Identification of dental pain sensation based on cardiorespiratory signals

The aim of this study is to investigate the feasibility of the detection of brief periods of pain sensation based on cardiorespiratory signals during dental pain triggers. Twenty patients underwent dental treatment and reported their pain events by pressing a push button while ECG, PPG, and thoracic effort signals were simultaneously recorded. Potential pain-indicating features were calculated from the physiological data (sample length of 6 s) and were used for supervised learning of a Random forest pain detector. The best feature combination was determined by Feature forward selection. The best feature combination comprises nine feature groups consisting of four respiratory and five cardiac related groups. The final algorithm achieved a sensitivity of 87% and a specificity of 63% with an AUC of 0.828. Using supervised learning it is possible to train an algorithm to differentiate between short time intervals of pain and no pain solely based on cardiorespiratory signals. An on-site and real-time detection and rating of pain sensations would allow a precise, individuum- and treatment-tailored administration of local anesthesia. Severe phases of pain could be paused or avoided, this would allow more comfortable treatment and yield better patient compliance.