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IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification

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Abstract

Epilepsy, a fatal neurological disorder, has been emerged as a worldwide problem and is one of the major risks to human lives. There exists an urgent need for an efficient technique for early detection of epileptic seizures at its initial stage in order to save the lives of thousands of epileptic patients annually. Now a days, internet of things in combination with machine learning techniques and cloud computing services has emerged as a powerful technology to resolve many problems in healthcare sector. This paper also presents an automatic epileptic seizure detection system and its layered architecture for early detection of epileptic seizures using existing communication technologies in collaboration with machine learning and cloud computing. This model transmits sensed EEG signals from patient’s scalp to cloud through 4G cellular network or Wi-Fi network. At cloud, EEG signals are processed using Fast Walsh Hadamard transform and higher order spectra (HOS) based feature extraction for extracting higher order statistics and entropy-based features. The correlation-based feature selection algorithm has been employed for reducing the dimensionality of EEG datasets so as to tackle the problem of large volume of data and to reduce delays in service offered to the end user. Random Forest algorithm has been employed for classification of EEG signals into three different seizure stages, viz., normal, preictal and ictal. For performance analysis, other well-known machine learning algorithms like Bayes Net, Naïve Bayes, Multilayer Perceptron, Radial Basis function neural network and C4.5 Decision Tree are also considered. The simulation and testing results show that Random Forest classifier provides maximum values of classification accuracy of 99.40%, sensitivity of 99.40% and specificity of 99.66%, minimum mean square error of 0.0871 along with optimum training time of 20 ms, which makes this model more real time compatible, thereby making HOS features based Random Forest algorithm’s cloud model an efficient technique for early and automatic detection of epileptic seizures in real time.

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Acknowledgements

The authors of this paper are highly grateful to Research and Development laboratory of Guru Nanak Dev University, Amritsar, Punjab, India for providing research facilities to carry out this research work. Also, the authors would like to thank the reviewers in advance for their invaluable comments and suggestions.

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

    Kuldeep Singh

  2. Department of ECE, Guru Nanak Dev University Regional Campus, Jalandhar, India

    Jyoteesh Malhotra

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Singh, K., Malhotra, J. IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification. J Ambient Intell Human Comput 14, 15497–15512 (2023). https://doi.org/10.1007/s12652-019-01613-7

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