Abstract
This paper proposes a novel lightweight shifting sample difference method for efficient epileptic seizure detection from electroencephalogram signals. Unlike most recent seizure detection methods that use complex signal transformations, the shifting sample difference method is based on time-domain and does not require any transformation. The epilepsy detection performances using five popular electroencephalogram features (statistical measures, Hjorth parameters, fractal dimensions, approximate entropy, and sample entropy) are investigated in this study. The proposed shifting sample difference method outperforms widely used discrete wavelet transform and empirical mode decomposition based features in three classification problems from the Bonn university epilepsy dataset. Accuracies of 99%, 98%, and 100% are obtained for normal vs. inter-ictal, inter-ictal vs. ictal, and normal vs. ictal classifications. The highest accuracy and reduced computational complexity show the potential scope of proposed shifting sample differences in epilepsy diagnosis.
Similar content being viewed by others
Data availability
The datasets analysed during the current study are available in the Epileptologie Bonn repository, [http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/eegdata.html].
References
Alickovic E, Kevric J, Subasi A (2018) Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Signal Process Control 39:94–102
Alturki FA, AlSharabi K, Abdurraqeeb AM, Aljalal M (2020) Eeg signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors 20(9):2505
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E 64(6):061907
Bajaj V, Rai K, Kumar A, Sharma D (2017) Time-frequency image based features for classification of epileptic seizures from eeg signals. Biomed Phys Eng Express 3(1):015012
Beghi E (2020) The epidemiology of epilepsy. Neuroepidemiology 54(2):185–191
Bhattacharyya A, Pachori RB (2017) A multivariate approach for patient-specific eeg seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng 64(9):2003–2015
Chakrabarti S, Swetapadma A, Ranjan A, Pattnaik PK (2020) Time domain implementation of pediatric epileptic seizure detection system for enhancing the performance of detection and easy monitoring of pediatric patients. Biomed Signal Process Control 59:101930
Dash DP, Kolekar MH (2020) Hidden markov model based epileptic seizure detection using tunable q wavelet transform. J Biomed Res 34(3):170
Deivasigamani S, Senthilpari C, Yong WH (2021) Machine learning method based detection and diagnosis for epilepsy in eeg signal. J Ambient Intell Hum Comput 12(3):4215–4221
El-Gindy SAE, Hamad A, El-Shafai W, Khalaf AA, El-Dolil SM, Taha TE, El-Fishawy AS, Alotaiby TN, Alshebeili SA, Abd El-Samie FE (2021) Efficient communication and eeg signal classification in wavelet domain for epilepsy patients. J Ambient Intell Hum Comput pp 1–16
Fasil O, Rajesh R (2019) Time-domain exponential energy for epileptic eeg signal classification. Neurosci Lett 694:1–8
Fasil O, Rajesh R (2020) Empirical mode decomposition of eeg signals for the effectual classification of seizures. In: Advances in Neural Signal Processing, IntechOpen, p 65
Fasil O, Rajesh R, Thasleema T (2017) Influence of differential features in focal and non-focal eeg signal classification. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), IEEE, pp 646–649
George ST, Subathra M, Sairamya N, Susmitha L, Premkumar MJ (2020) Classification of epileptic eeg signals using pso based artificial neural network and tunable-q wavelet transform. Biocybern Biomed Eng 40(2):709–728
Gong C, Zhang X, Niu Y (2020) Identification of epilepsy from intracranial eeg signals by using different neural network models. Comput Biol Chem 87:107310
Gurumoorthy S, Muppalaneni NB, Kumari GS (2020) Eeg signal denoising using haar transform and maximal overlap discrete wavelet transform (modwt) for the finding of epilepsy. In: Epilepsy, IntechOpen
Hassan (2016) Automatic identification of epileptic seizures from eeg signals using linear programming boosting. computer methods and programs in biomedicine 136:65–77
Hassan AR, Subasi A, Zhang Y (2020) Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowl-Based Syst 191:105333
Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Physica D 31(2):277–283
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A: Math Phys Eng Sci 454(1971):903–995
Jiang X, Ma ZJ, Ren WX (2012) Crack detection from the slope of the mode shape using complex continuous wavelet transform. Comput-Aid Civ Infrastruct Eng 27(3):187–201
Jiang X, Xu K, Zhang R, Ren H, Chen W (2019) Redundancy removed dual-tree discrete wavelet transform to construct compact representations for automated seizure detection. Appl Sci 9(23):5215
Kang JH, Chung YG, Kim SP (2015) An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms. Comput Biol Med 66:352–356
Katz MJ (1988) Fractals and the analysis of waveforms. Comput Biol Med 18(3):145–156
Khan KA, Shanir P, Khan YU, Farooq O (2020) A hybrid local binary pattern and wavelets based approach for eeg classification for diagnosing epilepsy. Expert Syst Appl 140:112895
Kumar Y, Dewal M, Anand R (2014) Epileptic seizure detection using dwt based fuzzy approximate entropy and support vector machine. Neurocomputing 133:271–279
Kumar Y, Dewal M, Anand R (2014) Epileptic seizures detection in eeg using dwt-based apen and artificial neural network. SIViP 8(7):1323–1334
Lahmiri S (2014) Wavelet low-and high-frequency components as features for predicting stock prices with backpropagation neural networks. JKing Saud Univ-Comput Inf Sci 26(2):218–227
Li C, Zhou W, Liu G, Zhang Y, Geng M, Liu Z, Wang S, Shang W (2021) Seizure onset detection using empirical mode decomposition and common spatial pattern. IEEE Trans Neural Syst Rehabil Eng 29:458–467
Li M, Chen W, Zhang T (2017) Classification of epilepsy eeg signals using dwt-based envelope analysis and neural network ensemble. Biomed Signal Process Control 31:357–365
Li Y, Cui W, Luo M, Li K, Wang L (2018) Epileptic seizure detection based on time-frequency images of eeg signals using gaussian mixture model and gray level co-occurrence matrix features. Int J Neural Syst 28(07):1850003
Mahapatra HK, Arindam G (2018) Classification of ictal and interictal eeg using rms frequency, dominant frequency, root mean instantaneous frequency square and their parameters ratio. Biomed Signal Process Control 44:168–180
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693
Martis RJ, Acharya UR, Tan JH, Petznick A, Yanti R, Chua CK, Ng EK, Tong L (2012) Application of empirical mode decomposition (emd) for automated detection of epilepsy using eeg signals. Int J Neural Syst 22(06):1250027
Matin A, Bhuiyan RA, Shafi SR, Kundu AK, Islam MU (2019) A hybrid scheme using pca and ica based statistical feature for epileptic seizure recognition from eeg signal. In: 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision and Pattern Recognition (icIVPR), IEEE, pp 301–306
Moalong KMC, Espiritu AI, Fernandez MLL, Jamora RDG (2021) Treatment gaps and challenges in epilepsy care in the philippines. Epilepsy Behav 115:107491
Moctezuma LA, Molinas M (2020) Classification of low-density eeg for epileptic seizures by energy and fractal features based on emd. J Biomed Res 34(3):180
Nayak J, Kaur J, Tayal A (2021) A hybrid mathematical model using dwt and svm for epileptic seizure classification. In: International conference on artificial intelligence and sustainable computing, Springer, pp 203–218
Oh SH, Lee YR, Kim HN (2014) A novel eeg feature extraction method using hjorth parameter. Int J Electron Electr Eng 2(2):106–110
Omidvar M, Zahedi A, Bakhshi H (2021) Eeg signal processing for epilepsy seizure detection using 5-level db4 discrete wavelet transform, ga-based feature selection and ann/svm classifiers. J Ambient Intell Hum Comput pp 1–9
Pachori RB, Bajaj V (2011) Analysis of normal and epileptic seizure eeg signals using empirical mode decomposition. Comput Methods Programs Biomed 104(3):373–381
Petrosian A (1995) Kolmogorov complexity of finite sequences and recognition of different preictal eeg patterns. In: Proceedings Eighth IEEE symposium on computer-based medical systems, IEEE, pp 212–217
Rafik D, Larbi B (2019) Autoregressive modeling based empirical mode decomposition (emd) for epileptic seizures detection using eeg signals. Traitement du Signal 36:273–279
Raghavendra B, Dutt DN (2010) Computing fractal dimension of signals using multiresolution box-counting method. Int J Inf Math Sci 6(1):50–65
Raghu S, Sriraam N, Temel Y, Rao SV, Hegde AS, Kubben PL (2019) Performance evaluation of dwt based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using svm classifier. Comput Biol Med 110:127–143
Raghu S, Sriraam N, Temel Y, Rao SV, Hegde AS, Kubben PL (2020) Complexity analysis and dynamic characteristics of eeg using modwt based entropies for identification of seizure onset. J Biomed Res 34(3):213
Redelico FO, Traversaro F, García MC, Silva W, Rosso OA, Risk M (2017) Classification of normal and pre-ictal eeg signals using permutation entropies and a generalized linear model as a classifier. Entropy 19(2):72
Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K (2015) Emd-based temporal and spectral features for the classification of eeg signals using supervised learning. IEEE Trans Neural Syst Rehabil Eng 24(1):28–35
Rodríguez-Sotelo JL, Osorio-Forero A, Jiménez-Rodríguez A, Cuesta-Frau D, Cirugeda-Roldán E, Peluffo D (2014) Automatic sleep stages classification using eeg entropy features and unsupervised pattern analysis techniques. Entropy 16(12):6573–6589
Samiee K, Kovacs P, Gabbouj M (2014) Epileptic seizure classification of eeg time-series using rational discrete short-time fourier transform. IEEE Trans Biomed Eng 62(2):541–552
Sharma M, Pachori RB, Acharya UR (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 94:172–179
Sharma R, Pachori RB, Acharya UR (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2):669–691
Sharmila A, Aman Raj S, Shashank P, Mahalakshmi P (2018) Epileptic seizure detection using dwt-based approximate entropy, shannon entropy and support vector machine: a case study. J Med Eng Technol 42(1):1–8
Singh N, Dehuri S (2020) Multiclass classification of eeg signal for epilepsy detection using dwt based svd and fuzzy knn classifier. Intelligent Decision Technologies (Preprint):1–14
Sinha N, Babu D et al (2015) Statistical features based epileptic seizure eeg detection-an efficacy evaluation. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp 1394–1398
Siuly S, Alcin OF, Bajaj V, Sengur A, Zhang Y (2019) Exploring hermite transformation in brain signal analysis for the detection of epileptic seizure. IET Sci Measur Technol 13(1):35–41
Srinath R, Gayathri R (2021) Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods. Int J Imaging Syst Technol 31(2):729–740
Sweeney-Reed CM, Nasuto SJ, Vieira MF, Andrade AO (2018) Empirical mode decomposition and its extensions applied to eeg analysis: a review. Adv Data Sci Adapt Anal 10(02):1840001
Tanveer M, Pachori RB, Angami N (2018) Classification of seizure and seizure-free eeg signals using hjorth parameters. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp 2180–2185
Tao Z, Wan-Zhong C, Ming-Yang L (2016) Automatic seizure detection of electroencephalogram signals based on frequency slice wavelet transform and support vector machine. Acta Physica Sinica 65(3):1550040
Tzimourta KD, Tzallas AT, Giannakeas N, Astrakas LG, Tsalikakis DG, Angelidis P, Tsipouras MG (2019) A robust methodology for classification of epileptic seizures in eeg signals. Heal Technol 9(2):135–142
Wang Y, Zhou W, Yuan Q, Li X, Meng Q, Zhao X, Wang J (2013) Comparison of ictal and interictal eeg signals using fractal features. Int J Neural Syst 23(06):1350028
Wijayanto I, Hartanto R, Nugroho HA, Winduratna B (2019) Seizure type detection in epileptic eeg signal using empirical mode decomposition and support vector machine. In: 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA), IEEE, pp 314–319
Wijayanto I, Hartanto R, Nugroho HA (2020) Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal. Informatics in Medicine Unlocked p 100325
Wu J, Zhou T, Li T (2020) Detecting epileptic seizures in eeg signals with complementary ensemble empirical mode decomposition and extreme gradient boosting. Entropy 22(2):140
You Y, Chen W, Li M, Zhang T, Jiang Y, Zheng X (2020) Automatic focal and non-focal eeg detection using entropy-based features from flexible analytic wavelet transform. Biomed Signal Process Control 57:101761
Zarei A, Asl BM (2021) Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of eeg signals. Comput Biol Med 131:104250
Zhu G, Li Y, Wen PP (2014) Epileptic seizure detection in eegs signals using a fast weighted horizontal visibility algorithm. Comput Methods Programs Biomed 115(2):64–75
Acknowledgements
The authors would like to thank Central University of Kerala for providing research support for this work.
Author information
Authors and Affiliations
Contributions
OKF: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing - Original Draft, Visualization. RR: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing - Review & Editing, Visualization, Supervision, Project administration
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Fasil, O.K., Rajesh, R. Epileptic seizure classification using shifting sample difference of EEG signals. J Ambient Intell Human Comput 14, 11809–11822 (2023). https://doi.org/10.1007/s12652-022-03737-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-022-03737-9