A channel independent generalized seizure detection method for pediatric epileptic seizures

Chakrabarti, Satarupa; Swetapadma, Aleena; Pattnaik, Prasant Kumar

doi:10.1016/j.cmpb.2021.106335

Cited by 25 publications

(20 citation statements)

References 40 publications

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“…The last layer of the decision tree consists of leaf nodes which usually have a predefined class target value. The primary backbone for building a decision tree is repeatedly dividing the nodes on each level based on the criteria for splitting [29]. This splitting and expanding phases last until a stopping criterion are encountered.…”

Section: A Traditional Classifiers 1) Decision Treementioning

confidence: 99%

“…In ensemble learning, there are various aggregation strategies; bagging is one of them, and it is employed in prediction models to reduce variance. Other strategies include: stacking which tries to reduce prediction bias, and boosting which aids in the conversion of several weak learners into an aggregated strong model [29,25]. Fig.…”

Section: B Ensemble Learningmentioning

confidence: 99%

“…The Gini index is one of the criteria to divide the data into random forest. The random forest is also important for variables, which does not only contribute to the development of a precise model but also contributes to prediction [29,30]. Fig.…”

Section: B Ensemble Learningmentioning

confidence: 99%

See 2 more Smart Citations

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

Saha¹,

Tripathy²,

Masmoudi³

et al. 2022

IJACSA

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In the telecommunication industry, being able to predict customers' behavioral pattern to successfully design and recommend a suitable tariff plan is the ultimate target. The behavioral pattern has a vital connection with the customers' demographic background. Different researches have been done based on hypothesis testing, regression analysis, and conjoint analysis to determine the interdependencies among them and the effects on the customers' behavioral needs. This has presented us with ample scope for research using numerous classification-based techniques. This work proposes a model to predict customer's behavioral pattern by using their demographic data. This model was built after investigating various types of classification-based machine learning techniques including the traditional ones like decision tree, k-nearest neighbor, logistic regression, and artificial neural networks along with some ensemble techniques such as random forest, adaboost, gradient boosting machine, extreme gradient boosting, bagging, and stacking. They are applied to a dataset collected using a questionnaire in India. Among the traditional classifiers, decision tree gave the best result of 81% accuracy and random forest showed the best result among the ensemble learning techniques with an accuracy of 83%. The proposed model has shown a very positive outcome in predicting the customers' behavioral pattern.

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Section: A Traditional Classifiers 1) Decision Treementioning

confidence: 99%

Section: B Ensemble Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

Saha¹,

Tripathy²,

Masmoudi³

et al. 2022

IJACSA

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show abstract

“…In this sense, [25] designed a PPR detection method by analysing the potential and oscillation of the response provoked by a flashing stimulation, but following a different stimulation pattern from the standard one. There are other recent studies that analyse the photosensitivity and epilepsy based on other generalized discharges or seizures than PPRs: in [18], a detection method based on the band amplitude fluctuation computed from a high-frequency and a low-frequency components of the EEG windows in each EEG channel is proposed; [30] applied the extreme gradient boost technique for the classification of seizures in two different ways (applying a standard partitioning of the data and applying a leave-one-out cross-validation scheme), while a channel-independent long short-term memory network is used in [5]; the information extracted from EEG and electrocardiogram (ECG) signals is used in [35] in a multi-modal neural network which analyse the data in three different ways (only EEG data with a convolutional LSTM network; only ECG data with a residual convolutional network; and a fused network which combines the outputs of the individual networks to perform the final classification); in [6], K-nearest neighbours and artificial neural networks are used for the detection of ictal discharges and inter-ictal states; [32] proposed an EEG single-channel analysis applying three types of visibility graphs (basic, horizontal and difference) to represent different EEG patterns.…”

Section: Introductionmentioning

confidence: 99%

Virtual reality and machine learning in the automatic photoparoxysmal response detection

Martins

Martín

Suárez

et al. 2022

Neural Comput & Applic

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Photosensitivity, in relation to epilepsy, is a genetically determined condition in which patients have epileptic seizures of different severity provoked by visual stimuli. It can be diagnosed by detecting epileptiform discharges in their electroencephalogram (EEG), known as photoparoxysmal responses (PPR). The most accepted PPR detection method—a manual method—considered as the standard one, consists in submitting the subject to intermittent photic stimulation (IPS), i.e. a flashing light stimulation at increasing and decreasing flickering frequencies in a hospital room under controlled ambient conditions, while at the same time recording her/his brain response by means of EEG signals. This research focuses on introducing virtual reality (VR) in this context, adding, to the conventional infrastructure a more flexible one that can be programmed and that will allow developing a much wider and richer set of experiments in order to detect neurological illnesses, and to study subjects’ behaviours automatically. The loop includes the subject, the VR device, the EEG infrastructure and a computer to analyse and monitor the EEG signal and, in some cases, provide feedback to the VR. As will be shown, AI modelling will be needed in the automatic detection of PPR, but it would also be used in extending the functionality of this system with more advanced features. This system is currently in study with subjects at Burgos University Hospital, Spain.

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“…As one of the oldest neurophysiological techniques, it facilitates the identification of normal and abnormal events occurring in the human brain and reveals the dynamic changes caused by seizures. Hence, it is widely used in studies related to seizure detection [3]- [6], seizure prediction [7]- [9] and the localization of epileptic focus [10]- [12].…”

mentioning

confidence: 99%

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction

Wang

Shi

Cheng

et al. 2023

IEEE J. Biomed. Health Inform.

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Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal or spatial dimension, making it difficult to take both relationships into account. Therefore, the effective properties of electroencephalograms (EEGs) may not be fully evaluated. To solve this problem, we propose a spatiotemporal graph attention network (STGAT) based on synchronization. The spatial and functional connectivity information between EEG channels was extracted by using the phase locking values (PLVs) first, which allowed multichannel EEG signals to be modeled as graph signals. Afterward, the STGAT model was used to dynamically learn the temporal correlation properties of EEG sequences and explore the spatial topological structure information of multiple channels. Experimental results demonstrated that the STGAT model was able to obtain spatiotemporal correlations and achieve good results on two benchmark datasets. The accuracy, specificity and sensitivity were 98.74%, 99.21% and 98.87%, respectively, on the CHB-MIT dataset. Moreover, all evaluation indices of the private dataset had reached more than 98.8%, with the area under the curve (AUC) reaching 99.96% . The proposed method is superior or comparable to the state-of-the-art models. Extensive experiments demonstrate that our endto-end automatic seizure prediction model can be extended to design clinical assistant decision systems.

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A channel independent generalized seizure detection method for pediatric epileptic seizures

Cited by 25 publications

References 40 publications

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

Virtual reality and machine learning in the automatic photoparoxysmal response detection

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction

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