OBJECTIVE: An increase in intracranial pressure (ICP) is frequently observed in patients with severe traumatic brain injury (TBI). The information derived from the observation of temporal changes in the mean ICP is insufficient for assessment of the compensatory reserve of the injured brain. This assessment can be achieved via continuous morphological analysis of the pulse waveform of the ICP. METHODS: Continuous arterial blood pressure (ABP) and ICP recordings from 292 TBI patients were analyzed. The algorithm extracted morphological landmarks (peaks, troughs, and flats) from the ICP. Among the extracted peaks, P1, P2, and P3 were assigned through peak clustering. The performance of the proposed method was validated through a comparison of the algorithm-defined peaks and those manually identified by experienced observers. RESULTS: The proposed algorithm successfully identified the three distinguishing peaks of the ICP with satisfactory accuracy (95.3%, 87.8%, and 87.5% for P1, P2, and P3, respectively), even from minimally filtered raw signals. CONCLUSION: The algorithm extracted the morphological features from both ABP and ICP recordings with high accuracy. SIGNIFICANCE: The ABP and ICP pulse waveforms can be simultaneously analyzed in real time using the proposed algorithm. The morphological features from these signals may aid the continuous care of patients with TBI.
OBJECTIVE: An increase in intracranial pressure (ICP) is frequently observed in patients with severe traumatic brain injury (TBI). The information derived from the observation of temporal changes in the mean ICP is insufficient for assessment of the compensatory reserve of the injured brain. This assessment can be achieved via continuous morphological analysis of the pulse waveform of the ICP. METHODS: Continuous arterial blood pressure (ABP) and ICP recordings from 292 TBI patients were analyzed. The algorithm extracted morphological landmarks (peaks, troughs, and flats) from the ICP. Among the extracted peaks, P1, P2, and P3 were assigned through peak clustering. The performance of the proposed method was validated through a comparison of the algorithm-defined peaks and those manually identified by experienced observers. RESULTS: The proposed algorithm successfully identified the three distinguishing peaks of the ICP with satisfactory accuracy (95.3%, 87.8%, and 87.5% for P1, P2, and P3, respectively), even from minimally filtered raw signals. CONCLUSION: The algorithm extracted the morphological features from both ABP and ICP recordings with high accuracy. SIGNIFICANCE: The ABP and ICP pulse waveforms can be simultaneously analyzed in real time using the proposed algorithm. The morphological features from these signals may aid the continuous care of patients with TBI.
Authors: Vidhya V; Anjan Gudigar; U Raghavendra; Ajay Hegde; Girish R Menon; Filippo Molinari; Edward J Ciaccio; U Rajendra Acharya Journal: Int J Environ Res Public Health Date: 2021-06-16 Impact factor: 3.390