A Case-Based Reasoning System for the Diagnosis of Individual Sensitivity to Stress in Psychophysiology

Stress is an increasing problem in our present world. Especially negative stress could cause serious health problems if it remains undiagnosed/misdiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, ECG, finger temperature and breathing rate during a number of exercises to diagnose stressrelated disorders. One of the physiological parameters for quantifying stress levels is the finger temperature measurement which helps the clinicians in diagnosis and treatment of stress. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are only few experts who are able to diagnose and predict stress-related problems. A system that can help the clinician in diagnosing stress is important, but the large individual variations make it difficult to build such a system. This research work has investigated several artificial Intelligence techniques for the purpose of developing an intelligent, integrated sensor system for establishing diagnosis and treatment plan in the psychophysiological domain. To diagnose individual sensitivity to stress, case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning process. The validation of the approach is based on close collaboration with experts and measurements from twenty four persons used as reference. 39 time series from these 24 persons have been used to evaluate the approach (in terms of the matching algorithms) and an expert has ranked and estimated the similarity. The result shows that the system reaches a level of performance close to an expert. The proposed system could be used as an expert for a less experienced clinician or as a second option for an experienced clinician to their decision making process in stress diagnosis.