Time–frequency time–space LSTM for robust classification of physiological signals

Abstract Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties...

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Autor principal:	Tuan D. Pham
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/388b4a827ec147799ddb5b88df51f304
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spelling	oai:doaj.org-article:388b4a827ec147799ddb5b88df51f3042021-12-02T11:45:01ZTime–frequency time–space LSTM for robust classification of physiological signals10.1038/s41598-021-86432-72045-2322https://doaj.org/article/388b4a827ec147799ddb5b88df51f3042021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86432-7https://doaj.org/toc/2045-2322Abstract Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.Tuan D. PhamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tuan D. Pham Time–frequency time–space LSTM for robust classification of physiological signals
description	Abstract Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.
format	article
author	Tuan D. Pham
author_facet	Tuan D. Pham
author_sort	Tuan D. Pham
title	Time–frequency time–space LSTM for robust classification of physiological signals
title_short	Time–frequency time–space LSTM for robust classification of physiological signals
title_full	Time–frequency time–space LSTM for robust classification of physiological signals
title_fullStr	Time–frequency time–space LSTM for robust classification of physiological signals
title_full_unstemmed	Time–frequency time–space LSTM for robust classification of physiological signals
title_sort	time–frequency time–space lstm for robust classification of physiological signals
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/388b4a827ec147799ddb5b88df51f304
work_keys_str_mv	AT tuandpham timefrequencytimespacelstmforrobustclassificationofphysiologicalsignals
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Time–frequency time–space LSTM for robust classification of physiological signals

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