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