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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.

Show MeSH
Signal L made of two Gaussian peaks with different standard deviations. Its first Lx and second Lxx derivatives are particularly usefull to discriminate peaks because their amplitude depends on the peak width but remains invariant to any global shift in elevation of the signal.
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Figure 4: Signal L made of two Gaussian peaks with different standard deviations. Its first Lx and second Lxx derivatives are particularly usefull to discriminate peaks because their amplitude depends on the peak width but remains invariant to any global shift in elevation of the signal.

Mentions: Previous MOCAIP-based studies [15,18] exploited the dominant pulses directly as input to peak recognition techniques. In signal processing, it is common to derive features that emphasize different properties of the signal. For example, the first derivative measures the changing rate of the signal with respect to time. As illustrated in Figure 4, it is particularly interesting in our case because for a similar amplitude, a wide peak, and a narrow peak will lead to different derivative values. Therefore, features extracted from the ICP signal derivative provide additional morphological characteristics that should help to discriminate between ICP peaks. One advantage of using these features is that they are invariant to a shift of the signal elevation. Note that the framework is not restricted to these features, any other features could in principle be exploited. In our experiments, we will evaluate the impact of using the first Lx and second Lxx derivatives, as well as the curvature K extracted from the ICP signal within MOCAIP++ framework.


Robust peak recognition in intracranial pressure signals.

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

Signal L made of two Gaussian peaks with different standard deviations. Its first Lx and second Lxx derivatives are particularly usefull to discriminate peaks because their amplitude depends on the peak width but remains invariant to any global shift in elevation of the signal.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Signal L made of two Gaussian peaks with different standard deviations. Its first Lx and second Lxx derivatives are particularly usefull to discriminate peaks because their amplitude depends on the peak width but remains invariant to any global shift in elevation of the signal.
Mentions: Previous MOCAIP-based studies [15,18] exploited the dominant pulses directly as input to peak recognition techniques. In signal processing, it is common to derive features that emphasize different properties of the signal. For example, the first derivative measures the changing rate of the signal with respect to time. As illustrated in Figure 4, it is particularly interesting in our case because for a similar amplitude, a wide peak, and a narrow peak will lead to different derivative values. Therefore, features extracted from the ICP signal derivative provide additional morphological characteristics that should help to discriminate between ICP peaks. One advantage of using these features is that they are invariant to a shift of the signal elevation. Note that the framework is not restricted to these features, any other features could in principle be exploited. In our experiments, we will evaluate the impact of using the first Lx and second Lxx derivatives, as well as the curvature K extracted from the ICP signal within MOCAIP++ framework.

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