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Regression analysis for peak designation in pulsatile pressure signals.

Scalzo F, Xu P, Asgari S, Bergsneider M, Hu X - Med Biol Eng Comput (2009)

Bottom Line: A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses.The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients.The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, USA. fabien.scalzo@gmail.com

ABSTRACT
Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm.

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Related in: MedlinePlus

Detection of peaks on six different ICP pulses (a), (b), (c), (d), (e), and (f). The ground truth is marked as a black cross and the prediction of the MOCAIP regression algorithm is depicted as a green dot
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Fig6: Detection of peaks on six different ICP pulses (a), (b), (c), (d), (e), and (f). The ground truth is marked as a black cross and the prediction of the MOCAIP regression algorithm is depicted as a green dot

Mentions: Figure 6 illustrates successful detection results on four different pulses. We can observe that the detection is robust given the large shape variability of the ICP signal.Fig. 6


Regression analysis for peak designation in pulsatile pressure signals.

Scalzo F, Xu P, Asgari S, Bergsneider M, Hu X - Med Biol Eng Comput (2009)

Detection of peaks on six different ICP pulses (a), (b), (c), (d), (e), and (f). The ground truth is marked as a black cross and the prediction of the MOCAIP regression algorithm is depicted as a green dot
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2734262&req=5

Fig6: Detection of peaks on six different ICP pulses (a), (b), (c), (d), (e), and (f). The ground truth is marked as a black cross and the prediction of the MOCAIP regression algorithm is depicted as a green dot
Mentions: Figure 6 illustrates successful detection results on four different pulses. We can observe that the detection is robust given the large shape variability of the ICP signal.Fig. 6

Bottom Line: A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses.The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients.The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, USA. fabien.scalzo@gmail.com

ABSTRACT
Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm.

Show MeSH
Related in: MedlinePlus