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Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification
Early detection of malign patterns in patients’ biological signals can save millions of lives. Despite the steady improvement of artificial intelligence–based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients’ data. Previou...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8373129/ https://www.ncbi.nlm.nih.gov/pubmed/34407948 http://dx.doi.org/10.1126/sciadv.abh0693 |
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author | Cucchi, Matteo Gruener, Christopher Petrauskas, Lautaro Steiner, Peter Tseng, Hsin Fischer, Axel Penkovsky, Bogdan Matthus, Christian Birkholz, Peter Kleemann, Hans Leo, Karl |
author_facet | Cucchi, Matteo Gruener, Christopher Petrauskas, Lautaro Steiner, Peter Tseng, Hsin Fischer, Axel Penkovsky, Bogdan Matthus, Christian Birkholz, Peter Kleemann, Hans Leo, Karl |
author_sort | Cucchi, Matteo |
collection | PubMed |
description | Early detection of malign patterns in patients’ biological signals can save millions of lives. Despite the steady improvement of artificial intelligence–based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients’ data. Previous studies have identified organic electrochemical devices as ideal candidates for biosignal monitoring. However, their use for pattern recognition in real time was never demonstrated. Here, we produce and characterize brain-inspired networks composed of organic electrochemical transistors and use them for time-series predictions and classification tasks using the reservoir computing approach. To show their potential use for biofluid monitoring and biosignal analysis, we classify four classes of arrhythmic heartbeats with an accuracy of 88%. The results of this study introduce a previously unexplored paradigm for biocompatible computational platforms and may enable development of ultralow–power consumption hardware-based artificial neural networks capable of interacting with body fluids and biological tissues. |
format | Online Article Text |
id | pubmed-8373129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83731292021-08-27 Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification Cucchi, Matteo Gruener, Christopher Petrauskas, Lautaro Steiner, Peter Tseng, Hsin Fischer, Axel Penkovsky, Bogdan Matthus, Christian Birkholz, Peter Kleemann, Hans Leo, Karl Sci Adv Research Articles Early detection of malign patterns in patients’ biological signals can save millions of lives. Despite the steady improvement of artificial intelligence–based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients’ data. Previous studies have identified organic electrochemical devices as ideal candidates for biosignal monitoring. However, their use for pattern recognition in real time was never demonstrated. Here, we produce and characterize brain-inspired networks composed of organic electrochemical transistors and use them for time-series predictions and classification tasks using the reservoir computing approach. To show their potential use for biofluid monitoring and biosignal analysis, we classify four classes of arrhythmic heartbeats with an accuracy of 88%. The results of this study introduce a previously unexplored paradigm for biocompatible computational platforms and may enable development of ultralow–power consumption hardware-based artificial neural networks capable of interacting with body fluids and biological tissues. American Association for the Advancement of Science 2021-08-18 /pmc/articles/PMC8373129/ /pubmed/34407948 http://dx.doi.org/10.1126/sciadv.abh0693 Text en Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Research Articles Cucchi, Matteo Gruener, Christopher Petrauskas, Lautaro Steiner, Peter Tseng, Hsin Fischer, Axel Penkovsky, Bogdan Matthus, Christian Birkholz, Peter Kleemann, Hans Leo, Karl Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification |
title | Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification |
title_full | Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification |
title_fullStr | Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification |
title_full_unstemmed | Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification |
title_short | Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification |
title_sort | reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8373129/ https://www.ncbi.nlm.nih.gov/pubmed/34407948 http://dx.doi.org/10.1126/sciadv.abh0693 |
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