Real-time epileptic seizure prediction based on online monitoring of pre-ictal features

Sadeghzadeh, Hoda; Hosseini-Nejad, Hossein; Salehi, Sina

doi:10.1007/s11517-019-02039-1

Cited by 13 publications

(2 citation statements)

References 26 publications

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“…Accurate seizure prediction and detection will enable the creation of wearable and implantable devices. In recent work on seizure prediction using scalp EEG, there have been advancements that will make it easier to deploy within hardware ( 23 – 25 ). A limitation of the paper is that we did not use other algorithms on our data sets for direct comparison.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

Fields

Panov

et al. 2021

Front. Neurol.

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In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.

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Section: Discussionmentioning

confidence: 99%

Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

Fields

Panov

et al. 2021

Front. Neurol.

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“…Moreover, the diagnosis result is also sensitive and should be protected. As an example, patients with epilepsy may experience feelings of shame and isolation [12]. Releasing diagnosis result may lead to potential psychological problems.…”

Section: Introductionmentioning

confidence: 99%

Real-time privacy preserving disease diagnosis using ECG signal

Miao¹,

Ding²,

Wu³

2022

Preprint

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The rapid development in Internet of Medical Things (IoMT) boosts the opportunity for real-time health monitoring using various data types such as electroencephalography (EEG) and electrocardiography (ECG). Security issues have significantly impeded the e-healthcare system implementation. Three important challenges for privacy preserving system need to be addressed: accurate matching, privacy enhancement without compromising security, and computation efficiency. It is essential to guarantee prediction accuracy since disease diagnosis is strongly related to health and life. In this paper, we propose efficient disease prediction that guarantees security against malicious clients and honest-but-curious server using matrix encryption technique. A biomedical signal provided by the client is diagnosed such that the server does not get any information about the signal as well as the final result of the diagnosis while the client does not learn any information about the server's medical data. Thorough security analysis illustrates the disclosure resilience of the proposed scheme and the encryption algorithm satisfies local differential privacy. After result decryption performed by the client's device, performance is not degraded to perform prediction on encrypted data. The proposed scheme is efficient to implement real-time health monitoring.

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Improved Patient-Independent Seizure Detection System Using Novel Feature Extraction Techniques

Nandini

Yadav

Rani

et al. 2021

Proceedings of Second Doctoral Symposium on Computational Intelligence

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Real-time epileptic seizure prediction based on online monitoring of pre-ictal features

Cited by 13 publications

References 26 publications

Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

Real-time privacy preserving disease diagnosis using ECG signal

Improved Patient-Independent Seizure Detection System Using Novel Feature Extraction Techniques

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