Description of Invention
Epilepsy is known as one of the neurological disorders that will cause seizures, it is known as a sudden abnormal electrical activity that happened in the brain. Seizures usually cause abnormal muscle activity, sensations, and even the loss of consciousness. These symptoms usually last for a few minutes. Hence, an epileptic seizure might cause the loss of life when the occurrence of the seizure causes deadly accidents such as road accidents or fall into death. According to the World Health Organization (WHO), there are around 50 million people around the world suffered from epilepsy. A journal published in Frontiers in Epidemiology: Mortality, and life expectancy in Epilepsy and Status Epilepticus—current trends and future aspects (2023) stated that about 125,000 people die each year due to epilepsy. However, there is a fact showing that an estimated 70% of epileptic patients could live seizure-free if they are diagnosed in the early stage and received proper medical treatment. The common method in diagnosing epilepsy includes blood tests and neurological exams. The entire process of diagnosis is very time-consuming. Besides, the diagnosis also requires medical experts and doctors who have great experience in the diagnosis process. However, we are facing a situation in the medical field which is the shortage of neurologists. In the face of a shortage of medical officers, the implementation of our proposed AI-powered seizure detection system MindAlert can serve as a promising alternative. This technology can offer an effective solution for the timely and accurate detection of seizures, aiding in the diagnosis and treatment of patients. By leveraging artificial intelligence algorithms, the system can analyze EEG data and quickly identify seizure activity, potentially reducing the burden on medical officers and ensuring prompt intervention when needed. In this research, we benchmark the MLBCNN deep learning models with the machine learning models as well as the common deep learning models. As a result, the developed MLBCNN outperformed the other machine learning models and the common deep learning models with the best classification performance with an accuracy of 97.4% The developed MLBCNN has achieved the best classification performance by comparing with the other benchmark machine learning and deep learning neural network. Hence, with such a high accuracy and recall rate in classifying the seizure EEG signal, it is quite convincing that this MindAlert system can be implemented as part of the epileptic seizure diagnostic tool to assist doctors in diagnosing accurately with a shorter diagnosis time. This will definitely benefit not only the doctors but the patients too, as earlier diagnosis may increase the chance to stay in seizure-free life. This brings good news to the nation and globally too as the significant economic impact caused by the epileptic seizure disease can be greatly reduced.