Abstract
The unpredictable nature of epileptic seizures makes it challenging to detect and effectively treat this disorder. The seizures are random, and most epileptic patients experience dangerous physical symptoms during an attack that renders the patient uneasy when conducting their daily tasks. This paper focuses on the generalised type of epilepsy, namely "Grand mal epilepsy Tonic-Clonic (GTC) seizure. The research aims to monitor symptoms of epileptic disease behaviour signals in humans and prevent it at its early stage of illness. To achieve this objective, we used the Electrocardiogram (ECG), Electromyography (EMG), accelerometer 3-axes for fall detection, and Dallas sensor for body temperature signals monitoring for updating the IoT system. The fuzzy logic algorithm that has been used to assess specified data set of diseased patients' parameters allows the classification into diverse types of seizures such as heart rate, body temperature, muscles spasm and falls. These are used as inputs to obtain the seizure type as an output which is then illustrated graphically on the dashboard of an IoT platform (Think-Speak), where abnormal conditions have been used to notify the medical personnel by sending an SMS message through "If This Then That" (IFTTT) technology. A prototype of an epileptic monitoring system has been successfully built and tested. It has an average accuracy of 98.90%, 95.49%, 83.0%, and 87.21% for body temperature, heart rate monitoring, muscle spasm, and fall detection.