A seizure is a burst of electrical activity in the brain that can lead to a range of complications. Seizures occur when the normal communication between brain cells is disrupted which leads to abnormal electrical activity. The unpredictable nature of seizures makes daily life challenging for those affected. Because seizures can occur without warning, individuals often face significant anxiety and fear about when and where a seizure might happen. This unpredictability can limit their participation in everyday activities, such as driving, attending social events, or even going to work or school. To address this problem, I proposed an EEG-based seizure detection system utilizing machine learning techniques. EEG captures the brain's electrical activity through electrodes placed on the scalp which provides real-time data on neuronal patterns. By analyzing this data, the system can identify specific electrical signatures associated with seizure activity. To enhance the accuracy of the system, I introduced a triplet loss function that leverages improved feature representation. Extensive experimental results clearly demonstrate that the proposed approach increased accuracy by 5.28%. Additionally, the proposed approach was evaluated with four state-of-the-art convolutional neural networks which achieved an accuracy of 80.2% on a public dataset.
Park et al. (Fri,) studied this question.