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

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 s...

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Bibliographic Details
Published in:Scientific reports 2021-03, Vol.11 (1), p.6936-6936, Article 6936
Main Author: Pham, Tuan D.
Format: Article
Language:English
Subjects:
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639/705
692/699
Classification
Clinical trials
Electrocardiography
Gait Analysis
Humanities and Social Sciences
Humans
Long short-term memory
multidisciplinary
Neural networks
Neural Networks, Computer
Parkinson Disease - physiopathology
Physiology
Physiology - methods
Science
Science (multidisciplinary)
Software
Time series
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-86432-7