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Analysis of EEG Signal to Classify Sleep Stages Using Machine Learning

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Abstract

Automated classification of sleep stages is in demand to overcome the limitations of manual sleep stage classification. Analyzing sleep stages manually using neurophysiological signals and inspecting visually is very difficult, time-consuming process. Many techniques have been proposed already in the past two decades. Sleep experts, physicians do not have assurance with such techniques concerned with accuracy, specificity and sensitivity. The aim of the paper is to propose an efficient technique for sleep stage classification based on Electroencephalogram (EEG) signals analysis using machine learning algorithms by considering 10 s of epochs. EEG signals have played significant role in automatic sleep stage classification. EEG signals are filtered and decomposed into frequency sub-bands using band-pass filter. Statistical features are extracted and trained with Decision Tree, Support Vector Machine and Random Forest algorithms with different testing dataset percentage. Results show Random Forest algorithm achieves 97.8% of accuracy.

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Abbreviations

EEG:

Electroencephalogram

PSG:

Polysomnography

ECG:

Electrocardiogram

EOG:

Electrooculogram

EMG:

Electromyogram

REM:

Rapid eye movement

NREM:

Non rapid eye movement

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Acknowledgements

We thank Dr. D. A. Torse and Dr. Anil B. Gavade for their valuable guidance and inputs.

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No funding been considered for this research work

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka, India

    Sagar Santaji & Veena Desai

Authors
  1. Sagar Santaji
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  2. Veena Desai
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Contributions

Analysis of EEG data is carried out using preprocessing and extracting features. EEG data is classified using machine learning algorithms.

Corresponding author

Correspondence to Sagar Santaji.

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Ethical statement

Clinical data referred to carryout the analysis of sleep stages is collected by consulting Dr.Chandrashekhar, Assosciate Professor, Belgaum Institute of Medical Sciences, Belagavi, Karnataka, India.

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Cite this article

Santaji, S., Desai, V. Analysis of EEG Signal to Classify Sleep Stages Using Machine Learning. Sleep Vigilance 4, 145–152 (2020). https://doi.org/10.1007/s41782-020-00101-9

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