Bayesian tracking of intracranial pressure signal morphology

Abstract

Background The waveform morphology of intracranial pressure (ICP) pulses holds essential informations about intracranial and cerebrovascular pathophysiological variations. Most of current ICP pulse analysis frameworks process each pulse independently and therefore do not exploit the temporal dependency existing between successive pulses. We propose a probabilistic framework that exploits this temporal dependency to track ICP waveform morphology in terms of its three peaks. Material ICP and electrocardiogram (ECG) signals were recorded from a total of 128 patients treated for various intracranial pressure related conditions. Methods The tracking is posed as inference in a graphical model that associates a random variable to the position of each peak. A key contribution is to exploit a nonparametric Bayesian inference algorithm that offers robustness and real time performance. A simple, yet effective learning procedure estimates the statistical, nonlinear, dependencies between the peaks in a nonparametric way using evidence collected from manually annotated pulses. Results Experiments demonstrate the effectiveness of the tracking framework on real ICP pulses and its robustness to occlusion and missing peaks. On artificialy distorted ICP sequences, the average error in latency in comparision with MOCAIP detector was reduced as follows: 11.88–8.09 ms, 11.80–6.90 ms, and 11.76–7.46 ms for the first, second, and third peak, respectively. Conclusion The proposed tracking algorithm sucessfuly increases the temporal resolution of detecting ICP pulse morphological changes from the minute-level to the beat-level.

Noise reduction in intracranial pressure signal using causal shape manifolds

“…2), which still holds pulsatile information, is the “ground truth” that will further be used in our experiments. As it has been shown in previous works [9,12], the average shape of the ICP waveform is related to the ICP elevation. Pulses corresponding to normal ICP tend to exhibits three peaks (in blue), while higher ICP ones generally tend to become unimodal (in red).…”

“…In contrast to previous works [22,9], our method captures the continuously varying characteristics of ICP waveforms. This was accomplished by characterizing the morphology of ICP waveforms via clustering, warping the subspace using continuous valued statistics (DC value and expert annotations), and tracking the progression of waveforms within the proposed I/CST framework.…”

“…There has been significant interest in modeling the morphology of the ICP waveform [4–9]. One of the main findings of those studies is to characterize the relationship between the average ICP value and the signal morphology (up to the beat level).…”

Detection of Intracranial Hypertension using Deep Learning

“…Numerous studies have been conducted suggesting use of machine learning methods and probabilistic frameworks to understand the characteristics in waveform morphology linked to elevated intracranial pressure [8], [9], [10]. A study by colleagues at UCLA demonstrated key relationships between ICP sub-peaks and hypertension [11].…”

Real-Time Analysis of Intracranial Pressure Waveform Morphology

“…In this section, we present a probabilistic framework [24] to track ICP peaks in real time. The tracking is posed as inference in a graphical model that associates a continuous random variable to the position of each of the three peaks, in terms of their latency within the pulse and pressure level.…”