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Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG

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Abstract

Epilepsy is a commonly observed long-term neurological disorder that impairs nerve cell activity in the brain and has a severe impact on people’s daily lives. Accurate seizure detection in the long-term electroencephalogram (EEG) signals has gained vital importance in the diagnosis of patients with epilepsy. Visual interpretation and detection of epileptic seizures in long-term EEG is a time-consuming and burdensome task for neurologists. Therefore, in this study, we propose a computationally efficient automated seizure detection model using a novel feature called successive decomposition index (SDI). We observed that the SDI feature was significantly higher during the epileptic seizure as compared to normal EEG. The performance of the proposed method was evaluated using three databases, namely the Ramaiah Medical College and Hospital (DB1), CHB-MIT (DB2) and the Temple University Hospital (DB3) consisting of 58 h, 884 h, and 408 h of EEG, respectively. Experimental results revealed the sensitivity–false detection rate–median detection delay of 97.53%–0.4/h–1.5 s, 97.28%–0.57/h–1.7 s, and 95.80%–0.49/h–1.5 s for DB1, DB2, and DB3, respectively, using the support vector machine classifier. The proposed method significantly outperformed previously presented methods (wavelets and other feature extraction methods) while being computationally more efficient. Further, to the best of the author’s knowledge, present study is the first study that was tested on three different EEG databases and showed consistent results leading to the generalization and robustness of the algorithm. Hence, the proposed method is an efficient tool for neurologists to detect epileptic seizures in long-term EEG.

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Notes

  1. http://www.physionet.org/pn6/chbmit.

  2. https://www.isip.piconepress.com/projects/tuh_eeg/index.shtml.

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Acknowledgements

The authors are grateful to doctors of the Institute of Neuroscience, Ramaiah Medical College and Hospitals, Bengaluru, India, for valuable discussion and providing EEG recordings for research purpose. Authors also grateful to Dr. Ali Shoeb from CHB-MIT and team of the TUH for permitting to use their database for research. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Neurosurgery, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands

    S. Raghu

  2. Center for Medical Electronics and Computing, M S Ramaiah Institute of Technology, Bengaluru, India

    S. Raghu & Natarajan Sriraam

  3. Maastricht University, Maastricht, The Netherlands

    Shyam Vasudeva Rao

  4. Institute of Neuroscience, Ramaiah Medical College and Hospitals, Bengaluru, India

    Alangar Sathyaranjan Hegde

  5. Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands

    Pieter L. Kubben

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  1. S. Raghu
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Correspondence to Natarajan Sriraam.

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The proper consent was taken from the ethics committee to use the RMCH database for research purpose. The proposed study makes use of two open source databases (DB2 and DB2), where appropriate ethical clearance has been already taken before database made available to public.

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Appendices

Appendix 1

Figure 9a illustrates the EEG from DB2 (subject no:chb01) and Fig. 9b shows its image representation derived from SDI feature. An epileptic seizure activity begins at 327 s and ends at 420 s; Fig. 9b shows a significant increase of SDI during seizure activity. Similarly, Fig. 10a shows the epileptic EEG from DB3 (subject no.:1217, session:s03, and file:a02) and Fig. 10b presents an image representation of the corresponding SDI feature.

Fig. 9
figure 9

a Epileptic EEG example collected from DB2 which consist of 23 bipolar channels and display shows the EEG between the time duration of 300–500 s. b Image derived from SDI for the EEG as shown in Fig. 9a. The x-axis and y-axis represent time and channel, respectively. The colormap represents the SDI values (color figure online)

Full size image
Fig. 10
figure 10

a Epileptic EEG example collected from DB3 which consist of 19 channels and EEG displayed between the time duration of 170–255 s. b Image derived from SDI for the EEG as shown in a. The x-axis and y-axis represent time and channel respectively. The colormap represents the SDI values (color figure online)

Full size image

Appendix B

Table 8 shows the functions of MATLAB GUI components.

Table 8 Functions of MATLAB GUI components
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Raghu, S., Sriraam, N., Vasudeva Rao, S. et al. Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG. Neural Comput & Applic 32, 8965–8984 (2020). https://doi.org/10.1007/s00521-019-04389-1

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