Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation
Abstract
:1. Introduction
2. Results
2.1. Overview
2.2. Patient Characteristics
2.3. DL Model Performance
3. Discussion
3.1. Artificial Intelligence in Cardiology
3.2. AF Signals
3.3. Rotational Activity Detection Analysis
3.4. Clinical Implications of Rotational Activity
3.5. Study Limitations
4. Materials and Methods
4.1. Patient Cohort
4.2. Signal Database
4.3. Signal Pre-Processing
4.4. Classification Models
4.4.1. SimpleCNN
4.4.2. ATI-CNN
4.4.3. CRNN
4.5. Implementation and Specifications
4.6. Statistical Analysis and Performance Metrics
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics | All Patients | Training Set | Test Set | p-Value |
---|---|---|---|---|
N | 75 (100.0) | 43 | 5 | - |
Age (years) | 60.7 ± 9.7 | 61.4 ± 9.5 | 60.0 ± 4.1 | 0.7509 |
Sex | ||||
Men | 56 (74.7) | 37 (86.0) | 3 (60.0) | 0.1389 |
Women | 19 (25.3) | 6 (14.0) | 2 (40.0) | 0.1389 |
Atrial volume (cm3) | 148.5 ± 39.4 | 158.6 ± 40.5 | 144.4 ± 37.7 | 0.4649 |
Diagnosis of AF (years) | 3.1 ± 3.5 | 3.6 ± 2.5 | 1.8 ± 1.5 | 0.1594 |
Comorbidities | ||||
BSA (m2) | 2.0 ± 0.2 | 2.1 ± 0.2 | 2.1 ± 0.1 | 0.6083 |
CHA2DS2-VASc | 1.8 ± 1.5 | 1.6 ± 1.4 | 2.0 ± 1.3 | 0.5403 |
COPD | 4 (5.3) | 4 (9.0) | 0 (0.0) | 0.4777 |
Diabetes mellitus | 12 (16.0) | 6 (14.0) | 1 (20.0) | 0.7188 |
Dyslipidemia | 26 (34.7) | 16 (37.0) | 3 (60.0) | 0.3222 |
Heart failure | 13 (17.3) | 8 (16.6) | 1 (20.0) | 0.9362 |
Hypertension | 36 (48.04) | 21 (49.0) | 3 (60.0) | 0.6384 |
Obstructive sleep apnea | 16 (21.3) | 10 (23.0) | 0 (0.0) | 0.2263 |
SHD | 22 (29.3) | 12 (28.0) | 3 (60.0) | 0.1416 |
Stroke | 4 (5.33) | 4 (9.0) | 0 (0.0) | 0.4777 |
Signal acquisitions (per patient) | ||||
Number of acquisitions | 37.2 ± 14.7 | 39.9 ± 13.6 | 35.0 ± 8.6 | 0.4471 |
Number of rotational events | 51.2 ± 112.4 | 66.5 ± 111.9 | 50.4 ± 57.2 | 0.7568 |
Rotor cycle duration (ms) | 166.8 ± 36.1 | 167.0 ± 36.4 | 164.4 ± 31.1 | 0.9073 |
DL Model | Input Data | Signal Length | Sampling Frequency | Validation Accuracy | Test Accuracy | Precision | Recall | Specificity | MCC |
---|---|---|---|---|---|---|---|---|---|
(Type) | (ms) | (Hz) | (%) | (%) | (%) | (%) | (%) | ||
SimpleCNN | uEGMs | 500 | 500 | 49.83 | 49.93 | 49.96 | 77.82 | 22.05 | −0.002 |
250 | 50.99 | 49.07 | 49.25 | 60.72 | 37.43 | −0.019 | |||
100 | 53.54 | 52.13 | 52.26 | 49.38 | 54.89 | 0.043 | |||
2500 | 500 | 51.23 | 49.97 | 49.94 | 26.88 | 73.05 | −0.001 | ||
250 | 48.82 | 50.62 | 50.37 | 84.33 | 16.91 | 0.017 | |||
100 | 50.20 | 51.24 | 51.15 | 55.00 | 47.48 | 0.025 | |||
bEGMs | 500 | 500 | 50.00 | 50.00 | 50.00 | 100.00 | 0.00 | 0.000 | |
250 | 50.00 | 50.01 | 50.00 | 100.00 | 0.01 | 0.008 | |||
100 | 48.96 | 49.46 | 49.27 | 36.73 | 62.18 | −0.011 | |||
2500 | 500 | 50.00 | 50.00 | 50.00 | 100.00 | 0.00 | 0.000 | ||
250 | 50.00 | 50.00 | 50.00 | 100.00 | 0.00 | 0.000 | |||
100 | 50.00 | 50.05 | 50.03 | 100.00 | 0.10 | 0.022 | |||
uLATs | 500 | 500 | 57.90 | 55.86 | 55.10 | 63.32 | 48.39 | 0.118 | |
250 | 55.97 | 53.21 | 52.87 | 59.08 | 47.34 | 0.065 | |||
100 | 53.61 | 52.51 | 52.45 | 53.64 | 51.38 | 0.050 | |||
2500 | 500 | 51.07 | 50.77 | 51.14 | 34.67 | 66.88 | 0.016 | ||
250 | 51.36 | 49.80 | 49.78 | 44.77 | 54.84 | −0.004 | |||
100 | 52.16 | 48.59 | 48.62 | 49.97 | 47.20 | −0.028 | |||
ATI-CNN | uEGMs | 500 | 500 | 64.00 | 58.56 | 70.63 | 29.32 | 87.80 | 0.211 |
250 | 58.82 | 56.83 | 73.20 | 21.54 | 92.11 | 0.193 | |||
100 | 62.00 | 58.81 | 66.80 | 35.05 | 82.57 | 0.200 | |||
2500 | 500 | 63.37 | 59.28 | 68.18 | 34.80 | 83.76 | 0.213 | ||
250 | 59.85 | 58.83 | 71.11 | 29.73 | 87.92 | 0.217 | |||
100 | 54.33 | 55.67 | 66.41 | 22.95 | 88.39 | 0.150 | |||
bEGMs | 500 | 500 | 62.69 | 63.30 | 71.97 | 43.56 | 83.04 | 0.289 | |
250 | 67.60 | 58.75 | 68.16 | 32.86 | 84.65 | 0.205 | |||
100 | 67.42 | 62.00 | 69.78 | 42.33 | 81.67 | 0.261 | |||
2500 | 500 | 64.01 | 59.85 | 63.46 | 46.44 | 73.26 | 0.204 | ||
250 | 65.49 | 63.05 | 65.09 | 56.31 | 69.80 | 0.263 | |||
100 | 65.74 | 58.96 | 57.69 | 67.21 | 50.70 | 0.182 | |||
uLATs | 500 | 500 | 64.14 | 63.29 | 69.56 | 47.25 | 79.33 | 0.281 | |
250 | 70.56 | 65.36 | 68.63 | 56.57 | 74.14 | 0.312 | |||
100 | 76.44 | 70.03 | 69.48 | 71.44 | 68.62 | 0.401 | |||
2500 | 500 | - | - | - | - | - | - 1 | ||
250 | 62.39 | 54.23 | 75.75 | 12.43 | 96.02 | 0.154 | |||
100 | 70.89 | 65.46 | 59.85 | 93.95 | 36.97 | 0.376 | |||
CRNN | uEGMs | 500 | 500 | 71.76 | 68.40 | 77.91 | 51.36 | 85.44 | 0.400 |
250 | 65.76 | 63.12 | 77.17 | 37.25 | 88.98 | 0.310 | |||
100 | 56.25 | 64.81 | 70.93 | 50.18 | 79.44 | 0.310 | |||
2500 | 500 | 63.05 | 60.55 | 46.73 | 64.73 | 58.27 | 0.220 | ||
250 | 71.40 | 63.66 | 70.90 | 46.34 | 80.98 | 0.290 | |||
100 | 64.86 | 61.71 | 58.20 | 83.11 | 40.31 | 0.260 | |||
bEGMs | 500 | 500 | 78.39 | 72.52 | 67.12 | 89.80 | 50.24 | 0.410 | |
250 | 72.57 | 59.88 | 76.89 | 28.24 | 91.51 | 0.260 | |||
100 | 73.18 | 65.48 | 67.85 | 58.82 | 72.13 | 0.310 | |||
2500 | 500 | 80.93 | 80.04 | 74.14 | 92.27 | 67.82 | 0.680 | ||
250 | 79.23 | 63.96 | 63.28 | 66.50 | 61.42 | 0.280 | |||
100 | 74.64 | 60.33 | 63.47 | 48.70 | 71.97 | 0.210 | |||
uLATs | 500 | 500 | 74.28 | 68.72 | 67.09 | 73.46 | 63.98 | 0.376 | |
250 | 69.60 | 61.87 | 57.30 | 93.21 | 30.54 | 0.305 | |||
100 | 73.15 | 64.61 | 60.96 | 81.27 | 47.95 | 0.310 | |||
2500 | 500 | 50.48 | 49.84 | 30.77 | 0.26 | 99.41 | −0.025 | ||
250 | 67.86 | 56.94 | 53.87 | 96.68 | 17.20 | 0.229 | |||
100 | 70.71 | 60.23 | 71.01 | 34.57 | 85.89 | 0.238 |
Layer | Kernel Size (H, W, D) | Stride (H, W) | Activations 1 | |
---|---|---|---|---|
Unipolar EGMs, LATs | Bipolar EGMs | |||
Input | - | - | 1250 × 20 × 1 | 1250 × 15 × 1 |
Zero Padding 2D 1 | (37, 0, 0) | 1324 × 20 × 1 | 1324 × 15 × 1 | |
Batch Normalization 1 | - | - | ||
Dropout 1 | - | - | 1324 × 20 × 1 | 1324 × 15 × 1 |
Conv2D 1 | (5, 23, 32) | (1, 1) | 1324 × 20 × 32 | 1324 × 15 × 32 |
Batch Normalization 2 | - | - | 1324 × 20 × 32 | 1324 × 15 × 32 |
LeakyReLU 1 | - | - | 1324 × 20 × 32 | 1324 × 15 × 32 |
Max Pooling 2D 1 | (2, 2, 32) | (2, 1) | 662 × 19 × 32 | 662 × 14 × 32 |
Dropout 2 | - | - | 662 × 19 × 32 | 662 × 14 × 32 |
Conv2D 2 | (5, 23, 64) | (1, 1) | 662 × 19 × 64 | 662 × 14 × 64 |
Batch Normalization 3 | - | - | 662 × 19 × 64 | 662 × 14 × 64 |
LeakyReLU 2 | - | - | 662 × 19 × 64 | 662 × 14 × 64 |
Max Pooling 2D 3 | (3, 3, 64) | (3, 3) | 220 × 6 × 64 | 220 × 4 × 64 |
Dropout 3 | - | - | 220 × 6 × 64 | 220 × 4 × 64 |
Conv2D 3 | (5, 23, 64) | (1, 1) | 220 × 6 × 64 | 220 × 4 × 64 |
Batch Normalization 4 | - | - | 220 × 6 × 64 | 220 × 4 × 64 |
Leaky ReLU 3 | - | - | 220 × 6 × 64 | 220 × 4 × 64 |
Max Pooling 2D 3 | (4, 4, 64) | (4, 4) | 55 × 1 × 64 | 55 × 1 × 64 |
Dropout 4 | - | - | 55 × 1 × 64 | 55 × 1 × 64 |
Reshape 1 | - | - | 55 × 64 | 55 × 64 |
GRU 1 | 32 units | - | 55 × 32 | 55 × 32 |
GRU 2 | 32 units | - | 32 | 32 |
Dropout 5 | - | - | 32 | 32 |
Dense | - | - | 1 | 1 |
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Ríos-Muñoz, G.R.; Fernández-Avilés, F.; Arenal, Á. Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation. Int. J. Mol. Sci. 2022, 23, 4216. https://doi.org/10.3390/ijms23084216
Ríos-Muñoz GR, Fernández-Avilés F, Arenal Á. Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation. International Journal of Molecular Sciences. 2022; 23(8):4216. https://doi.org/10.3390/ijms23084216
Chicago/Turabian StyleRíos-Muñoz, Gonzalo Ricardo, Francisco Fernández-Avilés, and Ángel Arenal. 2022. "Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation" International Journal of Molecular Sciences 23, no. 8: 4216. https://doi.org/10.3390/ijms23084216
APA StyleRíos-Muñoz, G. R., Fernández-Avilés, F., & Arenal, Á. (2022). Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation. International Journal of Molecular Sciences, 23(8), 4216. https://doi.org/10.3390/ijms23084216