Key result
Improved BCG-based DCGAN and SVM models achieve ~76% accuracy for four-stage sleep classification.
Why the study?
Monitoring sleep quality and status is important to assess health conditions and prevent sleep apnea, with BCG-based sleep stage classification offering equipment simplicity and high positioning accuracy.
An improved DCGAN and optimal SVM model using BCG-derived HRV and RV features achieved up to 76.38% accuracy for four-stage sleep classification.
Supports BCG-based sleep staging feasibility; leaves open prospective validation before clinical use.
Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. Sleep stage classification based on Ballistocardiography (BCG) has attracted more attention due to its simplicity in equipment usage and high positioning accuracy. Firstly, this paper tries to reconstruct Electrocardiogram (ECG) signals from BCG signals using an improved Deep Convolutional Generative Adversarial Networks (DCGAN) model. Then an optimal Support Vector Machine (SVM) model is exploited for sleep stage classification, in which a Particle Swarm Optimization (PSO) is combined with a Genetic Algorithm (GA) to train the classifier. Finally, the accuracy of four-stage and six-stage SVM models are analyzed and compared using Heart Rate Variability (HRV), incorporating HRV and Respiratory Variability (RV) features. The results show that the accuracy of the four-stage and six-stage SVM models using RV features for sleep stage classification reaches 76.38% and 71.11%, respectively.
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Wu et al. (2023) studied Sleep stage classification. Sleep stage classification using improved DCGAN and SVM models based on BCG signals was evaluated on Accuracy of four-stage and six-stage SVM models using RV features. Sleep stage classification using an improved DCGAN and SVM model based on BCG signals achieved an accuracy of 76.38% for four-stage and 71.11% for six-stage classification.
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