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Cloud based ensemble machine learning approach for smart detection of epileptic seizures using higher order spectral analysis

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Abstract

The present paper proposes a smart framework for detection of epileptic seizures using the concepts of IoT technologies, cloud computing and machine learning. This framework processes the acquired scalp EEG signals by Fast Walsh Hadamard transform. Then, the transformed frequency-domain signals are examined using higher-order spectral analysis to extract amplitude and entropy-based statistical features. The extracted features have been selected by means of correlation-based feature selection algorithm to achieve more real-time classification with reduced complexity and delay. Finally, the samples containing selected features have been fed to ensemble machine learning techniques for classification into several classes of EEG states, viz. normal, interictal and ictal. The employed techniques include Dagging, Bagging, Stacking, MultiBoost AB and AdaBoost M1 algorithms in integration with C4.5 decision tree algorithm as the base classifier. The results of the ensemble techniques are also compared with standalone C4.5 decision tree and SVM algorithms. The performance analysis through simulation results reveals that the ensemble of AdaBoost M1 and C4.5 decision tree algorithms with higher-order spectral features is an adequate technique for automated detection of epileptic seizures in real-time. This technique achieves 100% classification accuracy, sensitivity and specificity values with optimally small classification time.

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  1. Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India

    Kuldeep Singh

  2. Department of Engineering & Technology, Guru Nanak Dev University Regional Campus, Jalandhar, Punjab, India

    Jyoteesh Malhotra

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  1. Kuldeep Singh
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Correspondence to Kuldeep Singh.

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The authors of this manuscript declare that they have no conflict of interest with any person or organization for carrying out this research work.

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This article contains studies performed on a publicly available EEG dataset, which was developed at University of Bonn, Germany. There is no direct involvement of human participants and/or animals to carry out this research work.

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This manuscript uses a publicly available EEG dataset, which was developed at the Department of Epileptology in the University of Bonn, Germany. The authors of this manuscript have cited the article corresponding to this dataset as per the recommendations of its developers. The developers of this dataset had also acknowledged the support of the Deutsche Forschungsgemeinschaft (a German Research Funding Organization). The appropriate informed consent has already taken by the developers of this dataset from the concerned organization before making it online.

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Singh, K., Malhotra, J. Cloud based ensemble machine learning approach for smart detection of epileptic seizures using higher order spectral analysis. Phys Eng Sci Med 44, 313–324 (2021). https://doi.org/10.1007/s13246-021-00970-y

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