Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model

Dash, Deba Prasad; Kolekar, Maheshkumar H.; Jha, Kamlesh

doi:10.1016/j.compbiomed.2019.103571

Cited by 56 publications

(16 citation statements)

References 24 publications

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“…Researchers have used different combination of time, frequency, joint time‐frequency, sparse and rational domain techniques for ASDS. Such combinations are often termed as “hybrid features” which extract and interpret spectral complexities, nonlinear, non‐deterministic and non‐Gaussian nature of EEG signals (Alickovic et al, 2018; Bhattacharyya et al, 2017; Dash et al, 2020; Garcés Correa et al, 2019; Panwar et al, 2019; Raghu et al, 2019; Solaija et al, 2018; Tsiouris et al, 2018; Wang et al, 2019; Yao et al, 2021). The present study is focused on quantitative statistical analysis of generated metadata to observe above mentioned nature of EEG signals.…”

Section: Methodsmentioning

confidence: 99%

“…Automatic classification of EEG signals has received significant attention from researchers over the past decade (Gotman, 2011). Various studies have focused on EEG signal classification for seizures, epilepsy, etc., based on novel feature estimations (Bhattacharyya et al, 2017; Panwar et al, 2019; Raghu et al, 2019), advanced signal processing methods (Alickovic et al, 2018; Dash et al, 2020; Garcés Correa et al, 2019; Hassan & Haque, 2015; Solaija et al, 2018), and state‐of‐the‐art Machine Learning (ML) and Deep Learning (DL) algorithms (Panwar et al, 2019; Tsiouris et al, 2018; Wang et al, 2019; Yao et al, 2021). The general framework used for detection purposes is depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Peri‐ictal and non‐seizure EEG event detection using generated metadata

Handa

Goel

2021

Expert Systems

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Lack of open access, seizure specific database has hindered the development of Automated Seizure Detection System (ASDS) along with state‐of‐the‐art feature selection and classification methods. Available databases contain noise, artefacts, different length & time EEG segments, associated comorbidities, clinical settings, and epilepsy/seizure types etc. Pre‐processing of such continuous EEG segments requires significant amount of time and may feed redundant information (with reference to a seizure event) to the classification model leading to inaccurate, real‐time seizure event detection systems. Hence, this paper proposes a metadata generation of large EEG databases (here, CHB‐MIT EEG scalp database v.1.0.0) for ASDS. We elucidate the need to generate seizure sensitive data through peri‐ictal and non‐seizure EEG segments. This paper performs multi‐variate analysis and two class (non‐seizure and seizure event) classification between these fixed length and time EEG segments from support vector machine (SVM) and k‐NN classifier. We thoroughly analysed the variation and dependence of different kernels, cost function, gamma, and degree for SVM based pipeline. The proposed pipeline has been compared with state‐of‐the‐art pipelines and has achieved good classification score. Such methods will help in development of generalised approach toward handling large EEG databases and machine learning applications for seizure detection and prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Peri‐ictal and non‐seizure EEG event detection using generated metadata

Handa

Goel

2021

Expert Systems

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“…The correlational dimension D 2 can be calculated with both monofractal and multifractal approaches. The Katz fractal dimension (KFD) [ 82 ], the Petrosian fractal dimension (PFD) [ 83 ], and the Higuchi fractal dimension (HFD) [ 84 ] are different approaches to the estimation of the fractal dimension. With multifractal time-series analysis, a fractal spectrum consisting of multiple fractal dimensions can be obtained [ 85 , 86 ].…”

Section: Eeg Featuresmentioning

confidence: 99%

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Stančin¹,

Cifrek²,

Jović³

2021

Sensors

148

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Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

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“…They claimed the classification accuracy of more than 99% in binary classification on ten subjects only. Dash et al [24] proposed an iterative filtering-based decomposition of EEG signals to improve the accuracy of seizure detection. Hidden Markov Model is used as a probabilistic classifier to detect the seizure event.…”

Section: Related Workmentioning

confidence: 99%

Classification of Seizure Types Using Random Forest Classifier

Ashjan¹,

Arif²

2021

Adv. Sci. Technol. Res. J.

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Epilepsy is one of the most common mental disorders in the world, affecting 65 million people. The prevalence in Arab countries of Epilepsy is estimated at 174 per 100,000 individuals, and in Saudi Arabia is 6.54 per 1,000 individuals. Epilepsy seizures have different types, and each patient needs to have a treatment plan according to the seizure type. Hence, accurate classification of seizure type is an essential part of diagnosing and treating epileptic patients. In this paper, the features based on fast Fourier transform from EEG montages were used to classify different types of seizures. Since the distribution of classes was not uniform, the dataset suffered from severe imbalance. Various algorithms were used to under-sample the majority class and over-sample the minority classes. Random forest classifier produced classification accuracy of 96% to differentiate three types of seizures from the healthy EEG reading.

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Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model

Cited by 56 publications

References 24 publications

Peri‐ictal and non‐seizure EEG event detection using generated metadata

Peri‐ictal and non‐seizure EEG event detection using generated metadata

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Classification of Seizure Types Using Random Forest Classifier

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