Theoretical and methodological analysis of EEG based seizure detection and prediction: An exhaustive review

Cherian, Resmi; Kanaga, E Gracemary

doi:10.1016/j.jneumeth.2022.109483

Cited by 36 publications

(17 citation statements)

References 138 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually, the method is used for off-line seizure detection in curated data sets, many of them recorded at a limited bandwidth ( Pachori and Bajaj, 2011 ). Nevertheless, several advances have been made for seizure detection using this method ( Cherian and Kanaga, 2022 ). In most cases of these approaches the first IMF (IMF1) derived by the EMD analysis is considered an artifact and is dismissed as such ( Zahra et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, the changes in center frequency and spectral power of IMF1 would have put the seizure start 6 s and the seizure end 1 s ahead of the respective times defined by the seizure detection algorithm ( Figure 6B ). It is clear that detection of these critical time points is still ambiguous, and new algorithms based on deep learning may help elucidating this problem ( Antonoudiou and Maguire, 2020 ; Cho and Jang, 2020 ; Abou Jaoude et al, 2022 ; Cherian and Kanaga, 2022 ). In the future, it will be interesting to compare the performance of the deep learning and AI seizure detection approaches to the EMD analyses presented here.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Capturing the power of seizures: an empirical mode decomposition analysis of epileptic activity in the mouse hippocampus

Molńar

Ferando

Liu

et al. 2023

Front. Mol. Neurosci.

View full text Add to dashboard Cite

IntroductionVarious methods have been used to determine the frequency components of seizures in scalp electroencephalography (EEG) and in intracortical recordings. Most of these methods rely on subjective or trial-and-error criteria for choosing the appropriate bandwidth for filtering the EEG or local field potential (LFP) signals to establish the frequency components that contribute most to the initiation and maintenance of seizure activity. The empirical mode decomposition (EMD) with the Hilbert-Huang transform is an unbiased method to decompose a time and frequency variant signal into its component non-stationary frequencies. The resulting components, i.e., the intrinsic mode functions (IMFs) objectively reflect the various non-stationary frequencies making up the original signal.Materials and methodsWe employed the EMD method to analyze the frequency components and relative power of spontaneous electrographic seizures recorded in the dentate gyri of mice during the epileptogenic period. Epilepsy was induced in mice following status epilepticus induced by suprahippocampal injection of kainic acid. The seizures were recorded as local field potentials (LFP) with electrodes implanted in the dentate gyrus. We analyzed recording segments that included a seizure (mean duration 28 s) and an equivalent time period both before and after the seizure. Each segment was divided into non-overlapping 1 s long epochs which were then analyzed to obtain their IMFs (usually 8–10), the center frequencies of the respective IMF and their spectral root-mean-squared (RMS) power.ResultsOur analysis yielded unbiased identification of the spectral components of seizures, and the relative power of these components during this pathological brain activity. During seizures, the power of the mid frequency components increased while the center frequency of the first IMF (with the highest frequency) dramatically decreased, providing mechanistic insights into how local seizures are generated.DiscussionWe expect this type of analysis to provide further insights into the mechanisms of seizure generation and potentially better seizure detection.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Capturing the power of seizures: an empirical mode decomposition analysis of epileptic activity in the mouse hippocampus

Molńar

Ferando

Liu

et al. 2023

Front. Mol. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Diverse components of the data collection are used to train the decision trees. Each DT receives a fresh sample as input, and the forest chooses the classification with the greatest votes [ 65 ].…”

Section: Machine Learning Techniques For Epileptic Seizure Detectionmentioning

confidence: 99%

An overview of machine learning methods in enabling IoMT-based epileptic seizure detection

Al-hajjar

Al-Qurabat

2023

J Supercomput

View full text Add to dashboard Cite

The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today’s world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people’s lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.

show abstract

“…Deep learning is a subset of machine learning that employs artificial neural networks with many hidden layers. Such artificial neural networks learn hierarchical representations of input data, which makes them particularly suitable for analyzing complex, high-dimensional EEG signals [ 30 , 31 , 32 , 33 , 34 ]. Some deep learning models have obtained remarkable accuracy for detecting seizures, such as Convolutional Neural Network (CNN) [ 28 ], Recurrent-CNN (RCNN) [ 26 ], and auto-encoders [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Al-Hussaini

Mitchell

2023

Bioengineering

View full text Add to dashboard Cite

This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

show abstract

Theoretical and methodological analysis of EEG based seizure detection and prediction: An exhaustive review

Cited by 36 publications

References 138 publications

Capturing the power of seizures: an empirical mode decomposition analysis of epileptic activity in the mouse hippocampus

Capturing the power of seizures: an empirical mode decomposition analysis of epileptic activity in the mouse hippocampus

An overview of machine learning methods in enabling IoMT-based epileptic seizure detection

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Contact Info

Product

Resources

About