A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
Anubha GuptaMandar Karlekar

Abstract

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boos...Continue Reading

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Citations

Dec 17, 2019·Healthcare Technology Letters·Piyush SwamiTapan K Gandhi
Apr 28, 2020·Computers in Biology and Medicine·Shuhan YangFengfeng Zhou
Mar 30, 2021·Physical and Engineering Sciences in Medicine·Virender Kumar MehlaRam Bilas Pachori
Mar 30, 2021·Computers in Biology and Medicine·Hong PengBin Hu
May 11, 2021·Biocybernetics and Biomedical Engineering·Narendra Kumar MishraShiv Dutt Joshi
Aug 1, 2021·Computers in Biology and Medicine·Pritam KhanAmir H Gandomi
Oct 23, 2020·Computers in Biology and Medicine·Yunxiao LiuJing Wang

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