Abstract
Epilepsy is a neurological disorder that affects millions of people worldwide, and it is characterized by recurrent seizures that can vary in frequency and severity. The management of epilepsy requires timely and accurate diagnosis, as well as effective monitoring of symptoms and treatment. Continuous monitoring of a patient’s condition can result in better outcomes and timely intervention. Remote monitoring can reduce hospital visits and ease the burden on healthcare systems. In this work, we have developed a system that uses various sensors and equipment, such as GPS and GSM module, ECG sensor, tilt, and vibration sensors to detect and predict epilepsy. The system is based on IoT technology and includes devices like the Arduino Nano and ESP32. The monitoring devices can be worn by patients all day as they are portable and easy to use, with the system constantly recording and analyzing their data which is then sent to a secure cloud server, where healthcare professionals can access it. The system can also generate alerts when the patient is experiencing a seizure, enabling timely intervention and treatment. An essential aspect of our system involves leveraging machine learning algorithms to meticulously examine the sensor-derived data for precise diagnostics. Within our system, we’ve integrated three distinct algorithms, each yielding impressive accuracy rates. Specifically, the CNN algorithm boasts an accuracy of 95.26%, Random Forest exhibits 92.93% accuracy, and Logistic Regression achieves a commendable accuracy rate of 93.68%.
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Jagadeesh Basavaiah: Conceptualization, Methodology, Investigation and Writing - Original DraftAudre Arlene Anthony: Data curation, Formal analysis, Visualization and Writing - Review & EditingMahadevaswamy S: Software, Validation and supervision of the workNaveen Kumar H N: Writing - Review & Editing and supervision of the work.
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Basavaiah, J., Anthony, A.A., Mahadevaswamy, S. et al. An efficient approach of epilepsy seizure alert system using IoT and machine learning. J Reliable Intell Environ 10, 449–461 (2024). https://doi.org/10.1007/s40860-024-00228-w
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DOI: https://doi.org/10.1007/s40860-024-00228-w