This study introduces the Epilet Band, an innovative wristwatch-like device employing machine learning algorithms to detect Convulsive Epileptic Seizures (CES) in real-time. Distinguished by its use of Federated Machine Learning (FML), it ensures maximum data privacy and minimal data transfer. The Epilet band uses the DAP (detection, avoidance and prevention) method and incorporates a carefully selected array of sensors– accelerometer, gyroscope, temperature sensor, light intensity sensor and a digital microphone– all integrated into the Arduino Nano 33 BLE Sense. This study developed a seizure generator that replicates the movement patterns observed during epileptic seizures. Utilizing the Edge Impulse training platform, a machine learning model is trained to recognize these seizures, continually refining its accuracy through retraining and remodeling processes. The Epilet Band works by differentiating epileptic seizures from day-to-day activities by detecting muscle movement patterns produced by the two actions. Moreover, the Epilet Band actively detects ambient temperature, humidity, noise levels and light intensity (for example flashing lights over a period of 5 seconds) and alerts the user’s caretaker in case these conditions align with those which trigger epileptic seizures, with a message that an attack could be triggered. Research findings demonstrate the efficacy of the proposed Epilet band in detecting uncontrollable convulsive seizures timely. The machine learning model allows for improved accuracy of the detection algorithm as the number of trials and sample size increase with time. Being alerted of a potential seizures or triggers affords caretakers the opportunity to act fast to reduce fatalities or unfortunate accidents caused by a sudden onset of seizure. This exhaustive study underscores the innovation and scientific diligence captured by the Epilet Band, illustrating a future where epilepsy management is significantly empowered through technology trained by existing data, offering new horizons for individuals afflicted with this condition.
Abstract This study introduces the Epilet Band, an innovative wristwatch-like device employing machine learning algorithms to detect Convulsive Epileptic Seizures (CES) in real-time. [...]
Innovation in Epilepsy Management in South Asia: Design and Development of a Wearable Seizure Detection Device 
