Abstract

Background: Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infections, and developmental disorders. Epileptic seizures are usually transient events. They normally leave no trace after the postictal recovery period has passed.

Aims: An electroencephalogram (EEG) can only detect brain activity during the recording. It will be detected if an epileptogenic focus or generalized abnormality is active during the recording.

Objective: This work demonstrated a smart seizure detection system for Healthcare IoT, which is a challenging problem of EEG data analysis.

Method: The study suggested an integrated methodology in recognition of the drawbacks of manual identification and the significant negative effects of uncontrollable seizures on patients' lives.

Result: The research shows remarkable accuracy, up to 100% in some experiments, by combining classifier ensembles like Decision Trees, Logistic Regression, and Support Vector Machine with different signal processing techniques like Discrete Wavelet Transform, Hjorth Parameters, and statistical features. The results were compared using the kNN classifier, other datasets and other state-of-the-art techniques.

Conclusion: Healthcare IoT is further utilized by the methodology, which takes a comprehensive approach using classifier ensembles and signal processing approaches resulting in real-time data to help them make better decisions. This demonstrates how well the suggested method works for smart seizure detection, which is a crucial development for better patient outcomes.

Keywords: EEG, seizures, feature extraction, machine learning, smart healthcare, IoMT.

Graphical Abstract