Signal Processing Using Mutual Information

Hudson, J.E.

doi:10.1109/sp-m.2006.248712

Cited by 20 publications

(29 citation statements)

References 13 publications

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“…As shown in figure 1 the DV partitioning algorithm allows us to partition the state-space associated with a multivariate time series into varying size bins (or hypercubes) for the purpose of density estimation (Hudson 2006). The DV partitioning was previously shown to be effective in calculating transfer entropy (Lee et al 2012; Nemati et al 2013), a statistical measure of the amount of directed entropy transfer between two random processes, and it was shown to have lower computational cost than the competing methods.…”

Section: Methodsmentioning

confidence: 99%

Multiscale network representation of physiological time series for early prediction of sepsis

Shashikumar

Clifford

et al. 2017

Physiol. Meas.

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Sepsis, a dysregulated immune-mediated host response to infection, is the leading cause of morbidity and mortality in critically ill patients. Indices of heart rate variability and complexity (such as entropy) have been proposed as surrogate markers of neuro-immune system dysregulation with diseases such as sepsis. However, these indices only provide an average, one dimensional description of complex neuro-physiological interactions. We propose a novel multiscale network construction and analysis method for multivariate physiological time series, and demonstrate its utility for early prediction of sepsis. We show that features derived from a multiscale heart rate and blood pressure time series network provide approximately 20% improvement in the area under the receiver operating characteristic (AUROC) for four hours ahead prediction of sepsis over traditional indices of heart rate entropy (0.78 versus 0.66). Our results indicate that this improvement is attributable to both the improved network construction method proposed here, as well as the information embedded in the higher order interaction of heart rate and blood pressure time series dynamics. Our final model, which included the most commonly available clinical measurements in patients’ Electronic Medical Records, multiscale entropy features as well as the proposed network-based features, achieved an AUROC of 0.80. Prediction of the onset of sepsis prior to clinical recognition will allow for meaningful earlier interventions (e.g., antibiotic and fluid administration), which have the potential to decrease sepsis-related morbidity, mortality and healthcare costs.

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Section: Methodsmentioning

confidence: 99%

Multiscale network representation of physiological time series for early prediction of sepsis

Shashikumar

Clifford

et al. 2017

Physiol. Meas.

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“…To compare the MI across components, the normalized MI was calculated utilizing the entropy of s i (Hudson 2006;Liu et al, 2012):…”

Section: Mutual Informationmentioning

confidence: 99%

“…It is based on the combination of independent component analysis (ICA; Bell and Sejnowski, 1995) and mutual information theory (MI; Hudson, 2006). Thereby, ICA is initially used to decompose data from the sensor level to the component level.…”

Section: Introductionmentioning

confidence: 99%

Rejecting deep brain stimulation artefacts from MEG data using ICA and mutual information

Abbasi

Hirschmann

Schmitz

et al. 2016

Journal of Neuroscience Methods

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“…To compare the mutual information across components, the normalized mutual information (MI) was calculated utilizing the entropy of s t (Hudson, 2006;Liu et al, 2012a):…”

Section: Mutual Informationmentioning

confidence: 99%

Time-frequency analysis of resting state and evoked EEG data recorded at higher magnetic fields up to 9.4T

Abbasi

Dammers

Warbrick

et al. 2015

Journal of Neuroscience Methods

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Signal Processing Using Mutual Information

Cited by 20 publications

References 13 publications

Multiscale network representation of physiological time series for early prediction of sepsis

Multiscale network representation of physiological time series for early prediction of sepsis

Rejecting deep brain stimulation artefacts from MEG data using ICA and mutual information

Time-frequency analysis of resting state and evoked EEG data recorded at higher magnetic fields up to 9.4T

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