Classification of Seizure Types Based on Statistical Variants and Machine Learning

Shankar, Anand; Dandapat, Samarendra; Barma, Shovan

doi:10.1109/indicon52576.2021.9691717

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Supervised ML classifiers are commonly employed in this domain [ 7 , 8 , 9 , 10 , 11 , 12 ], along with deep learning (DL) techniques [ 13 , 14 ]. These approaches are geared towards developing models adept at binary classification [ 7 , 8 , 9 , 11 , 12 ] as well as multiclass classification tasks [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…The methodologies for feature extraction in this context span across time, frequency, time–frequency, and non-linear domains [ 17 ], utilizing values from EEG signals over brief temporal windows. Time domain techniques often employ statistical metrics such as standard deviation, kurtosis, skewness, and mean [ 8 , 15 , 16 ]. Frequency domain approaches typically involve the use of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) coefficients, either directly or through derived statistical measures of these coefficients [ 7 , 9 , 10 , 11 , 12 , 16 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Time domain techniques often employ statistical metrics such as standard deviation, kurtosis, skewness, and mean [ 8 , 15 , 16 ]. Frequency domain approaches typically involve the use of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) coefficients, either directly or through derived statistical measures of these coefficients [ 7 , 9 , 10 , 11 , 12 , 16 , 18 , 19 ]. Owing to the inherent non-linearity of EEG signals [ 20 ], certain studies also focus on extracting non-linear features, including Sample Entropy, Wavelet Entropy [ 21 ], and the Lyapunov exponent [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals

Vieira,

Guedes,

Santos

et al. 2023

Sensors

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Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models—without resorting to deep learning—is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals

Vieira,

Guedes,

Santos

et al. 2023

Sensors

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“…The symptoms as well as types of seizures vary. The two major classes of seizures are focal or partial onset and generalized onset seizures [3]. Focal onset seizures start from one area of the brain and can spread across the brain leading to mild or severe symptoms [4].…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Seizure Detection Using Feed- Forward and LSTM Neural Networks Based on a Neonates Dataset

Zeedan¹,

Al-Fakhroo²,

Barakeh³

2022

Preprint

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<p>Electroencephalography (EEG) signals are used for the diagnosis of neurological disorders and detection of seizures. Early detection of seizures not only can save the patient’s life but also can improve the quality of life. The traditional methods for seizure detection are time-consuming. To combat these problems and further improve the accuracy of seizure detection, the techniques of deep machine learning have been applied to EEG signals. This work develops an EEG-based seizure detection program using deep machine learning to detect seizures using EEG signals. The model is developed using a data set of EEG recordings from 79 human subjects. To this end, a literature review has been conducted to review the latest research related to deep learning algorithms applied to EEG signals for seizure detection. The EEG data has been pre-processed, filtered, and segmented before being fed to the machine learning model. Three models were developed, two models are based on feed-forward neural networks and the third model is based on long-short term memory (LSTM) network. For the feed-forward neural network with 10 hidden neurons, the obtained accuracy was too low (66.1%). For the feed-forward neural network with 100 hidden neurons, the accuracy was slightly improved to 74.3%. The LSTM model showed an accuracy of 87.7%. The model correctly classified 96.4% of normal patients as having no seizures and correctly classified 71.6% of seizure patients as having seizures. The limitations on increasing the accuracy of the models are discussed and possible solutions are suggested. If channel-specific seizure annotations are provided by medical experts, the accuracy of the model is expected to increase significantly.</p>

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“…For instance, Saputro et al [9], employed a support vector machine (SVM) along with principal component analysis with features such as Mel frequency cepstral coefficient (MFCC) and Hjorth descriptor from EEG signal to classify three different types of seizure and achieved accuracy up to 91.4%; Inung et al [10], discriminated four seizure types using statistical variants as features that were extracted from decomposed components of EEG and achieved 95% of classification accuracy; Kassahun et al [11], classified two types of seizure by involving different machine learning algorithms and reached accuracy up to 77.8%. Shankar et al [12], employed five different machine learning algorithms to classify three types of seizures along with seizure-free using statistical features which were extracted directly from raw EEG and got reasonable accuracy. However, the performance of these methods fully relies on how and what kinds of predefined features are chosen, which is not very recommendable as very small differences exist among different types of seizures [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

Shankar

Dandapat

Barma

2022

IEEE Open J. Instrum. Meas.

Self Cite

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Discrimination of types of seizure using the Electroencephalogram (EEG) signal has always been a challenging task due to minuscule differences among different types of seizures. In this regard, deep learning (DL) which has already evidenced notable performance in image recognition could be suitable. However, a few attempts have been made so far in this regard mainly by constructing 2D input images for DL from 1D EEG signals directly using various techniques. Besides, the quality of the generated images has not been verified. Therefore, in this work, 2D images for the DL pipeline have been generated from brain rhythms, which already displayed remarkable performance in analyzing various brain activities. For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. Further, the quality of the 2D image along with appropriate brain rhythms have also been investigated. For experimental validation, EEG recordings of six different types of seizure that are provided by the Temple University EEG dataset (TUH v1.5.2) has been taken into account. The proposed method has achieved the highest classification accuracy and weighted F1-score up to 91.1% and 91.0% respectively. Further analysis shows that higher image resolution can provide the best classification accuracy. In addition, the δ rhythm has been found the most suitable in seizure type classification. In a comparative study, the proposed idea demonstrated its superiority by displaying the uppermost classification performance.

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Classification of Seizure Types Based on Statistical Variants and Machine Learning

Cited by 11 publications

References 19 publications

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals

EEG-Based Seizure Detection Using Feed- Forward and LSTM Neural Networks Based on a Neonates Dataset

Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

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