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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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Average recognition accuracy (Eq. 21) after a five-fold cross-validation for MOCAIP-based peak recognition methods improved with the use of the first derivative Lx of the ICP.
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Figure 7: Average recognition accuracy (Eq. 21) after a five-fold cross-validation for MOCAIP-based peak recognition methods improved with the use of the first derivative Lx of the ICP.

Mentions: When combined with derivative-based features, GMM, and KSR methods exhibit a similar ranking of improvement; first derivative offers the largest effect on accuracy, while curvature and second derivatives generally have less significant improvement. With the use of the first derivative (see Figure 7), GMM method improves from 70.47% ± 2.64 to 77.14% ± 1.85, while KSR only shows a marginal improvement from 88.78% ± 2.35 to 89.36% ± 2.51. We have also noticed in additional experiments that combining different features, such as Lx+Lxx, does not improve the performance obtained by using only the first derivative Lx of the ICP signal. These results demonstrate that the use of the first derivative within MOCAIP++ improves the recognition accuracy of the three peak recognition methods we have integrated. It can also be pointed out that the accuracy reached by SR + Lx is very close to KSR + Lx. Considering the previous remarks about the execution time and the storage of training samples for the kernel computation required for KSR, the use of SR combined with the first derivate seems to provide the right tradeoff between speed and accuracy for peak recognition on challenging ICP pulses.


Robust peak recognition in intracranial pressure signals.

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

Average recognition accuracy (Eq. 21) after a five-fold cross-validation for MOCAIP-based peak recognition methods improved with the use of the first derivative Lx of the ICP.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Average recognition accuracy (Eq. 21) after a five-fold cross-validation for MOCAIP-based peak recognition methods improved with the use of the first derivative Lx of the ICP.
Mentions: When combined with derivative-based features, GMM, and KSR methods exhibit a similar ranking of improvement; first derivative offers the largest effect on accuracy, while curvature and second derivatives generally have less significant improvement. With the use of the first derivative (see Figure 7), GMM method improves from 70.47% ± 2.64 to 77.14% ± 1.85, while KSR only shows a marginal improvement from 88.78% ± 2.35 to 89.36% ± 2.51. We have also noticed in additional experiments that combining different features, such as Lx+Lxx, does not improve the performance obtained by using only the first derivative Lx of the ICP signal. These results demonstrate that the use of the first derivative within MOCAIP++ improves the recognition accuracy of the three peak recognition methods we have integrated. It can also be pointed out that the accuracy reached by SR + Lx is very close to KSR + Lx. Considering the previous remarks about the execution time and the storage of training samples for the kernel computation required for KSR, the use of SR combined with the first derivate seems to provide the right tradeoff between speed and accuracy for peak recognition on challenging ICP pulses.

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