Rohr, Maurice ; Reich, Christoph ; Höhl, Andreas ; Lilienthal, Timm ; Dege, Tizian ; Plesinger, Filip ; Bulkova, Veronika ; Clifford, Gari ; Reyna, Matthew ; Hoog Antink, Christoph (2022)
Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning.
In: Physiological Measurement, 43 (7)
doi: 10.1088/1361-6579/ac7840
Article, Bibliographie
This is the latest version of this item.
Abstract
During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge.
| Item Type: | Article |
|---|---|
| Erschienen: | 2022 |
| Creators: | Rohr, Maurice ; Reich, Christoph ; Höhl, Andreas ; Lilienthal, Timm ; Dege, Tizian ; Plesinger, Filip ; Bulkova, Veronika ; Clifford, Gari ; Reyna, Matthew ; Hoog Antink, Christoph |
| Type of entry: | Bibliographie |
| Title: | Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning |
| Language: | English |
| Date: | 7 July 2022 |
| Publisher: | IOP Publishing |
| Journal or Publication Title: | Physiological Measurement |
| Volume of the journal: | 43 |
| Issue Number: | 7 |
| DOI: | 10.1088/1361-6579/ac7840 |
| Corresponding Links: | |
| Abstract: | During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge. |
| Additional Information: | Erstveröffentlichung; Art.No.: 074001 |
| Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
| Date Deposited: | 02 Feb 2023 07:45 |
| Last Modified: | 03 Jul 2024 02:59 |
| PPN: | |
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| Suche nach Titel in: | TUfind oder in Google |
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Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning. (deposited 11 Jul 2022 13:25)
- Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning. (deposited 02 Feb 2023 07:45) [Currently Displayed]
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