Rana N, Thakur T, Jain S. Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.
Curr Aging Sci 2025;
18:29-38. [PMID:
38706349 DOI:
10.2174/0118746098298618240429102237]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 05/07/2024]
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.
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