Data-mining Approaches for the Study of Emotional Responses in Healthy Controls and Traumatic Brain Injured Patients: Comparative Analysis and Validation

Relationship between Heart Rate Variability (HRV) and emotions subjectively reported by 26 healthy subjects during symphonic music listening have been investigated through Data Mining approaches. Most reliable decision models have been successively adopted to forecast an emotional assessment on a group of 16 Traumatic Brain Injured patients during the same type of stimulation, without algorithms retraining. The most performing decisional models have been a Rule Learner (ONE-R) and a Multi Layer Perceptron (M-LP) but, comparing them, the first one was the best in terms of reliability both on validation and independent test phases. Furthermore, ONE-R provides a simple "human-understandable" rule useful to evaluate emotional status of a subjects depending only on one HRV parameter: the normalized unit of Low Frequancy BandPower (nu_LF). Specifically, the classification by HRV nu_LF matched that on reported emotions, with 76.0% of correct classification; tenfold cross-validation: 70.2%; leave-one-out validation: 71.1%. On the other hand, MLP approache has provided an accuracy of 82.69% on healthy controls, but it has decreased to 47.11% and 46.15% on 10folds-cross and leave-one-out validation respectively. Finally, the accuracy has resulted in 51.56% when the MLP model has been applied to the posttraurnatic subjects, while the ONE-R accuracy has resulted in 70.31%. Data mining proved applicable in psychophysiological human research.