Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Version 1 : Received: 30 March 2022 / Approved: 31 March 2022 / Online: 31 March 2022 (08:38:58 CEST)

A peer-reviewed article of this Preprint also exists.

Diraco, G.; Siciliano, P.; Leone, A. Behavioral Change Prediction from Physiological Signals Using Deep Learned Features. Sensors 2022, 22, 3468. Diraco, G.; Siciliano, P.; Leone, A. Behavioral Change Prediction from Physiological Signals Using Deep Learned Features. Sensors 2022, 22, 3468.

Journal reference: Sensors 2022, 22, 3468
DOI: 10.3390/s22093468

Abstract

Predicting change from multivariate time series has relevant applications ranging from 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 paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

Keywords

behavioral change prediction; learned features; deep feature learning; handcrafted features; bidirectional long-short term memory; autoencoders; temporal convolutional neural network; clinical decision support system; multisensory stimulation therapy; physiological signals.

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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