Identification of inter-ictal activity in novel data by bagged prediction method using beta and gamma waves

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.

[1]  Haider Banka,et al.  Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals , 2017, Biomed. Signal Process. Control..

[2]  Pablo Valenti,et al.  Automatic detection of interictal spikes using data mining models , 2006, Journal of Neuroscience Methods.

[3]  Eiji Shimizu,et al.  Approximate Entropy in the Electroencephalogram during Wake and Sleep , 2005, Clinical EEG and neuroscience.

[4]  Yan Li,et al.  Clustering technique-based least square support vector machine for EEG signal classification , 2011, Comput. Methods Programs Biomed..

[5]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[6]  Alan V. Sahakian,et al.  Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome , 2007, IEEE Transactions on Biomedical Engineering.

[7]  P. Satishchandra,et al.  Epilepsy in India I: Epidemiology and public health , 2015, Annals of Indian Academy of Neurology.

[8]  Rajeev Sharma,et al.  Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions , 2015, Expert Syst. Appl..

[9]  U. Rajendra Acharya,et al.  A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension , 2017, Pattern Recognit. Lett..

[10]  Ram Bilas Pachori,et al.  A NOVEL APPROACH TO DETECT EPILEPTIC SEIZURES USING A COMBINATION OF TUNABLE-Q WAVELET TRANSFORM AND FRACTAL DIMENSION , 2017 .

[11]  P. Geethanjali,et al.  Epileptic Seizure Detection from EEG Signals Using Best Feature Subsets Based on Estimation of Mutual Information for Support Vector Machines and Naïve Bayes Classifiers , 2018 .

[12]  Bijaya K. Panigrahi,et al.  Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals , 2017, IEEE Journal of Biomedical and Health Informatics.

[13]  S. Muthukumaraswamy.  High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations , 2013, Front. Hum. Neurosci..

[14]  Natarajan Sriraam,et al.  Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures , 2017, Expert Syst. Appl..

[15]  Bijaya K. Panigrahi,et al.  A novel robust diagnostic model to detect seizures in electroencephalography , 2016, Expert Syst. Appl..

[16]  Alexandros T. Tzallas,et al.  A robust methodology for classification of epileptic seizures in EEG signals , 2018, Health and Technology.

[17]  N Sriraam,et al.  Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier , 2016, Cognitive Neurodynamics.

[18]  Baek Hwan Cho,et al.  Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network , 2020, Comput. Methods Programs Biomed..

[19]  Mahmut Hekim,et al.  The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system , 2016 .

[20]  S. Kara,et al.  Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure , 2009, Annals of Biomedical Engineering.

[21]  A Sharmila,et al.  Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study , 2018, Journal of medical engineering & technology.

[22]  Junjie Chen,et al.  The detection of epileptic seizure signals based on fuzzy entropy , 2015, Journal of Neuroscience Methods.

[23]  Pietro Liò,et al.  A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine , 2010 .

[24]  Shivnarayan Patidar,et al.  Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals , 2017, Biomed. Signal Process. Control..

[25]  Saiby Madan,et al.  A case study on Discrete Wavelet Transform based Hurst exponent for epilepsy detection , 2018, Journal of medical engineering & technology.

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  Muhammad Fayaz,et al.  Machine learning-based EEG signals classification model for epileptic seizure detection , 2021, Multimedia Tools and Applications.

[28]  W R Webber,et al.  An approach to seizure detection using an artificial neural network (ANN). , 1996, Electroencephalography and clinical neurophysiology.

[29]  Ram Bilas Pachori,et al.  Automated System for Epileptic EEG Detection Using Iterative Filtering , 2018, IEEE Sensors Letters.

[30]  M. L. Dewal,et al.  Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine , 2014, Neurocomputing.

[31]  M. L. Dewal,et al.  Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network , 2012, Signal, Image and Video Processing.