msadeqsirjani / synapse

seizure detector with neural networks

synapse

Seizure detection is the process of detecting seizures in people with epilepsy. Seizures are a result of abnormal electrical activity in the brain, and can be very dangerous if not detected in time. Seizure detection systems are designed to alert caregivers or medical professionals when a seizure is detected, so that appropriate action can be taken.

There are several different types of seizure detection systems available. Some systems use wearable sensors to detect changes in body temperature, heart rate, and other physiological signs that can indicate a seizure. Other systems use electroencephalography (EEG) to measure electrical activity in the brain and detect abnormal patterns that can indicate a seizure.

No matter which system is used, the goal of seizure detection is to provide an early warning so that appropriate action can be taken quickly. Seizure detection systems can help reduce the risk of injury or death due to a seizure, and can provide peace of mind to those living with epilepsy.

This project is an attempt to create an automated system for detecting epileptic seizures using TensorFlow and Python. The system is designed to detect seizures in EEG signals by analyzing the signals and identifying patterns that indicate the presence of a seizure.

The system consists of one components: a convolutional neural network (CNN). The CNN is used to extract features from the EEG signals and then used them to classify the signals as either “seizure” or “non-seizure”.

The system is trained on a dataset of EEG signals from epileptic patients. The dataset consists of both seizure and non-seizure EEG signals. The CNN is used to extract features from the EEG signals and used them to classify the signals.

The system is evaluated using a train-test validation technique. The results show that the system is able to accurately detect seizures with an accuracy of 79.6%.

This project demonstrates the potential of using deep learning techniques for automated seizure detection. The system could be further improved by using more advanced techniques such as recurrent neural networks and long short-term memory networks.