Predicting Changes in Affective States using Neural Networks
This addresses the problem of low sampling rates and communication barriers in healthcare, but it is incremental as it applies existing methods to a specific domain.
The study tackled predicting changes in patients' affective states by measuring physiological signals, achieving accuracies of 91.88% with neural networks and 89.10% with multiple linear regression.
Knowledge of patients affective state could prove to be crucial for health-care professionals in both diagnosis and treatment, however, this requires patients to report how they feel. In practice the sampling rate of affective states needs to be kept low, in order to ensure that the patients can rest. Furthermore using traditional methods of measuring affective states, is not always possible, e.g. patients can be incapable of verbal communications. In this study we explore the prediction of peoples self-reported affective state by measuring multiple physiological signals. We use different Neural networks (NN) setups and compare with different multiple linear regression (MLR) setups for prediction of changes in affective states. The results showed that NN and MLR predicted the change in affective states with accuracies of 91.88% and 89.10%, respectively.