IoT based Efficient Epileptic Seizure Prediction System Using Deep Learning

Daoud, Hisham; Williams, Phillip; Bayoumi, Magdy

doi:10.1109/wf-iot48130.2020.9221169

Cited by 22 publications

(9 citation statements)

References 20 publications

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“…Epilepsy Foundation of Minnesota has developed an application called Seizure First Aide which provides first aid instructions to the person who attends to an epileptic patient. This lists out the steps to follow in case of a seizure attack in the patient 6 . ICE Medical Standard has created a facility that allows the patient to share information such as contact numbers, medication details to the first responder on the patient's phone's lock screen.…”

Section: Related Studymentioning

confidence: 99%

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Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Ziyad

Ziyad²

2021

SCPE

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Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.

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Section: Related Studymentioning

confidence: 99%

“…This drawback of this research work is that it focuses only on a specific type of seizure called tonic-clonic seizures. In the IoT framework proposed by [6] the EEG recording is measured and communicated via Bluetooth to FGPA which has an embedded deep learning algorithm. The EEG data along with prediction results is sent to Rasberry pi.…”

Section: Related Studymentioning

confidence: 99%

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Ziyad

Ziyad²

2021

SCPE

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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)(24)(25). A limitation of the paper is that we did not use other algorithms on our data sets for direct comparison.…”

Section: Future Research and Limitationsmentioning

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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“…Artificial Neural Networks (ANNs) have previously been used for epileptic seizure detection [11] and prediction [12] on FPGA, no previous work has investigated the use of memristors for the detection or prediction of epileptic seizures using DL, which could drastically improve the performance on the IoMT edge.…”

Section: Related Workmentioning

confidence: 99%

Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction

Lammie,

Xiang,

Azghadi

2021

Preprint

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The unpredictability of seizures continues to distress many people with drug-resistant epilepsy. On account of recent technological advances, considerable efforts have been made using different hardware technologies to realize smart devices for the real-time detection and prediction of seizures. In this paper, we investigate the feasibility of using Memristive Deep Learning Systems (MDLSs) to perform real-time epileptic seizure prediction on the edge. Using the MemTorch simulation framework and the Children's Hospital Boston (CHB)-Massachusetts Institute of Technology (MIT) dataset we determine the performance of various simulated MDLS configurations. An average sensitivity of 77.4% and a Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.85 are reported for the optimal configuration that can process Electroencephalogram (EEG) spectrograms with 7,680 samples in 1.408ms while consuming 0.0133W and occupying an area of 0.1269mm 2 in a 65nm Complementary Metal-Oxide-Semiconductor (CMOS) process.

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IoT based Efficient Epileptic Seizure Prediction System Using Deep Learning

Cited by 22 publications

References 20 publications

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

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

Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction

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