DeepPlayer: An open-source SignalPlant plugin for deep learning inference

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F23%3A00565476" target="_blank" >RIV/68081731:_____/23:00565476 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26220/22:PU146235 RIV/00216208:11120/23:43924304 RIV/00064173:_____/23:43924304 RIV/00159816:_____/23:00079792

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/10.1002/spe.3159" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/spe.3159</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/spe.3159" target="_blank" >10.1002/spe.3159</a>

Result language

angličtina

Original language name

DeepPlayer: An open-source SignalPlant plugin for deep learning inference

Original language description

Background and Objective Machine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre-trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal. Methods The DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding. Results We developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant. The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre-processing and post-processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service. Conclusion The DeepPlayer plugin allows running deep learning models easily and interactively. Therefore, experts and non-AI experts alike can explore and apply deep learning models for (biomedical) signal processing. Its ease of use and interactivity might also contribute to a better understanding and acceptance of AI methods in biomedicine.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Software-Practice & Experience

ISSN

0038-0644

e-ISSN

1097-024X

Volume of the periodical

53

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

455-464

UT code for WoS article

000877357800001

EID of the result in the Scopus database

2-s2.0-85141344500