statistical features. |
Lake and Moorman [30] | 91.00% | 94.00% | – | 12 beats | Analysis of RR interval regularity by using the coefficient of sample entropy. |
Jiang et al. [36] | 98.20% | 97.50% | – | – | Analysis of RR interval density histograms combined with the P-wave presence study. |
Lee et al. [56] | 98.20% | 97.70% | – | ≈ 12 beats | Analysis of RR interval variability with time-varying coherence functions and Shannon entropy. |
Zhou et al. [32] | 96.89% | 98.25% | 97.67% | – | Analysis of RR interval regularity by applying nonlinear/linear integer filters, symbolic dynamics and Shannon entropy. |
Ladavich and Ghoraani [24] | 98.09% | 91.66% | 93.22% | 7 beats | Analysis of the P-wave absence by using a Gaussian mixture model. |
This work | 96.47% | 94.19% | 95.28% | 5 beats | Analysis of the median TQ interval by using WE. |
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Ródenas, J.; García, M.; Alcaraz, R.; Rieta, J.J. Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms. Entropy 2015, 17, 6179-6199. https://doi.org/10.3390/e17096179
Ródenas J, García M, Alcaraz R, Rieta JJ. Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms. Entropy. 2015; 17(9):6179-6199. https://doi.org/10.3390/e17096179
Chicago/Turabian StyleRódenas, Juan, Manuel García, Raúl Alcaraz, and José J. Rieta. 2015. "Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms" Entropy 17, no. 9: 6179-6199. https://doi.org/10.3390/e17096179
APA StyleRódenas, J., García, M., Alcaraz, R., & Rieta, J. J. (2015). Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms. Entropy, 17(9), 6179-6199. https://doi.org/10.3390/e17096179