Sleep Stage Classification via Kernel Extreme Learning Machine with Particle Swarm Optimization Using Heart Rate Variability as Features

The sleep stage classification is the most important step in the sleep research. It does not only provide a reference for evaluating the sleep quality clinically, but it also uses to diagnose the sleep disorders. Various methods have been developed for performing the sleep stage classification based on the electrocardiograms (ECGs). However, the computational complexity of these existing methods is very large. Also, the accuracy is still very low. To address this issue, this paper employs a kernel extreme learning machine (KELM) with the particle swarm optimization (PSO) for performing the sleep stage classification using the heart rate variability (HRV) as the features. The results show that the classification accuracies yielded by our proposed method for the three sleep stage classification and the four sleep stage classification are 80.18% and 76.02%, respectively. Obviously, our proposed method is superior to the existing methods.

• Publication year

  2022

• Venue

  2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST)

• Publication date

  2022-12-09

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/IAECST57965.2022.10062126
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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