Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Predicting change from multivariate time series has relevant applications ranging from the medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient's behavioral state. For example, patients who exhibit a baseline of agitation may be pa...

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Bibliographic Details
Main Authors: Diraco, G. (Author), Leone, A. (Author), Siciliano, P. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Auto encoders
Autoencoders
Behavioral change prediction
Behavioral changes
Bidirectional long-short term memory
Brain
Change prediction
Clinical decision support system
Clinical decision support systems
Convolution
Convolutional neural network
Convolutional neural networks
Decision support systems
Deep feature learning
Forecasting
Handcrafted feature
Handcrafted features
Learned feature
Learned features
Long short-term memory
Multisensory stimulation therapy
Multisensory stimulations
Patient treatment
Physiological models
Physiological signals
Temporal convolutional neural network
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