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ABSTRACT: False alarms produced by patient monitoring systems in intensive care units (ICU) are a major issue that causes alarm fatigue, waste of human resources, and increased patient risks. While alarms are typically triggered by manually adjusted thresholds, the trend and patterns observed prior to threshold crossing are generally not used by current systems. This study introduces and evaluates a smart alarm detection system for intracranial pressure signal (ICP) that is based on advanced pattern recognition methods. Models are trained in a supervised fashion from a comprehensive dataset of 4791 manually labelled alarm episodes extracted from 108 neurosurgical patients. The comparative analysis provided between spectral regression, kernel spectral regression, and support vector machines indicates the significant improvement of the proposed framework in detecting false ICP alarms in comparison to a threshold-based technique that is conventionally used. Another contribution of this work is to exploit an adaptive discretization to reduce the dimensionality of the input features. The resulting features lead to a decrease of 30% of false ICP alarms without compromising sensitivity.
IEEE transactions on bio-medical engineering 07/2012; · 2.15 Impact Factor
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ABSTRACT: The current gold standard to determine intracranial pressure (ICP) involves an invasive procedure for direct access to the intracranial compartment. The risks associated with this invasive procedure include intracerebral hemorrhage, infection, and discomfort. We previously proposed an innovative data-mining framework of noninvasive ICP (NICP) assessment. The performance of the proposed framework relies on designing a good mapping function. We attempt to achieve performance gain by adopting various linear and nonlinear mapping functions. Our results demonstrate that a nonlinear mapping function based on the kernel spectral regression technique significantly improves the performance of the proposed data-mining framework for NICP assessment in comparison to other linear mapping functions.
IEEE Transactions on Biomedical Engineering 04/2012; · 2.28 Impact Factor
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ABSTRACT: The continuous measurement of intracranial pressure (ICP) is an important and established clinical tool that is used in the management of many neurosurgical disorders such as traumatic brain injury. Only mean ICP information is used currently in the prevailing clinical practice, ignoring the useful information in ICP pulse waveform that can be continuously acquired and is potentially useful for forecasting intracranial and cerebrovascular pathophysiological changes. The present study introduces and validates an algorithm of performing automated analysis of continuous ICP pulse waveform. This algorithm is capable of enhancing ICP signal quality, recognizing nonartifactual ICP pulses, and optimally designating the three well-established subcomponents in an ICP pulse. Validation of the proposed algorithm is done by comparing nonartifactual pulse recognition and peak designation results from a human observer with those from automated analysis based on a large signal database built from 700 h of recordings from 66 neurosurgical patients. An accuracy of 97.84% is achieved in recognizing nonartifactual ICP pulses. An accuracy of 90.17%, 87.56%, and 86.53% was obtained for designating each of the three established ICP subpeaks. These results show that the proposed algorithm can be reliably applied to process continuous ICP recordings from real clinical environment to extract useful morphological features of ICP pulses.
IEEE transactions on bio-medical engineering 12/2008; 56(3):696-705. · 2.15 Impact Factor
