Abstract
Diagnosis of epilepsy primarily involves understanding cautious patient history and assessment of EEG (Electro Encephalography), which is an essential diagnostic support tool. It captures the electrical activity in the brain, which enables the neurologist to look for the presence of epileptiform patterns for which brain waves (Delta, Theta, Alpha, Beta, and Gamma) are studied thoroughly. The Delta (0–4 Hz), Theta (4–8 Hz), and Alpha (8- < 13 Hz) waves are interpreted visually with proficiency; however, the interpretation of Beta (13–35 Hz) and Gamma (36-44 Hz) presents a grave challenge because of their high-frequency nature. The objective of this study was to find out if these waves incorporate features essential for the identification of inter-ictal activity. The bandpass filter was used to extract beta and gamma frequency from the complete EEG signal. Five nonlinear features were extracted out from two, and four-second segments of Beta and Gamma waves. Bagged Tree Classifier is used to categorize the segments into controlled and inter-ictal activity. Data from a total of forty-two patients were used in this study; twenty-three patients with different types of epilepsy and nineteen controlled patients. For two-second segments, we achieved 91.3% classification accuracy, and for four-second segments, we achieved 93.1%. This is improvement from the previous work available in the literature where the segment length of 23.6 s has been used by researchers; with respect to use of public data. Also, the contribution of these brain waves have not been studied independently.
Similar content being viewed by others
Abbreviations
- AC:
-
Accuracy
- ANN:
-
Artificial Neural Network
- DWT:
-
Discrete Wavelet Transform
- ELM:
-
Extreme Learning Machine
- LDA:
-
Linear Discriminant Analysis
- PCA:
-
Principal Component Analysis
- SVM:
-
Support vector Machine
- SN:
-
Sensitivity
- SP:
-
Specificity
- WPLogEn:
-
Wavelet Packet Log Energy
References
Aayesha MBQ, Afzaal M, Qureshi MS, Fayaz M (2021) Machine learning-based EEG signals classification model for epileptic seizure detection. Multimed Tools Appl 80:17849–17877. https://doi.org/10.1007/s11042-021-10597-6
Al-Angari HM, Sahakian AV (2007) Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans Biomed Eng 54(10):1900–1904. https://doi.org/10.1109/TBME.2006.889772
Amudhan S, Gururaj G, Satishchandra P (2015) Epilepsy in India I: epidemiology and public health. Ann Indian Acad Neurol 18(3):263–277 10/20 System Positioning Manual, Trans Cranial Technol., Hong Kong, 2012
Aydın S, et al. (2009) Log energy entropy-based EEG classification with multilayer neural networks in seizure
Breiman L (2001) Random forests. Mach Learn 45:5–32
Burioka N, Miyata M, Cornélissen G, Halberg F, Takeshima T, Kaplan DT, Shimizu E (2017) Approximate entropy in the electroencephalogram during wake and sleep. Clinical EEG and Neuroscience 36(1):21–24. https://doi.org/10.1177/155005940503600106
Comparison of classification models using entropy based features from sub-bands of EEG. Kaur, Arshpreet, et al. (2020). 2, s.l. : International Information and Engineering Technology Association, 4 1, 2020, Traitement du Signal 37: 279–289. https://doi.org/10.18280/ts.370214.
Divya S (2015) Classification of EEG signal for epileptic seizure detection using EMD and ELM. International Journal for Trends in Engineering and Technology 3(2):68–74
Divya S (2015) Classification of EEG signal for epileptic seizure detection using EMD and ELM. International Journal for Trends in Engineering and Technology 3(2):68–74
Gotman J (1985) Seizure recognition and analysis. In: J. Gotman, J.R. lves and P. Gloor (Eds.), Long-Term Monitoring in Epilepsy. Electroenceph.clin. Neurophysiol., Suppl. 37. Elsevier, Amsterdam: 133–145.
Hekim M (2016) The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system. Turkish J Electr Eng Comput Sci 24(1):285–297
Hekim M (2016) The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system. https://doi.org/10.3906/elk-1306-164
Jaiswal AK, Banka H (2017) Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 34:81–92
Jaiswal AK, Banka H (2017) Biomedical signal processing and control local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 34:81–92. https://doi.org/10.1016/j.bspc.2017.01.005
Jukic S, Saracevic M, Subasi A, Kevric J (2020) Comparison of ensemble machine learning methods for automated classification of focal and non-focal epileptic EEG signals. Mathematics 8(9). https://doi.org/10.3390/math8091481
Kane N, Acharya J, Benickzy S, Caboclo L, Finnigan S, Kaplan PW, et al. (2017) A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Clin Neurophysiol Pract
Kaur A, Verma K, Bhondekar AP, Shashvat K Implementation of bagged SVM ensemble model for classification of epileptic states using EEG. Current Pharmaceutical Biotechnology (Bentham Science Publishers Ltd.) 20(9) (7 2019):755–765
Kumar Y, Dewal ML, Anand RS (2012) Epileptic seizures detection in Eeg using Dwt-based apen and artificial neural network. Signal Image Video Process 8(7):1323–1334
Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing. 133:271–279
Kumar Y, Dewal ML, Anand RS Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133:271–279. https://doi.org/10.1016/j.neucom.2013.11.009
Muthukumaraswamy SD (2013) High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations. Front Hum Neurosci 7:1–11
Panych LP, Wada JA (1990) Computer applications in data analysis. In:J.A. Wada and R.J. EUingson (Eds.), Clinical Neurophysiology of Epilepsy. EEG Handbook (Rev. Ser.). Amsterdam, Elsevier:361–385.
Patidar T, Panigrahi (2017) Detection of epileptic seizure using kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomedical Signal Processing and Control 34:74–80
Pradhan, N, P K Sadasivan, and G R Arunodaya. "Detection of seizure activity in EEG by an artificial neural network: a preliminary study." 1996, 303–313
Puspita JW, Soemarno G, Jaya AI, Soewono (2018). E. Interictal Epileptiform discharges (IEDs) classification in EEG data of epilepsy patients. J Phys Conf Ser. 943.
Raghu S, Sriraam N (2017) Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures. Expert Syst Appl 89:205–221
Saiby M, Kajri S, Sharmila A, Mahalakshmi P (2018) A case study on discrete wavelet transform based Hurst exponent for a case study on discrete wavelet transform based Hurst exponent for epilepsy detection. J Med Eng Technol 0(0):1–9
Shannon CE (1948) Mathematical theory of communication. Bell Syst. Tech. J 27:379–423, 623–656
Sharma R, Pachori RB (2015) Expert systems with applications classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42:1106–1117. https://doi.org/10.1016/j.eswa.2014.08.030
Sharma M, Pachori RB (2017) A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. Journal of Mechanics in Medicine and Biology 17(7):1740003. https://doi.org/10.1142/S0219519417400036
Sharma M, Pachori RB, Rajendra Acharya U (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 94:172–179. https://doi.org/10.1016/j.patrec.2017.03.023
Sharma RR, Varshney P, Pachori RB, Vishvakarma SK (2018) Automated system for epileptic EEG detection using iterative filtering. IEEE Sensors Lett 2(4):1–4
A.Sharmila and P. Geethanjali (2016) Epileptic seizure detection from EEG signals using best feature subsets based on estimation of mutual information for support vector machines and Naïve Bayes classifiers, control and automation
Sharmila A, Suman AR, Pandey S, Mahalakshmi P (2018) Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine : a case study. J Med Eng Technol 0(0):1–8
Siska B, Astuti F, Purnami SW, Atok RM, Islamiyah WR (2021) Classify epileptic EEG signals using extreme support vector machine for ictal and muscle artifact detection. 11(2). https://doi.org/10.18178/ijmlc.2021.11.2.1031
Siuly S, Li Y (2010) Wen PP clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Prog Biomed 104:358–372. https://doi.org/10.1016/j.cmpb.2010.11.014
Siuly S, Li Y, Wen PP (2011) Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Prog Biomed 104(3):358–372
Song Y, Liò P (2017) A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J Biomed Sci Eng 3(6):556–567
Sriraam SRN (2016) Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 11:51–66. https://doi.org/10.1007/s11571-016-9408-y
Sriraam SRN (2017) Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cognitive Neurodynamics 11(1):51–66
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
Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK (Jul 2017) Automated diagnosis of epilepsy using key-point based local binary pattern of EEG signals. IEEE J. Biomed. Health Inform 21(4):888–896
Tzallas A, Tsipouras M, Fotiadis Dware design of multiclass SVM classifi (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Computational Intelligence and Neuroscience
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. Health Technol (Berl) 9:135–142. https://doi.org/10.1007/s12553-018-0265-z
Valenti P et al (2006) Automatic detection of interictal spikes using data mining models. J Neurosci Methods 150:105–110
Wang Y, Li Z, Feng L, Bai H, Wang C (2017) Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection, pp. 108–115
Webber WRS, Lesser RP, Richardson RT, Wilson K (1996) An approach to seizure detection using an artificial neural network (ANN). Electroencephalogr Clin Neurophysiol 98(4):250–272. https://doi.org/10.1016/0013-4694(95)00277-4
Xiang J, Li C, Li H, Cao R, Wang B, Han X, Chen J (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18–25
You S, Cho BH, Yook S, Kim JY, Shon YM, Seo DW, Kim IY (2020) Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network. Comput Methods Prog Biomed 193:105472. https://doi.org/10.1016/j.cmpb.2020.105472
Yu J, Wang L, Chen X (2019) Epileptic seizure classification based on the combined features,0–5
Acknowledgements
The authors thank Mr. R.S. Rawat for his constant support during the data collection process.
Author information
Authors and Affiliations
Corresponding author
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
Kaur, A., Puri, V., Verma, K. et al. Identification of inter-ictal activity in novel data by bagged prediction method using beta and gamma waves. Multimed Tools Appl 81, 19795–19811 (2022). https://doi.org/10.1007/s11042-021-11035-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11035-3