Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3084 2018 2018 ComputerScienceDepartment, Faculty of Specific Education, Mansoura, Egypt Mahy Mahy ComputerScienceDepartment, Faculty of Specific Education, Mansoura, Egypt A. F. Elgamal NeurologyDepartment, Faculty of medicine, Mansoura, Egypt I. Elmenshawi ComputerScienceDepartment, Faculty of Specific Education, Mansoura, Egypt Hanan E. Abdelkader The integration of Artificial Intelligence (AI) within the Medical Internet of Things (MIoT) is advancing swiftly, leading to significant developments in the detection of illnesses like epilepsy by analyzing Interictal Epileptiform Discharges (IED) in electroencephalograms (EEG).The availability of EEG data has facilitated the creation of innovative applications, including seizure detection. While neurologists have traditionally relied on EEG data analysis to identify epileptic seizures, the manual evaluation of EEG brain waves is a laborious and complex process that places significant stress on specialists. This paper presents a simple Convolutional Neural Network (CNN) method for the automated detection of IEDs based on EEG waveforms. This approach helps reduce the burden on epilepsy patients by forecasting seizures and enabling timely interventions. It also eases the workload for neurologists and less experienced specialists, thereby accelerating the diagnosis process. The proposed method was implemented by utilizing a series of images that depicted the magnitude of the EEG signal across each sensor. The study divided participants into two groups: (A) healthy individuals and (B) individuals with epilepsy. The results demonstrated an accuracy of up to 96.4% compared to human expert diagnoses, displaying the method's effectiveness and practicality in detecting seizure occurrences in EEG data. 2025 2025 15 25 10.54216/FPA.170102 https://www.americaspg.com/articleinfo/3/show/3084