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Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

1
Department of Biomedical Engineering, University of Connecticut, Storrs CT 06269, USA
2
Naval Submarine Medical Research Laboratory, Groton CT 06340, USA
*
Author to whom correspondence should be addressed.
Behav. Sci. 2019, 9(4), 45; https://doi.org/10.3390/bs9040045
Received: 1 April 2019 / Revised: 19 April 2019 / Accepted: 23 April 2019 / Published: 25 April 2019
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Abstract

Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals. View Full-Text
Keywords: heart rate variability; electrodermal activity; autonomic nervous system; psychomotor vigilance task; working memory; ship search heart rate variability; electrodermal activity; autonomic nervous system; psychomotor vigilance task; working memory; ship search
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Posada-Quintero, H.F.; Bolkhovsky, J.B. Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity. Behav. Sci. 2019, 9, 45.

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