Abstract
Advances in artificial intelligence and computer
science have allowed for powerful assistive tools in a
wide range of fields. Decision support systems could help
health professionals to provide patients with quick and
cost-efficient diagnostic analysis. The 2020 CinC
Challenge challenges participants to develop such a tool
for 12-lead ECG recordings.
In this paper, an approach for a multi-stream neural
network is presented. Two parallel models were trained
with different input data to combine the two relevant
paradigms in modern machine learning. A simple
multilayer perceptron and a deep convolutional neural
network were concatenated for the final classification.
Since the data originated from different sources, an
ensemble of models was trained.
Due to technical difficulties, we (easyG) submitted a
trimmed version and achieved a test score of -0.290,
which ranked as the 39th entry. Validation score was
0.403. Although these results were mixed, offline 5-fold
cross validation showed the potency of the full version.
Our results indicate that deep learning methods could
in fact benefit from the addition of features derived via
classical signal processing.
Originalsprache | Englisch |
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Titel | 2020 Computing in Cardiology |
Seiten | 148-151 |
Seitenumfang | 4 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2021 |
Research Field
- Exploration of Digital Health