An automated algorithm for analyzing continuous intracranial pressure pulse waveforms achieved 97.84% accuracy in recognizing nonartifactual pulses compared to a human observer.
Can an automated algorithm accurately recognize nonartifactual ICP pulses and designate subpeaks compared to a human observer in neurosurgical patients?
An automated algorithm can reliably process continuous ICP recordings to extract morphological features of ICP pulses with high accuracy compared to human observers.
The continuous measurement of intracranial pressure (ICP) is an important and established clinical tool that is used in the management of many neurosurgical disorders such as traumatic brain injury. Only mean ICP information is used currently in the prevailing clinical practice, ignoring the useful information in ICP pulse waveform that can be continuously acquired and is potentially useful for forecasting intracranial and cerebrovascular pathophysiological changes. The present study introduces and validates an algorithm of performing automated analysis of continuous ICP pulse waveform. This algorithm is capable of enhancing ICP signal quality, recognizing nonartifactual ICP pulses, and optimally designating the three well-established subcomponents in an ICP pulse. Validation of the proposed algorithm is done by comparing nonartifactual pulse recognition and peak designation results from a human observer with those from automated analysis based on a large signal database built from 700 h of recordings from 66 neurosurgical patients. An accuracy of 97.84% is achieved in recognizing nonartifactual ICP pulses. An accuracy of 90.17%, 87.56%, and 86.53% was obtained for designating each of the three established ICP subpeaks. These results show that the proposed algorithm can be reliably applied to process continuous ICP recordings from real clinical environment to extract useful morphological features of ICP pulses.
Hu et al. (Wed,) conducted a other in Neurosurgical disorders (n=66). Automated analysis algorithm of continuous ICP pulse waveform vs. Human observer was evaluated on Accuracy in recognizing nonartifactual ICP pulses and designating subpeaks. An automated algorithm for analyzing continuous intracranial pressure pulse waveforms achieved 97.84% accuracy in recognizing nonartifactual pulses compared to a human observer.