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Automatic seizure detection using a highly adaptive directional time–frequency distribution

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Abstract

Electroencephalogram (EEG) signals can be used by a proficient neurologist to detect the presence of seizure activity inside the brain. Automated detection of seizures in EEG signals has clinical importance given that manual round-the-clock monitoring of EEG signals is impossible. A patient-independent algorithm for seizure detection is developed using features extracted from high-resolution time–frequency distributions (TFDs). In order to achieve good classification performance, a modified highly adaptive time–frequency distribution (HADTFD) is defined. The modified-HADTFD is used to obtain a clear and cross-term free time–frequency representation of EEG signals. This is followed by the extraction of features and training of a linear classifier. The proposed approach based on modified-HADTFD achieves the classification accuracy of \(98.56\%\) by using only three time–frequency features, which is \(37\%\) more than the accuracy achieved with other TFDs.

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Authors and Affiliations

  1. Laboratory of Advanced Industrial Signal Processing and AI, Shahrood University of Technology, Shahrood, Iran

    Mokhtar Mohammadi & Ali Akbar Pouyan

  2. Department of Computer Science, University of Human Development, Slemani, Iraq

    Mokhtar Mohammadi

  3. Electrical Engineering, Foundation University, Islamabad, Pakistan

    Nabeel Ali Khan

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  1. Mokhtar Mohammadi
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  2. Nabeel Ali Khan
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  3. Ali Akbar Pouyan
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Correspondence to Mokhtar Mohammadi.

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Mohammadi, M., Ali Khan, N. & Pouyan, A.A. Automatic seizure detection using a highly adaptive directional time–frequency distribution. Multidim Syst Sign Process 29, 1661–1678 (2018). https://doi.org/10.1007/s11045-017-0522-8

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