Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals

Sánchez-Hernández, Sergio E; Salido-Ruiz, Ricardo A.; Torres-Ramos, Sulema; Román-Godínez, Israel

doi:10.3390/s22083066

Cited by 27 publications

(21 citation statements)

References 56 publications

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“…The comparison showed that SAW had better accuracy and

. Regarding

, it exceeded the study by Sánchez et al [ 55 ], but as SAW tends to preserve a Negative-class majority, it had a lower

than Dissanayake et al [ 44 ].…”

Section: Implementation and Resultscontrasting

confidence: 67%

Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream

Fatlawi

Kiss

2022

Entropy

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Data stream mining techniques have recently received increasing research interest, especially in medical data classification. An unbalanced representation of the classification’s targets in these data is a common challenge because classification techniques are biased toward the major class. Many methods have attempted to address this problem but have been exaggeratedly biased toward the minor class. In this work, we propose a method for balancing the presence of the minor class within the current window of the data stream while preserving the data’s original majority as much as possible. The proposed method utilized similarity analysis for selecting specific instances from the previous window. This group of minor-class was then added to the current window’s instances. Implementing the proposed method using the Siena dataset showed promising results compared to the Skew ensemble method and some other research methods.

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“…The comparison showed that SAW had better accuracy and

. Regarding

, it exceeded the study by Sánchez et al [ 55 ], but as SAW tends to preserve a Negative-class majority, it had a lower

than Dissanayake et al [ 44 ].…”

Section: Implementation and Resultscontrasting

confidence: 67%

Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream

Fatlawi

Kiss

2022

Entropy

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“…For each of the 3 filtered ear EEG signals, the following statistical variables were calculated in each of the windows [ 40 , 41 , 42 ]: mean, median, mode, standard deviation, variance, absolute deviation, 25th percentile, 75th percentile, interquartile range, kurtosis, skewness and harmonic mean. In addition, spectral power [ 43 ] and entropy were also calculated, thus generating the following characteristics: mean spectral power, maximum spectral power value, maximum frequency of the maximum spectral power value, median entropy, maximum entropy value and minimum entropy value.…”

Section: Methodsmentioning

confidence: 99%

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Zambrana-Vinaroz

Vicente-Samper

Manrique-Córdoba

et al. 2022

Sensors

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Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.

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“…Statistical properties, such as mean, median, variance, standard deviation, skewness, kurtosis, peak amplitude, minimum amplitude, peak to peak, and similar, are the simplest features that may be derived from an EEG signal in the time domain [ 22 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Hjorth parameters are based on the variance of the subsequent derivatives of the EEG signal.…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.

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Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals

Cited by 27 publications

References 56 publications

Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream

Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

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