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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: Poster Session 1 (Even numbers)
Session starts: Thursday 26 January, 16:00
Presentation starts: 16:00
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.