Journal of Southern Medical University ›› 2025, Vol. 45 ›› Issue (3): 650-660.doi: 10.12122/j.issn.1673-4254.2025.03.23
Yong HONG(), Xin ZHANG, Mingjun LIN, Qiucen WU, Chaomin CHEN(
)
Received:
2024-12-04
Online:
2025-03-20
Published:
2025-03-28
Contact:
Chaomin CHEN
E-mail:3547475276@qq.com;571611621@qq.com
Yong HONG, Xin ZHANG, Mingjun LIN, Qiucen WU, Chaomin CHEN. A lightweight classification network for single-lead atrial fibrillation based on depthwise separable convolution and attention mechanism[J]. Journal of Southern Medical University, 2025, 45(3): 650-660.
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URL: https://www.j-smu.com/EN/10.12122/j.issn.1673-4254.2025.03.23
Method | Input | Dataset | Task | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|---|---|---|
MGNN[ | 32 RR | AFDB | AF vs Non-AF | 97.07 | 94.95 | 97.77 | - | 93.91 |
MT-DCNN[ | 30 s ECG | LTAFDB | AF vs Non-AF | 97.1 | 96.5 | 97.9 | - | 97.5 |
LDSNet[ | 15 s ECG | AFDB | AF vs Non-AF | 94.57 | 99.15 | 93.03 | - | - |
IMC-ResNet[ | 15 s ECG | AFDB | AF vs NSR | 96.18 | 99.97 | 94.36 | 89.51 | 94.45 |
ResNet18 | 30 s ECG | LTAFDB | AF vs NSR | 94.82 | 94.27 | 95.29 | 95.94 | 94.60 |
MobileNetV1 | 30 s ECG | LTAFDB | AF vs NSR | 95.64 | 97.86 | 97.80 | 94.17 | 95.80 |
EfficientNetB0 | 30 s ECG | LTAFDB | AF vs NSR | 95.64 | 97.94 | 97.95 | 94.40 | 95.92 |
DSC-AttNet,2024 | 30 s ECG | LTAFDB | AF vs NSR | 97.33 | 97.50 | 97.63 | 97.30 | 97.31 |
Tab.1 Comparison of classification performance among different models (%)
Method | Input | Dataset | Task | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|---|---|---|
MGNN[ | 32 RR | AFDB | AF vs Non-AF | 97.07 | 94.95 | 97.77 | - | 93.91 |
MT-DCNN[ | 30 s ECG | LTAFDB | AF vs Non-AF | 97.1 | 96.5 | 97.9 | - | 97.5 |
LDSNet[ | 15 s ECG | AFDB | AF vs Non-AF | 94.57 | 99.15 | 93.03 | - | - |
IMC-ResNet[ | 15 s ECG | AFDB | AF vs NSR | 96.18 | 99.97 | 94.36 | 89.51 | 94.45 |
ResNet18 | 30 s ECG | LTAFDB | AF vs NSR | 94.82 | 94.27 | 95.29 | 95.94 | 94.60 |
MobileNetV1 | 30 s ECG | LTAFDB | AF vs NSR | 95.64 | 97.86 | 97.80 | 94.17 | 95.80 |
EfficientNetB0 | 30 s ECG | LTAFDB | AF vs NSR | 95.64 | 97.94 | 97.95 | 94.40 | 95.92 |
DSC-AttNet,2024 | 30 s ECG | LTAFDB | AF vs NSR | 97.33 | 97.50 | 97.63 | 97.30 | 97.31 |
Fig.9 Evolution process of NSR-class features and AF-class features. A: Visualization of the output features from the first standard convolution layer. B: Visualization of the output features from the global attention module.
Method | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|
ResNet18 | 87.24 | 83.97 | 91.90 | 78.62 | 81.21 |
MobileNetV1 | 91.27 | 96.74 | 98.24 | 80.56 | 87.91 |
EfficientNetB0 | 90.15 | 83.88 | 92.21 | 85.81 | 84.84 |
DSC-AttNet | 92.78 | 89.98 | 95.05 | 88.26 | 89.11 |
Tab.2 Comparison of classification performance among different models on external test set AFDB (%)
Method | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|
ResNet18 | 87.24 | 83.97 | 91.90 | 78.62 | 81.21 |
MobileNetV1 | 91.27 | 96.74 | 98.24 | 80.56 | 87.91 |
EfficientNetB0 | 90.15 | 83.88 | 92.21 | 85.81 | 84.84 |
DSC-AttNet | 92.78 | 89.98 | 95.05 | 88.26 | 89.11 |
Method | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|
ResNet18 | 99.80 | - | 1.0 | - | - |
MobileNetV1 | 99.94 | - | 1.0 | - | - |
EfficientNetB0 | 99.77 | - | 1.0 | - | - |
DSC-AttNet | 99.97 | - | 1.0 | - | - |
Tab.3 Comparison of classification performance among different models on external test set NSRDB (%)
Method | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|
ResNet18 | 99.80 | - | 1.0 | - | - |
MobileNetV1 | 99.94 | - | 1.0 | - | - |
EfficientNetB0 | 99.77 | - | 1.0 | - | - |
DSC-AttNet | 99.97 | - | 1.0 | - | - |
Method | Input size | MACs (G) | Params (M) |
---|---|---|---|
MGNN[ | 1*32 | 0.02 | 1.49 |
LDSNet[ | 1*3000 | 0.33 | 0.2 |
ResNet18 | 1*3840 | 183.65 | 4.20 |
MobileNetV1 | 1*3840 | 39.46 | 3.19 |
EfficientNetB0 | 1*3840 | 31.68 | 7.00 |
DSC-AttNet, 2024 | 1*3840 | 27.19 | 1.01 |
Tab.4 Comparison of complexity among different models
Method | Input size | MACs (G) | Params (M) |
---|---|---|---|
MGNN[ | 1*32 | 0.02 | 1.49 |
LDSNet[ | 1*3000 | 0.33 | 0.2 |
ResNet18 | 1*3840 | 183.65 | 4.20 |
MobileNetV1 | 1*3840 | 39.46 | 3.19 |
EfficientNetB0 | 1*3840 | 31.68 | 7.00 |
DSC-AttNet, 2024 | 1*3840 | 27.19 | 1.01 |
Method | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|
Baseline | 97.13 | 97.70 | 97.80 | 96.89 | 97.15 |
+CBAM | 97.21 | 96.30 | 96.77 | 98.07 | 97.04 |
+Global Attention | 95.90 | 96.19 | 96.64 | 96.08 | 95.87 |
DSC-AttNet | 97.33 | 97.50 | 97.63 | 97.30 | 97.31 |
Tab.5 Results of ablation study (%)
Method | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|
Baseline | 97.13 | 97.70 | 97.80 | 96.89 | 97.15 |
+CBAM | 97.21 | 96.30 | 96.77 | 98.07 | 97.04 |
+Global Attention | 95.90 | 96.19 | 96.64 | 96.08 | 95.87 |
DSC-AttNet | 97.33 | 97.50 | 97.63 | 97.30 | 97.31 |
Method | Data input | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|---|
ResNet18 | 10s ECG | 61.97 | 55.29 | 90.41 | 60.30 | 54.23 |
MobileNetV1 | 10s ECG | 75.65 | 65.57 | 93.69 | 66.80 | 64.18 |
EfficientNetB0 | 10s ECG | 81.38 | 70.25 | 95.19 | 69.09 | 68.62 |
DSC-AttNet | 10s ECG | 71.20 | 60.98 | 92.44 | 77.13 | 59.19 |
Tab.6 Comparison of the performance of different models for 5 classification tasks on LTAFDB (%)
Method | Data input | Acc | Sen | Spe | Pre | F1 |
---|---|---|---|---|---|---|
ResNet18 | 10s ECG | 61.97 | 55.29 | 90.41 | 60.30 | 54.23 |
MobileNetV1 | 10s ECG | 75.65 | 65.57 | 93.69 | 66.80 | 64.18 |
EfficientNetB0 | 10s ECG | 81.38 | 70.25 | 95.19 | 69.09 | 68.62 |
DSC-AttNet | 10s ECG | 71.20 | 60.98 | 92.44 | 77.13 | 59.19 |
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