Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. ECG Dataset
2.3. DeepPLM Model
2.4. Implementation
2.5. Evaluation Index
3. Results
3.1. Performance of the Single-Lead ECG-Based Detection
3.2. Performance of the DeepPLM Model Optimization
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Characteristics | Normal | PLM |
---|---|---|
Subjects (N) | 26 | 26 |
Age (years) | 76.12 ± 5.51 | 76.08 ± 5.11 |
Periodic leg movement index (per hour) | 2.46 ± 4.16 | 57.88 ± 30.27 |
Body mass index (kg/m2) | 27.92 ± 3.12 | 29.15 ± 3.89 |
Sleep efficiency (%) | 74.35 ± 10.93 | 73.00 ± 11.34 |
Smoking status, n (%) | ||
Never Past | 12 (47.15%) 14 (53.85%) | 12 (56.0%) 14 (40.0%) |
Blood pressure | ||
Systolic Diastolic | 127.57 ± 12.82 66.85 ± 5.66 | 127.35 ± 19.08 68.81 ± 7.35 |
Datasets | Normal | PLM | Total |
---|---|---|---|
Training set | 33,280 | 33,280 | 66,560 |
Validation set | 8320 | 8320 | 16,640 |
Test set | 10,400 | 10,400 | 20,800 |
Total | 52,000 | 52,000 | 104,000 |
Datasets | Segment | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|---|
Training set | Normal | 0.94 | 0.97 | 0.96 | 0.89 |
PLM | 0.97 | 0.94 | 0.96 | ||
Validation set | Normal | 0.90 | 0.94 | 0.92 | 0.92 |
PLM | 0.94 | 0.90 | 0.92 | ||
Test set | Normal | 0.90 | 0.93 | 0.92 | 0.92 |
PLM | 0.93 | 0.90 | 0.92 |
Authors (Year of Publication) | No. of Subjects | Signal | Method | Results (F1-Score) |
---|---|---|---|---|
Wetter et al. (2004) [8] | 24 | EMG | EMG-based analytical method | 0.63 |
Ferri et al. (2005) [9] | 30 | EMG | Computer-assisted detection method | 0.72 |
Moore et al. (2014) [10] | 1833 | EMG, ECG | Ten-step PLM detection method | 0.79 |
Carvelli et al. (2020) [11] | 800 | EMG | CNN–LSTM model | 0.85 |
This work | 52 | ECG | CNN–LSTM model | 0.92 |
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Urtnasan, E.; Park, J.-U.; Lee, J.-H.; Koh, S.-B.; Lee, K.-J. Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals. Diagnostics 2022, 12, 2149. https://doi.org/10.3390/diagnostics12092149
Urtnasan E, Park J-U, Lee J-H, Koh S-B, Lee K-J. Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals. Diagnostics. 2022; 12(9):2149. https://doi.org/10.3390/diagnostics12092149
Chicago/Turabian StyleUrtnasan, Erdenebayar, Jong-Uk Park, Jung-Hun Lee, Sang-Baek Koh, and Kyoung-Joung Lee. 2022. "Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals" Diagnostics 12, no. 9: 2149. https://doi.org/10.3390/diagnostics12092149
APA StyleUrtnasan, E., Park, J. -U., Lee, J. -H., Koh, S. -B., & Lee, K. -J. (2022). Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals. Diagnostics, 12(9), 2149. https://doi.org/10.3390/diagnostics12092149