Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

backgroundEpilepsy, 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.

aimsAn 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.

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

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

resultThe 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.

conclusionHealthcare 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.