The effects of a sound-modified environment on physiological variables in premature infants in neonatal intensive care

This study investigated the effects of sound reduction on physiological variables in premature infants in neonatal intensive care. Ten premature infants born between 27 and 36 weeks gestation wore a specially designed earmuff cap for a 45-minute rest period. Heart rate, respiration rate, oxygen saturation level and behavioral state were measured and compared to a similar 45-minute control period without the earmuff cap. Subjects showed a significant decrease (p =.050) in mean respiration rate, and a significant increase (p $<$.02) in mean oxygen saturation level with the earmuff cap on. No significant differences were found in heart rate between the experimental condition and the control condition. Behavioral state was documented only as a potentially confounding variable, however a significant decrease (p $<$.05) in the time spent awake and a significant increase (p $<$.05) in the time spent in quiet sleep rather than active sleep occurred with the earmuff cap on. Findings suggest that noise reduction may be a viable means of increasing respiratory efficiency and the amount and quality of sleep in premature infants in neonatal intensive care.

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^