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12:45
15 mins
Using 2D CNN to detect tonic-clonic seizures based on accelerometer and photoplethysmography signals
Chunjiao Dong, Johannes van Dijk, Xi Long, Ronald M. Aarts
Session: Wearable
Session starts: Friday 27 January, 11:30
Presentation starts: 12:45
Room: Room 531
Chunjiao Dong (Eindhoven University of Technology)
Johannes van Dijk (Eindhoven University of Technology)
Xi Long (Eindhoven University of Technology)
Ronald M. Aarts (Eindhoven University of Technology)
Abstract:
This study aims to design a deep learning model to automatically detect tonic-clonic (TC) seizure events based on accelerometer (ACM) and photoplethysmography (PPG) signals, which are vital signals to illustrate the motion and heart rate changes during TC seizures [1]. Both signals were continuously collected using NightWatch armbands [2], from 44 patients during the night, and each patient was monitored for two to three months.