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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 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.

SUBMITTER: Pham TD 

PROVIDER: S-EPMC7994826 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Time-frequency time-space LSTM for robust classification of physiological signals.

Pham Tuan D TD  

Scientific reports 20210325 1

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, th  ...[more]