Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT–BIH databases

Carrara, Marta; Carozzi, Luca; Moss, Travis J.; Pasquale, Marco De; Cerutti, S.; Lake, Douglas E.; Moorman, J. Randall; Ferrario, Manuela

doi:10.1016/j.jelectrocard.2015.08.002

Cited by 18 publications

(14 citation statements)

References 21 publications

(31 reference statements)

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“…We captured these dynamics in the time, frequency, and non-linear dynamical domains. [ 14 , 15 , 17 , 18 , 44 ] We see many opportunities to apply these and other mathematical approaches to large, well-annotated clinical data sets to develop sophisticated and useful monitoring that use all available data.…”

Section: Discussionmentioning

confidence: 99%

Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study

et al. 2017

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BackgroundCharted vital signs and laboratory results represent intermittent samples of a patient’s dynamic physiologic state and have been used to calculate early warning scores to identify patients at risk of clinical deterioration. We hypothesized that the addition of cardiorespiratory dynamics measured from continuous electrocardiography (ECG) monitoring to intermittently sampled data improves the predictive validity of models trained to detect clinical deterioration prior to intensive care unit (ICU) transfer or unanticipated death.Methods and findingsWe analyzed 63 patient-years of ECG data from 8,105 acute care patient admissions at a tertiary care academic medical center. We developed models to predict deterioration resulting in ICU transfer or unanticipated death within the next 24 hours using either vital signs, laboratory results, or cardiorespiratory dynamics from continuous ECG monitoring and also evaluated models using all available data sources. We calculated the predictive validity (C-statistic), the net reclassification improvement, and the probability of achieving the difference in likelihood ratio χ2 for the additional degrees of freedom. The primary outcome occurred 755 times in 586 admissions (7%). We analyzed 395 clinical deteriorations with continuous ECG data in the 24 hours prior to an event. Using only continuous ECG measures resulted in a C-statistic of 0.65, similar to models using only laboratory results and vital signs (0.63 and 0.69 respectively). Addition of continuous ECG measures to models using conventional measurements improved the C-statistic by 0.01 and 0.07; a model integrating all data sources had a C-statistic of 0.73 with categorical net reclassification improvement of 0.09 for a change of 1 decile in risk. The difference in likelihood ratio χ2 between integrated models with and without cardiorespiratory dynamics was 2158 (p value: <0.001).ConclusionsCardiorespiratory dynamics from continuous ECG monitoring detect clinical deterioration in acute care patients and improve performance of conventional models that use only laboratory results and vital signs.

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Section: Discussionmentioning

confidence: 99%

Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study

et al. 2017

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“…In 74 patient-years of ECG recording from 8,356 consecutive ICU patients, we examined the prevalence, incidence, and burden of AF (22, 23). We tested the hypothesis that new-onset AF, recognized or not, is associated with outcomes such as ICU length of stay (LOS), hospital mortality, and survival after hospital discharge.…”

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New-Onset Atrial Fibrillation in the Critically Ill*

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Enfield

et al. 2017

Critical Care Medicine

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“…Although the RRI time series approach is frequently used, widely available and capable of providing adequate AF detection in a number of cases, entropy-based approaches have also been used independently to detect AF (Lake and Moorman, 2010 ; Carrara et al, 2015a , b ). Sample Entropy (SampEn) algorithms have been used to detect the probability that runs of AF will match with others within the time series (Richman and Moorman, 2000 ; Lake and Moorman, 2010 ) (Figure 4 ).…”

Section: Current Studies Using Information Theoretic Approaches In Atmentioning

confidence: 99%

Information Theory and Atrial Fibrillation (AF): A Review

et al. 2018

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Atrial Fibrillation (AF) is the most common cardiac rhythm disorder seen in hospitals and in general practice, accounting for up to a third of arrhythmia related hospitalizations. Unfortunately, AF treatment is in practice complicated by the lack of understanding of the fundamental mechanisms underlying the arrhythmia, which makes detection of effective ablation targets particularly difficult. Various approaches to AF mapping have been explored in the hopes of better pinpointing these effective targets, such as Dominant Frequency (DF) analysis, complex fractionated electrograms (CFAE) and unipolar reconstruction (FIRM), but many of these methods have produced conflicting results or require further investigation. Exploration of AF using information theoretic-based approaches may have the potential to provide new insights into the complex system dynamics of AF, whilst also providing the benefit of being less reliant on empirically derived definitions in comparison to alternate mapping approaches. This work provides an overview of information theory and reviews its applications in AF analysis, with particular focus on AF mapping. The works discussed in this review demonstrate how understanding AF from a signal property perspective can provide new insights into the arrhythmic phenomena, which may have valuable clinical implications for AF mapping and ablation in the future.

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Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT–BIH databases

Cited by 18 publications

References 21 publications

Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study

Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study

New-Onset Atrial Fibrillation in the Critically Ill*

Information Theory and Atrial Fibrillation (AF): A Review

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