Data mining and the functional relationship between heart rate variability and emotional processing comparative analyses, validation and application

Aims of the study are to 1-classify emotional responses in healthy and conscious brain injured subjects by Data Mining analysis of subjective reports and Heart Rate Variability (HRV), 2-compare different procedures for reliability, and 3-test applicability in patients with disordered consciousness (vegetative state). We measured HRV of 26 healthy and 16 posttraumatic subjects listening music samples selected by emotions they evoke. Each subject was interviewed and the reported emotions were used for identifing a model assessing the most probable emotion by the HRV parameters. Two macro-categories were defined: positive and negative emotions. The study matched a three-phases strategy. First, we applied several classification approaches to healthy subjects evaluating them through suitable validation techniques. Secondly, the best performing classifiers were used to forecast emotions of posttraumatic patients, without retraining. In the 3rd phase we used the most reliable decision model both for validation (1st phase) and independent test (2nd phase) in order to classify the "emotional" response of 9 subjects in vegetative state. One HRV parameter (normalized Low-Frequency Band Power) proved sufficient to forecast a reliable classification. Accuracy was greater than 70% on training, validation and test. Model represents an objective criterion to investigate possible emotional responses also in unconscious patients.