Martin Glasstetter
University of Freiburg
12 Papers
7 Citations
Martin Glasstetter is an academic researcher from University of Freiburg. The author has contributed to research in topics: Computer science & Wearable computer. The author has an hindex of 4, co-authored 10 publications. Previous affiliations of Martin Glasstetter include University Medical Center Freiburg.
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Papers
Deep learning with convolutional neural networks for EEG decoding and visualization.
Robin Tibor Schirrmeister,Jost Tobias Springenberg,Lukas D. J. Fiederer,Martin Glasstetter,Katharina Eggensperger,Michael Tangermann,Frank Hutter,Wolfram Burgard,Tonio Ball +8 more
TL;DR: This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.
2.8K
Signal quality and patient experience with wearable devices for epilepsy management
Mona Nasseri,Ewan S. Nurse,Martin Glasstetter,Sebastian Böttcher,Nicholas M. Gregg,Aiswarya Laks Nandakumar,Boney Joseph,Tal Pal Attia,Pedro Viana,Pedro Viana,Elisa Bruno,Andrea Biondi,Mark J. Cook,Gregory A. Worrell,Andreas Schulze-Bonhage,Matthias Dümpelmann,Dean R. Freestone,Mark P. Richardson,Benjamin H. Brinkmann +18 more
TL;DR: Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.
Data quality evaluation in wearable monitoring
Sebastian Böttcher,Solveig Vieluf,Elisa Bruno,Boney Joseph,Nino Epitashvili,Andrea Biondi,Nicolas Zabler,Martin Glasstetter,Matthias Dümpelmann,Kristof Van Laerhoven,Mona Nasseri,Benjamin H. Brinkman,Mark P. Richardson,Andreas Schulze-Bonhage,Tobias Loddenkemper +14 more
TL;DR: In this paper , the authors proposed a combined data quality assessment tool for the evaluation of multimodal wearable data, including accelerometry, electrodermal activity, blood volume pulse, and skin temperature.
Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation.
Sebastian Böttcher,Sebastian Böttcher,Elisa Bruno,Nikolay V. Manyakov,Nino Epitashvili,Kasper Claes,Martin Glasstetter,Sarah Thorpe,Simon Lees,Matthias Dümpelmann,Kristof Van Laerhoven,Mark P. Richardson,Mark P. Richardson,Andreas Schulze-Bonhage +13 more
TL;DR: Evidence is provided that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate.
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Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor.
Martin Glasstetter,Sebastian Böttcher,Nicolas Zabler,Nino Epitashvili,Matthias Dümpelmann,Mark P. Richardson,Andreas Schulze-Bonhage +6 more
TL;DR: In this article, a wrist-worn PPG sensor was used to detect ictal tachycardia (IT) in 28 patients with electrocardiography-based evidence of seizures.
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