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Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection

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Abstract

Electroencephalographic (EEG) activity recorded during the entire sleep cycle reflects various complex processes associated with brain and exhibits a high degree of irregularity through various stages of sleep. The identification of transition from wakefulness to stage1 sleep is a challenging area of research for the biomedical community. In this paper, spectral entropy (SE) is used as a complexity measure to quantify irregularities in awake and stage1 sleep of 8-channel sleep EEG data from the polysomnographic recordings of ten healthy subjects. The SE measures of awake and stage1 sleep EEG data are estimated for each second and applied to a multilayer perceptron feed forward neural network (MLP-FF). The network is trained using back propagation algorithm for recognizing these two patterns. Initially, the MLP network is trained and tested for randomly chosen subject-wise combined datasets I and II and then for the combined large dataset III. In all cases, 60 % of the entire dataset is used for training while 20 % is used for testing and 20 % for validation. Results indicate that the MLP neural network learns with maximum testing accuracy of 95.9 % for dataset II. In the case of combined large dataset, the network performs with a maximum accuracy of 99.2 % with 100 hidden neurons. Results show that in channels O1, O2, F3 and F4 (A1, A2 as reference), the mean of the spectral entropy value is higher in awake state than in stage1 sleep indicating that the EEG becomes more regular and rhythmic as the subject attains stage1 sleep from wakefulness. However, in C3 and C4 the mean values of SE values are not very much discriminative of both groups. This may prove to be a very effective indicator for scoring the first two stages of sleep EEG and may be used to detect the transition from wakefulness to stage1 sleep.

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Acknowledgments

We thank Dr. Uma Maheshwari and Sleep Lab staff of MS Ramaiah Medical College and Hospitals, Bangalore, India, for providing us with the PSG recordings of sleep data.

Authors contribution

N. Sriram and T.K. Padma Shri contributed equally in terms of the data collection, analysis and performing the classification. Uma Maheshwari Krishnaswamy was involved in clinical validation of the data and ensuring the statistical analysis of the data. All the three authors contributed equally in terms of manuscript preparation and finalization.

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Authors and Affiliations

  1. Center for Medical Electronics and Computing, M.S.Ramaiah Institute of Technology (An Autonomous Institute, Affiliated to Visvesvaraya Technological University), Bangalore, Karnataka, 560054, India

    N. Sriraam

  2. Department of Electronics & Communication, Manipal Institute of Technology, Manipal University, Manipal, Karnataka, 576104, India

    T. K. Padma Shri

  3. Department of Electronics & Communication, SCSVMV University, Kanchipuram, Tamil Nadu, 631561, India

    T. K. Padma Shri

  4. Department of Pulmonary Medicine, St. Johns National Academy of Health Sciences, Bangalore, India

    Uma Maheshwari

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  1. N. Sriraam
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Correspondence to N. Sriraam.

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Sriraam, N., Padma Shri, T.K. & Maheshwari, U. Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection. Australas Phys Eng Sci Med 39, 797–806 (2016). https://doi.org/10.1007/s13246-016-0472-8

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  • DOI: https://doi.org/10.1007/s13246-016-0472-8

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