Physiological Signals Fusion Oriented to Diagnosis - A Review

2018 ◽  
pp. 1-15 ◽  
Author(s):  
Y. F. Uribe ◽  
K. C. Alvarez-Uribe ◽  
D. H. Peluffo-Ordoñez ◽  
M. A. Becerra
Keyword(s):  
Physiological Signals

Emotion Monitoring Using Remote Measurement for Physiological Signals by Camera

2016 ◽  
Vol 2016 (1) ◽  
pp. 147-151 ◽  
Author(s):  
Genki Okada ◽  
Taku Yonezawa ◽  
Kouki Kurita ◽  
Norimichi Tsumura
Keyword(s):  
Physiological Signals ◽  
Remote Measurement

Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network

2021 ◽  
Author(s):  
Jun Liao ◽  
Dandan Liu ◽  
Guoxin Su ◽  
Li Liu
Keyword(s):  
Physiological Signals ◽  
Lstm Network

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated 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.

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