An Effective Approach for Sleep Stage Classification Based on PSG Recordings

Authors

  • Mayuri A. Rakhonde  Master of Technology Scholar, CSE, GCOE, Amravati, Amravati, Maharashtra, India
  • Prof. Ravi V. Mante  Assistant Professor, Computer Science and Engineering, GCOE, Amravati Amravati, Maharashtra, India
  • Dr. Kishor P. Wagh  Assistant Professor, Information Technology, GCOE, Amravati, Amravati, Maharashtra, India

Keywords:

Polysomnography, Sleep Stage, Stochastic Gradient Descent, Power Spectral Density

Abstract

A person spend his one-third of life in sleep. So, paying attention on sleep is necessary at present times. Appropriate scoring of sleep stages is essential part in recognition of particular sleep disorder. Sleep stage classification is the process of categorizing polysomnographic (PSG) recordings into different classes. PSG contains EEG, EMG, EOG signals. In proposed methodology, Power spectral density is used to extract power features of EEG and EMG signals. A machine learning model of Stochastic Gradient Descent algorithm is used for classifying extracted features into multi-class sleep stages.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-04-30

Issue

Volume 6, Issue 2, March-April-2020

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mayuri A. Rakhonde, Prof. Ravi V. Mante, Dr. Kishor P. Wagh, " An Effective Approach for Sleep Stage Classification Based on PSG Recordings , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.504-508, March-April-2020.