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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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Diagram showing the different modules in MOCAIP++ framework.
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Figure 3: Diagram showing the different modules in MOCAIP++ framework.

Mentions: MOCAIP++ generalizes its predecessors in two ways. First, it proposes a unifying view such that different peak recognition techniques can be integrated within the framework. Second, an additional processing step allows to exploit ICP features regardless the peak recognition method that is used. Similarly to MOCAIP, a pulse extraction technique (Section 2.2.1) first process the ICP signal to extract a reliable dominant pulse from which peak candidates are located at curves inflections (Section 2.2.2). Then, MOCAIP++ extracts different ICP features from the dominant pulse (such as curvature, first, and second derivative) (Section 2.2.3). The peak recognition module (Section 2.2.4) exploits the peak candidates and the features to recognize the peaks within the pulse. Finally, various statistics are estimated using the latency of these peaks and their ICP elevation (additional details can be found in the original papers [15,18]). The core of the algorithm is illustrated in Figure 3 and its major components are described in the next subsections.


Robust peak recognition in intracranial pressure signals.

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

Diagram showing the different modules in MOCAIP++ framework.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Diagram showing the different modules in MOCAIP++ framework.
Mentions: MOCAIP++ generalizes its predecessors in two ways. First, it proposes a unifying view such that different peak recognition techniques can be integrated within the framework. Second, an additional processing step allows to exploit ICP features regardless the peak recognition method that is used. Similarly to MOCAIP, a pulse extraction technique (Section 2.2.1) first process the ICP signal to extract a reliable dominant pulse from which peak candidates are located at curves inflections (Section 2.2.2). Then, MOCAIP++ extracts different ICP features from the dominant pulse (such as curvature, first, and second derivative) (Section 2.2.3). The peak recognition module (Section 2.2.4) exploits the peak candidates and the features to recognize the peaks within the pulse. Finally, various statistics are estimated using the latency of these peaks and their ICP elevation (additional details can be found in the original papers [15,18]). The core of the algorithm is illustrated in Figure 3 and its major components are described in the next subsections.

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