Res-BiANet: A Hybrid Deep Learning Model for Arrhythmia Detection Based on PPG Signal
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Overview of Proposed Model
2.3. Feature Extraction
2.3.1. Spatial Feature Extraction Module
2.3.2. Temporal Feature Extraction Module
2.4. Experimental Environment
2.5. Experimental Metrics
- Pre: the proportion of all predicted positive samples to the actual positive samples. The calculation formula is shown in Equation (4).
- 2.
- Sen: the proportion of samples that are actually positive and predicted to be positive. The calculation formula is shown in Equation (5).
- 3.
- Spe: the proportion of samples that are actually negative and predicted to be negative. The calculation formula is shown in Equation (6).
- 4.
- F1 score: the weighted average of model precision and recall. The calculation formula is shown in Equation (7).
- 5.
- Acc: the proportion of correctly predicted quantities to the total sample size. The calculation formula is shown in Equation (8).
3. Results
3.1. Model Training and Testing
3.2. Result of Ablation Experiment
- ResNet: Remove the temporal feature extraction module from the proposed model, leaving only the spatial feature extraction module.
- Res-BiLSTM: Remove the attention mechanism from the temporal feature extraction module to form a parallel network of ResNet and BiLSTM.
3.3. Result of Contrast Experiment
- The dataset we received is not complete. The dataset publicly available from Liu et al. [30] accounts for 40% of all data. A comparison under the same amount of data is not feasible.
- Prior to conducting this study, this publicly available dataset had not yet been found to be used in public studies.
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Label | Description | Number of Samples |
---|---|---|---|
SR | 0 | Sinus rhythm | 14,604 |
PVC | 1 | Premature ventricular contraction | 4425 |
PAC | 2 | Premature atrial contraction | 3773 |
VT | 3 | Ventricular tachycardia | 2179 |
SVT | 4 | Supraventricular tachycardia | 5677 |
AF | 5 | Atrial fibrillation | 16,169 |
Total | / | / | 46,827 |
Layers | Input Size | Output Size | Content |
---|---|---|---|
Layer 1 | 1 × 1000 | 64 × 500 | 1 × 7, 64, s 1 = 2, p 2 = 3 |
Layer 2 | 64 × 500 | 64 × 250 | Max pooling (3), s = 2, p = 1 |
Layer 3 | 64 × 250 | 64 × 250 | |
Layer 4 | 64 × 250 | 128 × 125 | |
Layer 5 | 128 × 125 | 256 × 63 |
Type | Pre (%) | Sen (%) | Spe (%) | F1 Score (%) | Acc (%) | Overall Acc (%) |
---|---|---|---|---|---|---|
SR | 98.04 | 99.52 | 99.10 | 98.77 | 99.52 | 92.38 |
PVC | 83.89 | 82.78 | 98.29 | 83.33 | 82.78 | |
PAC | 77.95 | 74.52 | 98.23 | 76.20 | 74.52 | |
VT | 87.80 | 71.34 | 99.48 | 78.72 | 71.34 | |
SVT | 88.53 | 88.16 | 98.35 | 88.34 | 88.16 | |
AF | 94.55 | 94.55 | 97.12 | 95.90 | 97.29 | |
Average | 88.46 | 85.15 | 98.43 | 86.88 | 85.60 |
Models | Pre (%) | Sen (%) | Spe (%) | F1 Score (%) | Acc (%) |
---|---|---|---|---|---|
ResNet | 83.09 ± 0.62 | 81.67 ± 0.68 | 97.87 ± 0.65 | 82.35 ± 0.60 | 89.64 ± 0.37 |
ResNet-BiLSTM | 85.33 ± 0.29 | 85.08 ± 0.45 | 98.30 ± 0.04 | 85.16 ± 0.32 | 91.43 ± 0.18 |
Res-BiANet | 86.68 ± 0.42 | 86.90 ± 0.45 | 98.46 ± 0.32 | 86.75 ± 0.23 | 92.22 ± 0.15 |
Models | Pre (%) | Sen (%) | Spe (%) | F1 Score (%) | Acc (%) |
---|---|---|---|---|---|
AlexNet | 69.90 ± 0.76 | 64.93 ± 0.62 | 95.54 ± 0.09 | 65.41 ± 0.48 | 79.89 ± 0.41 |
VGG16 | 80.35 ± 0.75 | 77.49 ± 0.94 | 97.36 ± 0.10 | 78.59 ± 0.86 | 87.34 ± 0.44 |
ResNet18 | 80.74 ± 0.19 | 81.02 ± 0.69 | 97.75 ± 0.04 | 80.82 ± 0.34 | 88.67 ± 0.14 |
Res-BiANet | 86.68 ± 0.42 | 86.90 ± 0.45 | 98.46 ± 0.32 | 86.75 ± 0.23 | 92.22 ± 0.15 |
References | Database | Number of Subjects | Data Volume | Detection | Method | Result |
---|---|---|---|---|---|---|
Shashikumar et al. [27] | Self-generated database | 98 | 98 | AF | CNN | AUC: 0.95 Acc: 91.8% |
Cheng et al. [28] | MIMIC-III waveform database; IEEE TAME Respiratory Rate Benchmark dataset; Synthetic dataset | 102 | 28,440 | AF | 2D-CNN + LSTM | Sen: 98.00% Spe: 98.07% F1 score: 98.13% Acc: 98.21% AUC: 0.9959 |
Aliamiri et al. [29] | Self-generated database | 19 | 1443 | AF | CNN + RNN | AUC:99.67% Acc: 98.19% |
Neha et al. [23] | PhysioNet MIMIC-II database | 23 | 670 | PVC, AFl, ST, and normal sinus rhythm | ANN | Pre: 96% Sen: 97% Spe: 97% F1 score: 96% Acc: 95.97% |
Liu et al. [30] | Self-generated database | 228 | 118,217 | PVC, PAC, VT, SVT, and AF | DCNN | Pre: 75.2% Sen: 75.8% Spe: 96.9% Acc: 85.0% |
This work | From the dataset in Ref. [30] | 91 | 46,827 | PVC, PAC, VT, SVT, and AF | Res-BiANet | Pre: 88.46% Sen: 85.15% Spe: 98.43% F1 score: 86.88% Acc: 92.38% |
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Wu, Y.; Tang, Q.; Zhan, W.; Li, S.; Chen, Z. Res-BiANet: A Hybrid Deep Learning Model for Arrhythmia Detection Based on PPG Signal. Electronics 2024, 13, 665. https://doi.org/10.3390/electronics13030665
Wu Y, Tang Q, Zhan W, Li S, Chen Z. Res-BiANet: A Hybrid Deep Learning Model for Arrhythmia Detection Based on PPG Signal. Electronics. 2024; 13(3):665. https://doi.org/10.3390/electronics13030665
Chicago/Turabian StyleWu, Yankun, Qunfeng Tang, Weizong Zhan, Shiyong Li, and Zhencheng Chen. 2024. "Res-BiANet: A Hybrid Deep Learning Model for Arrhythmia Detection Based on PPG Signal" Electronics 13, no. 3: 665. https://doi.org/10.3390/electronics13030665
APA StyleWu, Y., Tang, Q., Zhan, W., Li, S., & Chen, Z. (2024). Res-BiANet: A Hybrid Deep Learning Model for Arrhythmia Detection Based on PPG Signal. Electronics, 13(3), 665. https://doi.org/10.3390/electronics13030665