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 F1scores 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.
... Část II: Hluboké učení v praxi 117 -- 5 Hluboké učení pro počítačové vidění 118 -- 5.1 Seznámení s CNN ... ... Extrakce příznaků 139 -- 5.3.2 Jemné doladění 148 -- 5.3.3 Shrnutí 152 -- 5.4 Vizualizace toho, co se CNN ... ... rekurentních neuronových sítí 185 -- 6.2.1 Rekurentní vrstvy v Keras 187 -- 6.2.2 Pochopení vrstev LSTM ... ... a GRD 191 -- 6.2.3 Konkrétní příklad LSTM v Keras 193 -- 6.2.4 Shrnutí 194 -- 6.3 Pokročilé používání ... ... 211 -- 6.4.4 Kombinace CNN a RNN pro zpracování dlouhých sekvencí 213 -- Podrobný obsah -- 6.4.5 Shrnutí ...
1. elektronické vydání 1 online zdroj (328 stran)
The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as showing normal physiological or abnormal pathological activity has been performed by expert visual review. The potential value of unbiased, automated EEG classification has long been recognized, and in recent years the application of machine learning methods has received significant attention. A variety of solutions using convolutional neural networks (CNN) for EEG classification have emerged with impressive results. However, interpretation of CNN results and their connection with underlying basic electrophysiology has been unclear. This paper proposes a CNN architecture, which enables interpretation of intracranial EEG (iEEG) transients driving classification of brain activity as normal, pathological or artifactual. The goal is accomplished using CNN with long short-term memory (LSTM). We show that the method allows the visualization of iEEG graphoelements with the highest contribution to the final classification result using a classification heatmap and thus enables review of the raw iEEG data and interpret the decision of the model by electrophysiology means.
- MeSH
- Artifacts MeSH
- Datasets as Topic MeSH
- Deep Learning * MeSH
- Electroencephalography classification instrumentation methods MeSH
- Humans MeSH
- ROC Curve MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Observational Study MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Validation Study MeSH
