Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using Multivariate Statistical Process Control

Rukhsar, Salim; Khan, Yusuf Uzzaman; Farooq, Omar; Sarfraz, Mohammad; Khan, Ayesha Tooba

doi:10.1016/j.irbm.2019.08.004

Cited by 27 publications

(6 citation statements)

References 30 publications

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“…We tested the proposed Attentive recurrent neural network (ARNN) on two different heterogeneous datasets: CHB-MIT [29,30] and UPenn and Mayo Clinic's Seizure Detection Challenge dataset [23,24,31]. The performance of the ARNN cell is also compared with baseline methods.…”

Section: Resultsmentioning

confidence: 99%

Deep Optimized Electrodes and Frequency Bands in the Phase Space for Identification of Seizures

Rukhsar

Tiwari

Panda

2022

2022 IEEE 19th India Council International Conference (INDICON)

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The electroencephalogram (EEG) has been extensively used to record brain signals for the analysis of neurological diseases over several decades. Epilepsy is the one of the most common neurological disorder in the world. It is associated with seizures, which are a clinical manifestation of abnormal, excessive excitation and synchronisation of a population of cortical neurons, resulting in abrupt changes in consciousness, movement, behaviour, and feelings. Our goal in this letter is to propose a computationally efficient EEG based system that can detect seizures with improved detection performance. To deeply optimize the electrodes and frequency bands, we proposed a fast computational algorithm by finding the critical electrodes and optimal sub-bands of EEG signals. We used wavelet packet decomposition (WPD) to find optimal sub-bands and maximum relevance minimum redundancy (mRMR) method for electrodes selection. It is found that the portrait of the features in the phase space depicted promising discriminating ability to detect seizure efficiently. Experimental results show that the proposed method outperformed the state-of-art methods and provided 100% and 94.79% of detection accuracy on UBonn and CHB-MIT datasets, respectively, at a significantly reduced computational cost.

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Section: Resultsmentioning

confidence: 99%

Deep Optimized Electrodes and Frequency Bands in the Phase Space for Identification of Seizures

Rukhsar

Tiwari

Panda

2022

2022 IEEE 19th India Council International Conference (INDICON)

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“…To achieve excellent quality and defect-free operations process mapping becomes very important. It starts with understanding the process, its approach, and the application level and presenting the information with graphical representation [4]. Once the process mapping is done, the control and analysis part comes into the picture.…”

Section: Process Mapping Analysis and Controlmentioning

confidence: 99%

Benefit Using Statistical Process Control (Spc) for Process Control in Textile Manufacturing : A Review

Perkasa

2021

J. Mech. Sci. Eng.

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The history of technological developments affects the way we can provide services to customers quickly. One of the methods of industrial Control Systems is the statistical process control method (SPC) for short-term production. This review aims to explain some of the advantages of the SPC method, namely in terms of monitoring multistage processes and fault diagnosis has become a necessity. by Entering process control. The SPC and hybrid approaches and their results help in building a sound understanding of process control with a sense of a different approach which helps in analyzing processes that further assist in making the right decisions.

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“…Many researchers are urged to employ machine learning techniques for automated EEG signal analysis in light of the advancements in computer science and artificial intelligence. For instance, numerous studies have been conducted to automatically detect different neurological disorders such depression [6][7][8], epilepsy [9][10][11], seizure [12][13][14][15], Parkinson's disease [16,17], and schizophrenia [18].…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features

Movahed

Rezaeian

2022

Computational and Mathematical Methods in Medicine

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Accurate and early diagnosis of mild cognitive impairment (MCI) is necessary to prevent the progress of Alzheimer’s and other kinds of dementia. Unfortunately, the symptoms of MCI are complicated and may often be misinterpreted as those associated with the normal ageing process. To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.

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Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using Multivariate Statistical Process Control

Cited by 27 publications

References 30 publications

Deep Optimized Electrodes and Frequency Bands in the Phase Space for Identification of Seizures

Deep Optimized Electrodes and Frequency Bands in the Phase Space for Identification of Seizures

Benefit Using Statistical Process Control (Spc) for Process Control in Textile Manufacturing : A Review

Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features

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