Waveform descriptor for pulse onset detection of intracranial pressure signal

Abstract

We present an algorithm to identify the onset of intracranial pressure (ICP) pulses. The algorithm creates a waveform descriptor to extract the feature of each local minimum of the waveform and then identifies the onset by comparing the feature with a customized template. The waveform descriptor is derived by transforming the vectors connecting a given point and the local waveform samples around it into log-polar coordinates and ranking them into uniform bins. Using an ICP dataset consisting of 40933 normal beats and 306 segments of artifacts and noise, we investigated the performance of our algorithm (waveform descriptor, WD), global minimum within a sliding window (GM) and two other algorithms originally proposed for arterial blood pressure (ABP) signal (slope sum function, SSF and pulse waveform delineator, PUD). As a result, all the four algorithms showed good performance and WD showed overall better one. At a tolerance level of 30 ms (i.e., the predicted onset and ground truth were considered as correctly matched if the distance between the two was equal or less than 30 ms), WD achieved a sensitivity of 0.9723 and PPV of 0.9475, GM achieved a sensitivity of 0.9226 and PPV of 0.8968, PUD achieved a sensitivity of 0.9599 and PPV of 0.9327 and SSF, a sensitivity of 0.9720 and PPV of 0.9136. The evaluation indicates that the algorithms are effective for identifying the onset of ICP pulses.