Thesis - Instituto de Telecomunicações

70 CHAPTER 4. SIGNAL PROCESSING AND FEATURE EXTRACTION4.2.2 Electrodermal ModelsVisual Event Detection The initial methods used by the researchers for the interpretationof the EDA signal were based on visual detection of skin conductance responses, withthe observer manually annotating the time instants of the start of the events.In [98] the authors apply a preprocessing step prior to visual inspection, normalizing thesignal according to the recommendations of the EDA committee [72] and then smoothingthe speed of change, i.e., the second order derivative, to reduce signal noise. After thisprocessing step, the extracted information shows the existence of an event after a particularstimulus.The sympathetic skin response signal is also evaluated following a qualitative model in[134, 233]. The signal is visually scanned to find SCR events; these events are classified asp-waves, if the first peak of the wave is positive, or as n-waves, if it is negative.Visual-based SCR event detection, that we call the qualitative approach, is strongly conditionedby the experience of the human scorer, and the modeling problems listed above aredifficult to solve via visual inspection. Furthermore, the signal obtained in the sympatheticskin response has the peaks and valleys substantially altered by the filtering procedure andas such, different conclusions can be reached by using different filtering conditions in thepre-processing step. Therefore, without a common well-defined filtering setup, no reliableconclusions can be obtained. There is also no physiologic basis for the separation betweenthe two types of waves referred to above.Simple Model The first computational model applied to the EDA signal was based onthe identification of the valleys and peaks of the signal [25, 211]. The model assumes theexistence of two zones in a SCR: a decreasing part before t α , the activation instant, and acrescent zone between t α and t β (the maximum instant). The identification of these zones,and of these transition instants, is based on the computation of signal derivatives andcorresponding zero crossings, as summarized in expressions 4.16 to 4.20, that are processedin sequence. The pre-event zone (t < t α ) is characterized by negative first derivative values(equation 4.16). The activation instant (t α ) corresponds to a null signal first order derivativeand positive second order derivative (eq. 4.17). Along the rise zone, that occurs betweent α and t β - the local maximum amplitude instant, the signal first derivative is positive