Abstract
The seizure is defined as the sudden synchronous activity of the number of neurons resulting in abnormal body symptoms. This paper proposes a technique for the auto-detection of epileptic seizures using an online surface EEG database. The features were extracted for every 5 seconds window from the online surface EEG signal. Authors used dynamic mode decomposition power feature calculated from the multichannel EEG signal and Power spectral density, variance, and Katz fractal dimension features evaluated from wavelet packet decomposition coefficients for seizure detection. The K-nearest neighbor (KNN) classifier was used for classification. The KNN classifier was trained separately for each signal feature. The proposed system achieved good classification accuracy in seizure detection using a simple KNN classifier. The approach is further verified using the All India Institute of Medical Sciences (AIIMS) Patna seizure database and online seizure EEG database collected from neurology and sleep center, Hauz Khas, New Delhi. Different types of seizures were considered for validation of the model. KNN classifier-based approach achieved 98.99%, 99.69%, and 96.25% classification accuracy in detecting seizures from the online surface EEG seizure database, AIIMS Patna EEG seizure database, and online seizure database collected from neurology and sleep center, Hauz Khas, New Delhi. Support vector machine classifier was further evaluated for accuracy in seizure detection from the EEG signal collected at neurology and sleep center, Hauz Khas, New Delhi available online and achieved 95.5% accuracy in preseizure-seizure EEG segment classification and 96.5% accuracy in interseizure-seizure EEG segment classification. SVM Radial Basis Function (RBF) kernel based approach achieved highest accuracy compared to linear and polynomial kernel based approach.
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Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng KH, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7(4):401–408
Ahmed R, Temko A, Marnane WP, Boylan G, Lightbody G (2017) Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel. Comput Biol Med 82:100–110
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
Bentley PM, McDonnell JTE (1994) Wavelet transforms: an introduction. Electron Commun Eng J 6(4):175–186
Bogaarts J, Gommer ED, Hilkman D M, van Kranen-Mastenbroek V, Reulen JP (2016) Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection. Med Biol Eng Comput 54(8):1285–1293
Chandel G, Upadhyaya P, Farooq O, Khan Y (2019) Detection of seizure event and its onset/offset using orthonormal triadic wavelet based features. IRBM 40(2):103–112
Chen D, Wan S, Xiang J, Bao FS (2017) A high-performance seizure detection algorithm based on discrete wavelet transform (DWT) and EEG. PloS one 12(3):e0173138
Chen S, Zhang X, Chen L, Yang Z (2019) Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access 7:61046–61056
Dash DP, Kolekar MH (2017) EEG Based epileptic seizure detection using empirical mode decomposition and Hidden Markov Model. Indian J Public Health Res Dev 8(4)
Fergus P, Hignett D, Hussain A, Al-Jumeily D, Abdel-Aziz K (2015) Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques. BioMed Res Int
Garner DM, de Souza NM, Vanderlei LCM (2018) Heart rate variability analysis: higuchi and katz fractal dimensions in subjects with type 1 diabetes mellitus. Romanian J Diabetes Nutrit Metab Dis 25(3):289–295
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) Physiobank, Physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220
Gupta AK, Chakraborty C, Gupta B (2019) Monitoring of epileptical patients using cloud-enabled health-iot system. Traitement du Signal 36(5):425–431
Gupta AK, Chakraborty C, Gupta B (2021) Secure transmission of EEG data using watermarking algorithm for the detection of epileptical seizures. Traitement du Signal 38(2)
Hassan AR, Haque MA (2015) Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain. In: IEEE Region 10 conference, pp 1–6
Humairani A, Atmojo B, Wijayanto I, Hadiyoso S (2021) Fractal based feature extraction method for epileptic seizure detection in long-term EEG recording. J Phys Conf Series 1844(1):012019
Hussein R, Palangi H, Ward R, Wang ZJ (2018) Epileptic seizure detection: A deep learning approach. arXiv:180309848
Kiranyaz S, Ince T, Zabihi M, Ince D (2014) Automated patient-specific classification of long-term electroencephalography. J Biomed Inf 49:16–31
Kolekar MH, Dash DP (2015) A nonlinear feature based epileptic seizure detection using least square support vector machine classifier. In: IEEE Region 10 Conference, pp 1–6
Kolekar MH, Sengupta S (2015) Bayesian network-based customized highlight generation for broadcast soccer videos. IEEE Trans Broadcast 61(2):195–209
Le Douget J, Fouad A, Filali MM, Pyrzowski J, Le Van Quyen M (2017) Surface and intracranial EEG spike detection based on discrete wavelet decomposition and random forest classification. IEEE Eng Med Biol Soc :475–478
Mahmoodian N, Boese A, Friebe M, Haddadnia J (2019) Epileptic seizure detection using cross-bispectrum of electroencephalogram signal. Seizure 66:4–11
Orosco L, Correa AG, Leber EL (2011) Epileptic seizures detection based on empirical mode decomposition of EEG signals. Management of Epilepsy-Research, Results and Treatment
Qureshi MB, Afzaal M, Qureshi MS, Fayaz M et al (2021) Machine learning-based EEG signals classification model for epileptic seizure detection. Multimed Tools Appl 80(12):17849–17877
Raghu S, Sriraam N, Kumar GP (2017) Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent elman neural network classifier. Cogn Neurodyn 11(1):51–66
Sharma N, Kolekar MH, Jha K, Kumar Y (2019) EEG And cognitive biomarkers based mild cognitive impairment diagnosis. IRBM 40(2):113–121
Shoeb AH (2009) Application of machine learning to epileptic seizure onset detection and treatment. Doctoral dissertation, Massachusetts Institute of Technology
Shoeb AH, Guttag JV (2010) Application of machine learning to epileptic seizure detection. In: International conference on machine learning, pp 975–982
Simois FJ, Murillo-Fuentes JJ (2017) On the power spectral density applied to the analysis of old canvases. arXiv:1705.10060
Solaija MSJ, Saleem S, Khurshid K, Hassan SA, Kamboh AM (2018) Dynamic mode decomposition based epileptic seizure detection from scalp EEG. IEEE Access 6:38683–38692
Swami P, Gandhi TK, Panigrahi BK, Tripathi M, Anand S (2016) A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst Appl 56:116–130
Temko A, Thomas E, Marnane W, Lightbody G, Boylan G (2011) EEG-Based neonatal seizure detection with support vector machines. Clin Neurophysiol 122(3):464–473
Tian X, Deng Z, Ying W, Choi KS, Wu D, Qin B, Wang J, Shen H, Wang S (2019) Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Trans Neural Sys Rehab Eng 27(10):1962–1972
Tu JH, Rowley CW, Luchtenburg DM, Brunton SL, Kutz JN (2013) On dynamic mode decomposition: theory and applications. arXiv:13120041
Yuan Q, Zhou W, Zhang L, Zhang F, Xu F, Leng Y, Wei D, Chen M (2017) Epileptic seizure detection based on imbalanced classification and wavelet packet transform. Seizure 50:99–108
Yuvaraj R, Thomas J, Kluge T, Dauwels J (2018) A deep learning scheme for automatic seizure detection from long-term scalp EEG. In: Asilomar conference on signals, systems, and computers, pp 368–372
Zahra A, Kanwal N, ur Rehman N, Ehsan S, McDonald-Maier KD (2017) Seizure detection from EEG signals using multivariate empirical mode decomposition. Comput Biol Med 88:132–141
Zhang Y (2012) Support vector machine classification algorithm and its application. In: International conference on information computing and applications, pp 179–186
Zhou M, Tian C, Cao R, Wang B, Niu Y, Hu T, Guo H, Xiang J (2018) Epileptic seizure detection based on EEG signals and cnn. Front Neuroinf 12:95
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Dash, D.P., Kolekar, M.H. & Jha, K. Surface EEG based epileptic seizure detection using wavelet based features and dynamic mode decomposition power along with KNN classifier. Multimed Tools Appl 81, 42057–42077 (2022). https://doi.org/10.1007/s11042-021-11487-7
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DOI: https://doi.org/10.1007/s11042-021-11487-7