Scientific Reports (Mar 2021)

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

  • Tuan D. Pham

DOI
https://doi.org/10.1038/s41598-021-86432-7
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 11

Abstract

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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.