EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence

by   Shivam Gupta, et al.

Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70 solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system EpilNet is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13 five classes, leading to a significant increase of about 6-7 related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.


Neural Network Based Epileptic EEG Detection and Classification

Timely diagnosis is important for saving the life of epileptic patients....

Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is an age-related progressive and neurodegenera...

Automated Human Mind Reading Using EEG Signals for Seizure Detection

Epilepsy is one of the most occurring neurological disease globally emer...

Early Prediction of Epilepsy Seizures VLSI BCI System

Controlling the surrounding world and predicting future events has alway...

Automated Epilepsy Diagnosis Using Interictal Scalp EEG

Approximately over 50 million people worldwide suffer from epilepsy. Tra...

Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction

Developing a Brain-Computer Interface (BCI) for seizure prediction can h...

Distangling Biological Noise in Cellular Images with a focus on Explainability

The cost of some drugs and medical treatments has risen in recent years ...

Please sign up or login with your details

Forgot password? Click here to reset