Research Article
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Year 2021, Volume: 42 Issue: 2, 508 - 514, 30.06.2021

Muzaffer Aslan , Zeynep Alçin

https://doi.org/10.17776/csj.682734
Cited By: 5

Abstract

References

  • [1] Andrzejak R. G., Lehnertz K., Mormann F., Rieke C., David, P., Elger C. E., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Physical Review E., 64(6) (2001) 061907
  • [2] Çakil D., Inanir S., Baykan H., Aygün, H., Kozan R., Epilepsi ayırıcı tanısında psikojenik non-epileptik nöbetler, Göztepe Tıp Dergisi., 28(1) (2013) 41–47.
  • [3] World Health Organization, Epilepsy: a public health imperative. Available at: https://www.who.int/publications/i/item/epilepsy-a-public-health-imperative. Retrieved February, 2021.
  • [4] Bajaj V., Guo Y., Sengur A., Siuly S., Alcin O. F., A hybrid method based on time–frequency images for classification of alcohol and control EEG signals, Neural Computing and Applications., 28(12) (2017) 3717–3723.
  • [5] A. Jaiswal A. K., Banka H., Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals, Biomedical Signal Processing and Control., 34 (2017) 81–92.
  • [6] V. Joshi V., Pachori R. B., Vijesh A., Classification of ictal and seizure-free EEG signals using fractional linear prediction, Biomedical Signal Processing and Control., 9(1) (2014) 1–5.
  • [7] S. Siuly S., Alcin O. F., Bajaj V., Sengur A., Zhang Y., Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure, IET Science Measurement and Technology., 13(1) (2019) 35–41.
  • [8] Huang N. E., Shen Z., Long S. R., Wu M. C., Snin H. H., Zheng Q., Yen N. C., Tung C. C., Liu H. H., The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences., 454(1971) (1998) 903–995.
  • [9] Mutlu A. Y., Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition, Biomedical Signal Processing and Control., 40 (2018) 33–40.
  • [10] Fu K., Qu J., Chai Y., Dong Y., Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM, Biomedical Signal Processing and Control., 13(1) (2014) 15–22.
  • [11] Martis R. J., Acharya U. R., Tan J. H., Petznick A., Yanti R., Chua C. K., Ng E. Y. K., Tong L., Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals, International Journal of Neural Systems., 22(6) (2012) 1250027 1-16.
  • [12] Shuren Q, Zhong J, Extraction of features in EEG signals with the non-stationary signal analysis technology, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society., San Francisco, (2004) 349–352.
  • [13] Feldman, M., Time-varying vibration decomposition and analysis based on the Hilbert transform, Journal of Sound and Vibration., 295(3–5) (2006) 518–530.
  • [14] Al Ghayab H. R., Li Y., Abdulla S., Diykh M., Wan X., Classification of epileptic EEG signals based on simple random sampling and sequential feature selection, Brain Informatics., 3(2) (2016) 85–91.
  • [15] Andrzejak R. G., Lehnertz K., Mormann F., Rieke C., David, P., Elger C. E., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity, Dependence on recording region and brain state, Physical Review E., 64(6) (2001) 061907 1-8.
  • [16] Supriya S., Siuly S., Wang H., Cao J., Zhang Y., Weighted Visibility Graph with Complex Network Features in the Detection of Epilepsy, IEEE Access., 4 (2016) 6554–6566.
  • [17] Kumar Y., Dewal M. L., Anand R. S., Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine, Neurocomputing., 133 (2014) 271–279.
  • [18] Demir F., Sobahi N., Siuly S., Sengur A., Exploring Deep Learning Features For Automatic Classification Of Human Emotion Using EEG Rhythms, IEEE Sensors Journal., (2021) 1–8.
  • [19] Turkoglu M., Alcin O. F., Aslan M., Al-Zebari, A., Sengur A., Deep rhythm and long short term memory-based drowsiness detection, Biomedical Signal Processing and Control., 65 (2020) 102364.
  • [20] Andrzejak R. G., Lehnertz K., Mormann F., Rieke C., David, P., Elger C. E., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity, Dependence on recording region and brain state, Physical Review E., 64(6) (2001) 061907 1-8
  • [21] Oweis R. J., Abdulhay E. W., Seizure classification in EEG signals utilizing Hilbert-Huang transform, BioMedical Engineering Online., 10(1) (2011) 1-15 . [22] Wei B., Xie B., Li H., Zhong Z., You Y., An improved Hilbert–Huang transform method for modal parameter identification of a high arch dam, Applied Mathematical Modelling., 91 (2021) 297–310. [23] Aydın F., Aslan Z., Recognizing Parkinson’s disease gait patterns by vibes algorithm and Hilbert-Huang transform, Engineering Science and Technology, an International Journal., 24(1) (2021) 112–125.

Detection of epileptic seizures from EEG signals with Hilbert Huang Transformation

Year 2021, Volume: 42 Issue: 2, 508 - 514, 30.06.2021

Muzaffer Aslan , Zeynep Alçin

https://doi.org/10.17776/csj.682734
Cited By: 5

Abstract

Epilepsy is a significant neurological disease that occurs due to abnormal activities of a particular portion of brain neurons. Electroencephalography (EEG) signals are mainly used to detect this disease. Epilepsy can be diagnosed automatically by measuring and analyzing the non-linearity and non-stationary properties of EEG signals. In this study, the Hilbert Huang Transformation (HHT) is proposed to extract the distinctive features from EEG signals for epileptic seizure detection. Research work, firstly, the mean Instantaneous Amplitude (IA) and mean Instantaneous Frequency (IF) data were extracted from EEG signals with Hilbert Huang Transformation (HHT) as a feature. Then, these features were classified with Extreme Learning Machine (ELM). Classification results indicated that epileptic seizures are detected with high accuracy. In addition, the performance evaluation of the proposed method was compared with some other techniques studied by using the same dataset recently. According to the experimental results, HHT based approach has 0.5-1% better classification accuracy than current studies and higher accuracy in detecting epileptic seizures than similar studies.

Keywords

Electroencephalogram (EEG) signal Hilbert–Huang Transform (HHT) Feature Extraction Classification

References

  • [1] Andrzejak R. G., Lehnertz K., Mormann F., Rieke C., David, P., Elger C. E., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Physical Review E., 64(6) (2001) 061907
  • [2] Çakil D., Inanir S., Baykan H., Aygün, H., Kozan R., Epilepsi ayırıcı tanısında psikojenik non-epileptik nöbetler, Göztepe Tıp Dergisi., 28(1) (2013) 41–47.
  • [3] World Health Organization, Epilepsy: a public health imperative. Available at: https://www.who.int/publications/i/item/epilepsy-a-public-health-imperative. Retrieved February, 2021.
  • [4] Bajaj V., Guo Y., Sengur A., Siuly S., Alcin O. F., A hybrid method based on time–frequency images for classification of alcohol and control EEG signals, Neural Computing and Applications., 28(12) (2017) 3717–3723.
  • [5] A. Jaiswal A. K., Banka H., Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals, Biomedical Signal Processing and Control., 34 (2017) 81–92.
  • [6] V. Joshi V., Pachori R. B., Vijesh A., Classification of ictal and seizure-free EEG signals using fractional linear prediction, Biomedical Signal Processing and Control., 9(1) (2014) 1–5.
  • [7] S. Siuly S., Alcin O. F., Bajaj V., Sengur A., Zhang Y., Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure, IET Science Measurement and Technology., 13(1) (2019) 35–41.
  • [8] Huang N. E., Shen Z., Long S. R., Wu M. C., Snin H. H., Zheng Q., Yen N. C., Tung C. C., Liu H. H., The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences., 454(1971) (1998) 903–995.
  • [9] Mutlu A. Y., Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition, Biomedical Signal Processing and Control., 40 (2018) 33–40.
  • [10] Fu K., Qu J., Chai Y., Dong Y., Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM, Biomedical Signal Processing and Control., 13(1) (2014) 15–22.
  • [11] Martis R. J., Acharya U. R., Tan J. H., Petznick A., Yanti R., Chua C. K., Ng E. Y. K., Tong L., Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals, International Journal of Neural Systems., 22(6) (2012) 1250027 1-16.
  • [12] Shuren Q, Zhong J, Extraction of features in EEG signals with the non-stationary signal analysis technology, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society., San Francisco, (2004) 349–352.
  • [13] Feldman, M., Time-varying vibration decomposition and analysis based on the Hilbert transform, Journal of Sound and Vibration., 295(3–5) (2006) 518–530.
  • [14] Al Ghayab H. R., Li Y., Abdulla S., Diykh M., Wan X., Classification of epileptic EEG signals based on simple random sampling and sequential feature selection, Brain Informatics., 3(2) (2016) 85–91.
  • [15] Andrzejak R. G., Lehnertz K., Mormann F., Rieke C., David, P., Elger C. E., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity, Dependence on recording region and brain state, Physical Review E., 64(6) (2001) 061907 1-8.
  • [16] Supriya S., Siuly S., Wang H., Cao J., Zhang Y., Weighted Visibility Graph with Complex Network Features in the Detection of Epilepsy, IEEE Access., 4 (2016) 6554–6566.
  • [17] Kumar Y., Dewal M. L., Anand R. S., Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine, Neurocomputing., 133 (2014) 271–279.
  • [18] Demir F., Sobahi N., Siuly S., Sengur A., Exploring Deep Learning Features For Automatic Classification Of Human Emotion Using EEG Rhythms, IEEE Sensors Journal., (2021) 1–8.
  • [19] Turkoglu M., Alcin O. F., Aslan M., Al-Zebari, A., Sengur A., Deep rhythm and long short term memory-based drowsiness detection, Biomedical Signal Processing and Control., 65 (2020) 102364.
  • [20] Andrzejak R. G., Lehnertz K., Mormann F., Rieke C., David, P., Elger C. E., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity, Dependence on recording region and brain state, Physical Review E., 64(6) (2001) 061907 1-8
  • [21] Oweis R. J., Abdulhay E. W., Seizure classification in EEG signals utilizing Hilbert-Huang transform, BioMedical Engineering Online., 10(1) (2011) 1-15 . [22] Wei B., Xie B., Li H., Zhong Z., You Y., An improved Hilbert–Huang transform method for modal parameter identification of a high arch dam, Applied Mathematical Modelling., 91 (2021) 297–310. [23] Aydın F., Aslan Z., Recognizing Parkinson’s disease gait patterns by vibes algorithm and Hilbert-Huang transform, Engineering Science and Technology, an International Journal., 24(1) (2021) 112–125.
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Engineering Sciences
Authors

Muzaffer Aslan 0000-0002-2418-9472

Zeynep Alçin 0000-0002-7034-3119

Publication Date June 30, 2021
Submission Date February 5, 2020
Acceptance Date June 17, 2021
Published in Issue Year 2021Volume: 42 Issue: 2

Cite

APA Aslan, M., & Alçin, Z. (2021). Detection of epileptic seizures from EEG signals with Hilbert Huang Transformation. Cumhuriyet Science Journal, 42(2), 508-514. https://doi.org/10.17776/csj.682734

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A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection
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https://doi.org/10.3389/fninf.2022.1040084
A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders
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Extreme Learning Machine (ELM) based Performance Analysis and Epilepsy Identification from EEG Signals
IETE Journal of Research
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