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A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique

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Abstract

Early detection and proper treatment of epilepsy seizure is essential and meaningful to those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing, and obsessive hand movements. Neurological exams, blood testing, cognitive tests, and neuroimaging modalities are some of the techniques used to detect epileptic seizure activity in patients. Among them, neuroimaging techniques have attracted a lot of interest from doctors in the field. The use of computer-aided diagnostic systems (CADS) based on deep learning (DL) and neuroimaging modalities can help quickly and accurately diagnose epileptic seizures. This research introduces a novel seizure detection approach based on a bidirectional long short-term memory (Bi-LSTM) network. As a result, Bi-LSTM preserves the nonstationary nature of electroencephalogram (EEG) data while decreasing processing costs by employing local mean decomposition (LMD) and statistical feature extraction algorithms. Two distinct LSTM networks with opposing propagation directions are combined in the deep architecture. So, the deep model can use information from both before and after the current time of analysis to decide the output state. On a long-term scalp EEG database, an average accuracy of 97%, sensitivity of 95.70%, specificity of 93.90%, and G-Mean of 94.80% were achieved, with an anticipation time of 10 min.

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Data availability

The datasets analyzed during the current study are not publicly available due to individual privacy but are available from the corresponding author on reasonable request.

Notes

  1. https://physionet.org/content/chbmit/1.0.0/

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education, Hyderabad, India

    Puranam Revanth Kumar, B. Shilpa & Rajesh Kumar Jha

  2. School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravathi, Andhra Pradesh, India

    Sachi Nandan Mohanty

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  1. Puranam Revanth Kumar
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  2. B. Shilpa
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  3. Rajesh Kumar Jha
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  4. Sachi Nandan Mohanty
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Correspondence to Puranam Revanth Kumar.

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Kumar, P.R., Shilpa, B., Jha, R.K. et al. A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique. Int. j. inf. tecnol. 15, 4223–4231 (2023). https://doi.org/10.1007/s41870-023-01428-y

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