ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring
Abstract
:1. Introduction
2. Background and Related Work
3. Method
3.1. Proposed Architecture
3.2. Data Augmentation
3.3. Training Process
3.4. Network Evaluation
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Exp | #Blocks | #Filters | Filter Size | #Parms | Data Augmentation Factor | Epoch Size | F1 Score | Accuracy |
---|---|---|---|---|---|---|---|---|
4 | 16 | 10.2 K | class3:24, class4:12, class5:24 | 460 | ||||
3 | 16 | 6 K | class3:24, class4:12, class5:24 | 460 | ||||
5 | 16 | 15.4 K | class3:24, class4:12, class5:24 | 460 | ||||
4 | 8 | 2.7 K | class3:24, class4:12, class5:24 | 460 | ||||
4 | 32 | 39.9 K | class3:24, class4:12, class5:24 | 460 | ||||
4 | 16 | 14.9 K | class3:24, class4:12, class5:24 | 460 | ||||
4 | 32 | 58.4 K | class3:24, class4:12, class5:24 | 460 | ||||
3 | 16 | 8.6 K | class3:24, class4:12, class5:24 | 460 | ||||
3 | 16 | 12.2 K | class3:24, class4:12, class5:24 | 460 | ||||
3 | 32 | 23.3 K | class3:24, class4:12, class5:24 | 460 | ||||
4 | 16 | 10.2 K | class3:24, class4:24, class5:24 | 566 | ||||
4 | 16 | 10.2 K | class3:24, class4:06, class5:24 | 407 |
Class | Data Augmentation Type | ||
---|---|---|---|
Amplitude Shift | Time Shift | Amplification | |
PVC | |||
SP | |||
UB |
Class | Count | ||||
---|---|---|---|---|---|
Testing Set | Before Augmentation | After Augmentation | |||
Training Set | Total | Training Set | Total | ||
N | 292 | 2627 | 2919 | 2627 | 2919 |
Ron-T PVC | 177 | 1590 | 1767 | 1590 | 1767 |
PVC | 10 | 86 | 96 | 2150 | 2160 |
SP | 19 | 175 | 194 | 2287 | 2306 |
UB | 20 | 2 | 24 | 550 | 552 |
Total | 500 | 4500 | 5000 | 9204 | 9704 |
Architecture | Accuracy (%) | F1 Score (%) | #Parameters |
---|---|---|---|
TCN [57] | 94.2 | 89.0 | 14.88 K |
LSTM-FCN [58] | 94.1 | 72.5 | 404.74 K |
CCN [58] | 93.4 | 81.5 | 266.37 K |
LSTM [58] | 93.1 | 68.9 | 138.37 K |
1-NN (L2 dist.) [59] | 92.5 | 54.9 | 70 K |
Our TCN | 96.12 | 84.13 | 10.2 K |
Our TCN (First 3 classes) | 98.54 | 94.51 | 10.2 K |
Our TCN (Without Data Augmentation) | 93.4 | 70.18 | 10.2 K |
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Ismail, A.R.; Jovanovic, S.; Ramzan, N.; Rabah, H. ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring. Sensors 2023, 23, 1697. https://doi.org/10.3390/s23031697
Ismail AR, Jovanovic S, Ramzan N, Rabah H. ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring. Sensors. 2023; 23(3):1697. https://doi.org/10.3390/s23031697
Chicago/Turabian StyleIsmail, Ali Rida, Slavisa Jovanovic, Naeem Ramzan, and Hassan Rabah. 2023. "ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring" Sensors 23, no. 3: 1697. https://doi.org/10.3390/s23031697
APA StyleIsmail, A. R., Jovanovic, S., Ramzan, N., & Rabah, H. (2023). ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring. Sensors, 23(3), 1697. https://doi.org/10.3390/s23031697