Implementasi internet of medical things (IOMT) dan perangkat wearable untuk deteksi kejang epilepsi: Sebuah tinjauan literatur
DOI: https://doi.org/10.33024/hjk.v20i6.3362
Epilepsy Epilepsy Monitoring Internet of Medical Things Seizure Detection Wearable Devices
Abstract
Background: Epilepsy affects over 50 million people worldwide and poses risks of Sudden Unexpected Death in Epilepsy (SUDEP). Standard video-EEG (vEEG) monitoring is limited by cost, mobility, and discomfort, while self-reporting is often inaccurate due to post-ictal amnesia.
Purpose: To evaluate recent developments in Internet of Medical Things (IoMT) technology and the effectiveness of various biosensor modalities for automated seizure detection.
Method: This literature review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The article search was designed using the PICOS framework: P (problem, patient, or population), I (intervention, prognostic factor, or exposure), C (comparison or control), O (outcome), and S (study). The literature search was conducted on PubMed, ScienceDirect, and Google Scholar using the keywords “epilepsy” AND “epileptic seizure” AND “Internet of Medical Things” AND “wearable devices” AND “seizure detection” AND “seizure monitoring” AND “machine learning” AND “deep learning.” The publication timeframe was from 2020 to 2026, and the search included articles in both English and Indonesian. The search yielded 12 relevant and usable articles.
Results: Findings indicate that while high-level architectures like transformer-based SEEGformer offer the highest accuracy, non-invasive wearables are more practical for daily use. Multimodal sensor integration (ACC, EDA, and ECG) is proven superior in reducing False Alarm Rates (FAR) compared to single-sensor approaches. Local innovations such as EpCare demonstrate significant potential in pre-ictal phase detection using the Cardiac Sympathetic Index (CSI). IoMT integration with cloud and smartphone platforms enables real-time notifications and the development of closed-loop systems for automated intervention. However, limited datasets, inter-individual physiological variability, and non-motor seizure detection remain primary challenges.
Conclusion: The combination of multimodal wearables, artificial intelligence, and IoMT integration represents the most promising solution for future epilepsy management. Large-scale validation and personalized approaches are essential to enhance the clinical effectiveness of these systems.
Keywords: Epilepsy; Epilepsy Monitoring; Internet of Medical Things; Seizure Detection; Wearable Devices.
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