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Robust peak recognition in intracranial pressure signals.

Scalzo F, Asgari S, Kim S, Bergsneider M, Hu X - Biomed Eng Online (2010)

Bottom Line: While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurosurgery, Geffen School of Medicine, Neural Systems and Dynamic Lab, University of California, Los Angeles, CA, USA. fscalzo@mednet.ucla.edu

ABSTRACT

Background: The waveform morphology of intracranial pressure pulses (ICP) is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.

Methods: This paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse). Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.

Results: Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.

Conclusion: The proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.

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Illustration of two ICP pulses (the actual position of the peak is depicted in green, MOCAIP prediction in black). On the left, an ICP dominant pulse is correctly annotated with the position of the three peaks. On the right, the automatic annotation failed to correctly recognized the third peak because of the uncommon shape of the pulse. This pulse is considered as a challenging one in our study.
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Figure 1: Illustration of two ICP pulses (the actual position of the peak is depicted in green, MOCAIP prediction in black). On the left, an ICP dominant pulse is correctly annotated with the position of the three peaks. On the right, the automatic annotation failed to correctly recognized the third peak because of the uncommon shape of the pulse. This pulse is considered as a challenging one in our study.

Mentions: The extraction of morphological features is essential to monitor and to understand ICP in an automatic fashion with the ultimate goal of improving the treatment of pathophysiological intracranial and cerebrovascular conditions. Although ICP pulses are typically triphasic [8] (i.e. three peaks), their shape can exhibit irregular variations such that some peaks may be missing. The recognition of these top peaks is a challenging task that has recently drawn special attention from different research groups. Several algorithms have been developed to detect the first peak [12], and to recognize the three peaks of ICP pulses [13-17]. Existing methods can be divided into two categories depending if they work offline, like Morphologram [14], or online, like MOCAIP [13,15] (Morphological Clustering and Analysis of ICP Pulse). These techniques offer a satisfactory accuracy to recognize the peaks in general cases. However our recent observations show that their performance deteriorates significantly when the pulses exhibit abnormalities or are simply more challenging (a pulse is considered to be challenging if any of its peaks fails to be correctly designated by the baseline MOCAIP algorithm [15], see Figure 1). Such ICP pulses are of particular interest because we suspect that they might hold essential predictive information about the patient condition.


Robust peak recognition in intracranial pressure signals.

Scalzo F, Asgari S, Kim S, Bergsneider M, Hu X - Biomed Eng Online (2010)

Illustration of two ICP pulses (the actual position of the peak is depicted in green, MOCAIP prediction in black). On the left, an ICP dominant pulse is correctly annotated with the position of the three peaks. On the right, the automatic annotation failed to correctly recognized the third peak because of the uncommon shape of the pulse. This pulse is considered as a challenging one in our study.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2984490&req=5

Figure 1: Illustration of two ICP pulses (the actual position of the peak is depicted in green, MOCAIP prediction in black). On the left, an ICP dominant pulse is correctly annotated with the position of the three peaks. On the right, the automatic annotation failed to correctly recognized the third peak because of the uncommon shape of the pulse. This pulse is considered as a challenging one in our study.
Mentions: The extraction of morphological features is essential to monitor and to understand ICP in an automatic fashion with the ultimate goal of improving the treatment of pathophysiological intracranial and cerebrovascular conditions. Although ICP pulses are typically triphasic [8] (i.e. three peaks), their shape can exhibit irregular variations such that some peaks may be missing. The recognition of these top peaks is a challenging task that has recently drawn special attention from different research groups. Several algorithms have been developed to detect the first peak [12], and to recognize the three peaks of ICP pulses [13-17]. Existing methods can be divided into two categories depending if they work offline, like Morphologram [14], or online, like MOCAIP [13,15] (Morphological Clustering and Analysis of ICP Pulse). These techniques offer a satisfactory accuracy to recognize the peaks in general cases. However our recent observations show that their performance deteriorates significantly when the pulses exhibit abnormalities or are simply more challenging (a pulse is considered to be challenging if any of its peaks fails to be correctly designated by the baseline MOCAIP algorithm [15], see Figure 1). Such ICP pulses are of particular interest because we suspect that they might hold essential predictive information about the patient condition.

Bottom Line: While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurosurgery, Geffen School of Medicine, Neural Systems and Dynamic Lab, University of California, Los Angeles, CA, USA. fscalzo@mednet.ucla.edu

ABSTRACT

Background: The waveform morphology of intracranial pressure pulses (ICP) is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.

Methods: This paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse). Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.

Results: Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.

Conclusion: The proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.

Show MeSH