Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Classification Model for Simulated Breathing Model
3. Results
4. Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Group Name | Input Size Output Size | Block Structure (Kernel Size, Number) | Block Numbers (113-Layers) |
---|---|---|---|
Stage 0 | 1 × 64 | 15, 16 | 1 |
Stage 1 | 164 132 | 11, 16 | 1 |
15, 16 | |||
11, 16 | |||
Stage 2 | 132 132 | 11, 16 | 12 |
13, 16 | |||
11, 64 | |||
Stage 3 | 132 116 | 11, 32 | 12 |
13, 32 | |||
11, 128 | |||
Stage 4 | 116 18 | 11, 64 | 12 |
13, 64 | |||
11, 256 | |||
Average pooling | 18 1256 | 18 | 1 |
Fully connected layer | 1256 Classes number | 1 |
Class | Train | Test |
---|---|---|
Baseline | 425 | 106 |
Loaded | 624 | 156 |
Rapid/shallow | 700 | 174 |
Method | Data Type | Mean Accuracy (%) | STD | Best Accuracy (%) |
---|---|---|---|---|
Random Forest | O2Hb | - | - | 87.00 |
Pre-ResNet with DS | O2Hb | 88.79 | 0.423 | 89.44 |
HHb | 86.28 | 0.550 | 87.16 | |
O2Hb and HHb | 88.02 | 0.490 | 88.76 | |
Pre-ResNet with Stage 1, (1 × 3) | O2Hb | 90.58 | 0.488 | 91.51 |
HHb | 89.63 | 0.639 | 90.37 | |
O2Hb and HHb | 90.23 | 0.658 | 91.28 | |
Pre-ResNet with Stage 1, (1 × 5) | O2Hb | 91.77 | 0.456 | 92.43 |
HHb | 89.68 | 0.490 | 90.60 | |
O2Hb and HHb | 90.78 | 0.523 | 91.74 |
Metric | Pre-ResNet with DS | Pre-ResNet with Stage 1, (1 × 3) | Pre-ResNet with Stage 1, (1 × 5) |
---|---|---|---|
Recall Baseline | 0.92 | 0.94 | 0.93 |
Recall Loaded | 0.81 | 0.85 | 0.90 |
Recall Rapid | 0.96 | 0.95 | 0.94 |
Balanced Accuracy | 89.66% | 91.33% | 92.33% |
Method | Data Type | Number of Parameters | FLOPS | Mean Accuracy (%) | STD | Best Accuracy (%) |
---|---|---|---|---|---|---|
Pre-ResNet with DS | O2Hb | 0.7 M | 15 M | 87.25 | 0.472 | 88.07 |
Pre-ResNet with Stage 1, (1 × 5) | 0.7 M | 15 M | 90.14 | 0.649 | 91.28 | |
EfficientNetV2 m with DS [55] | 52 M | 225 M | 91.05 | 0.562 | 91.97 | |
PyramidNet with DS [51] | 17 M | 396 M | 87.39 | 0.481 | 88.89 | |
CF-CNN with DS [52] | 29.7 M | 627 M | 89.27 | 0.531 | 90.74 |
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Park, J.; Mah, A.J.; Nguyen, T.; Park, S.; Ghazi Zadeh, L.; Shadgan, B.; Gandjbakhche, A.H. Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases. Sensors 2023, 23, 5592. https://doi.org/10.3390/s23125592
Park J, Mah AJ, Nguyen T, Park S, Ghazi Zadeh L, Shadgan B, Gandjbakhche AH. Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases. Sensors. 2023; 23(12):5592. https://doi.org/10.3390/s23125592
Chicago/Turabian StylePark, Jinho, Aaron James Mah, Thien Nguyen, Soongho Park, Leili Ghazi Zadeh, Babak Shadgan, and Amir H. Gandjbakhche. 2023. "Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases" Sensors 23, no. 12: 5592. https://doi.org/10.3390/s23125592
APA StylePark, J., Mah, A. J., Nguyen, T., Park, S., Ghazi Zadeh, L., Shadgan, B., & Gandjbakhche, A. H. (2023). Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases. Sensors, 23(12), 5592. https://doi.org/10.3390/s23125592