Real-time algorithm for changes detection in depth of anesthesia signals
This paper presents a real-time algorithm for changes detection in depth of anesthesia signals. A PageHinkley test (PHT) with a forgetting mechanism (PHT-FM) was developed. The samples are weighted according to their age so that more importance is given to recent samples. This enables the detection of the changes with less time delay than if no forgetting factor was used. The performance of the PHT-FM was evaluated in a two-fold approach. First, the algorithm was run offline in depth of anesthesia (DoA) signals previously collected during general anesthesia, allowing the adjustment of the forgetting mechanism. Second, the PHT-FM was embedded in a real-time software and its performance was validated online in the surgery room. This was performed by asking the clinician to classify in real-time the changes as true positives, false positives or false negatives. The results show that 69% of the changes were classified as true positives, 26% as false positives, and 5% as false negatives. The true positives were also synchronized with changes in the hypnotic or analgesic rates made by the clinician. The contribution of this work has a high impact in the clinical practice since the PHT-FM alerts the clinician for changes in the anesthetic state of the patient, allowing a more prompt action. The results encourage the inclusion of the proposed PHT-FM in a real-time decision support system for routine use in the clinical practice.
adaptive systems data flow analysis change detection algorithms dynamic behavior