Multiresolution approach for artifacts removal and localization of seizure onset zone in epileptic EEG signal

Yedurkar, Dhanalekshmi P.; Metkar, Shilpa P.

doi:10.1016/j.bspc.2019.101794

Cited by 12 publications

(9 citation statements)

References 24 publications

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“…In the data processing module, initially the multichannel epileptic EEG data is pre-processed segment-wise using MRAF [ 29 ]. It is followed by the extraction of an optimized CSV feature using FPA and data dimensionality reduction using principal component analysis (PCA).…”

Section: Methodsmentioning

confidence: 99%

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A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm

Yedurkar

Metkar

Al‐Turjman³

et al. 2022

Sensors

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A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject’s smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain.

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Section: Methodsmentioning

confidence: 99%

“…So, it is very crucial to reduce such EEG interferences which hamper identification of the epileptogenic zone accurately. MRAF helps to remove the physiological artifacts by preserving the information of the epileptic seizure [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm

Yedurkar

Metkar

Al‐Turjman³

et al. 2022

Sensors

Self Cite

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“…It discriminated among several contamination levels and performed superior to the traditional single-channel algorithms. Yedurkar and Metkar [28] have proposed a technique for removing physiological artifacts and positioning the epileptic region. A hybrid method was recommended based on multi-resolutional analysis and adaptive filtering (MRAF) .…”

Section: Related Workmentioning

confidence: 99%

“…Electroencephalogram (EEG) is a voltage test recording the electrical activity of the neurons in the brain. EEG is obtained in various frequencies including delta (1-4 Hz), theta (4-8 Hz), alpha , beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (greater than 30 Hz) [1]. EEG brain rhythms with different frequency ranges are shown in Table 1 [2].…”

Section: Introductionmentioning

confidence: 99%

Artifact Removal Methods in EEG Recordings: A Review

Mathe

Padmaja

Krishna

2021

Proc. eng. technol. innov.

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To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

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“…In the first phase, they extracted attributes from the EEG signals using the autoregressive moving average (ARMA) model and then achieved high classification successes by classifying them with the support vector machine (SVM). Yedurkar & Metkar (2020) proposed a new method for locating the epileptic region and preventing the artifacts that occur in obtaining physiological signals from our body. They proposed a plan called multi-resolution analysis and adaptive filtering (MRAF) and applied it to the diagnosis of EEG epilepsy.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach

Alhudhaif

2021

PeerJ Computer Science

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Background Brain signals (EEG—Electroencephalography) are a gold standard frequently used in epilepsy prediction. It is crucial to predict epilepsy, which is common in the community. Early diagnosis is essential to reduce the treatment process of the disease and to keep the process healthier. Methods In this study, a five-classes dataset was used: EEG signals from different individuals, healthy EEG signals from tumor document, EEG signal with epilepsy, EEG signal with eyes closed, and EEG signal with eyes open. Four different methods have been proposed to classify five classes of EEG signals. In the first approach, the EEG signal was first divided into four different bands (beta, alpha, theta, and delta), and then 25 time-domain features were extracted from each band, and the main EEG signal and these extracted features were combined to obtain 125-time domain features (feature extraction). Using the Random Forests classifier, EEG activities were classified into five classes. In the second approach, each One-Against-One (OVO) approach with 125 attributes was split into ten parts, pairwise, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the ten classifiers. In the third proposed method, each One-Against-All (OVA) approach with 125 attributes was divided into five parts, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the five classifiers. In the fourth proposed approach, each One-Against-All (OVA) approach with 125 attributes was divided into five parts. Since each piece obtained had an imbalanced data distribution, an adaptive synthetic (ADASYN) sampling approach was used to stabilize each piece. Then, each balanced piece was classified with the Random Forests classifier. To combine the decisions obtanied from each classifier, the majority voting scheme has been used. Results The first approach achieved 71.90% classification success in classifying five-class EEG signals. The second approach achieved a classification success of 91.08% in classifying five-class EEG signals. The third method achieved 89% success, while the fourth proposed approach achieved 91.72% success. The results obtained show that the proposed fourth approach (the combination of the ADASYN sampling approach and Random Forest Classifier) achieved the best success in classifying five class EEG signals. This proposed method could be used in the detection of epilepsy events in the EEG signals.

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Multiresolution approach for artifacts removal and localization of seizure onset zone in epileptic EEG signal

Cited by 12 publications

References 24 publications

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm

Artifact Removal Methods in EEG Recordings: A Review

A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach

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