Multimodal Algorithms for the Classification of Circulation States During Out-of-Hospital Cardiac Arrest

Elola, Andoni; Aramendi, Elisabete; Irusta, Unai; Berve, Per Olav

doi:10.1109/tbme.2020.3030216

Cited by 17 publications

(29 citation statements)

References 51 publications

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“…An 8-level SWT decomposition was used with a Daubechies-4 mother wavelet and soft thresholding. Detail coefficients

were used to reconstruct the denoised ECG, which corresponds to an analysis band of

, a typical band for ECG analysis in OHCA [ 23 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The time-varying Fourier coefficients,

and

, were estimated using a Kalman smoother [ 23 ]. The Kalman observation and state vectors are then [ 23 , 34 ]:

…”

Section: Methodsmentioning

confidence: 99%

“…The time-varying Fourier coefficients were assumed to be Gaussian processes with update equations [ 23 , 34 ]:

where

and

is a zero-mean Gaussian process with

the standard deviation. The update equations are thus:

where

and

is the identity matrix of dimension

.…”

Section: Methodsmentioning

confidence: 99%

“…The time-varying Fourier coefficients were assumed to be Gaussian processes with update equations [23,34]:…”

Section: Ti Processing and Icc Extractionmentioning

confidence: 99%

“…Additionally, the thoracic impedance (TI) measured through the defibrillation pads reflects changes in tissue density and fluid content in the thoracic region and thus presents a small, but discernible component associated with blood flow [ 21 ]. TI has been successfully used to discriminate PEA from rhythms associated with ROSC, by extracting the impedance circulation component (ICC), which reflects blood flow during ROSC [ 22 , 23 ]. In fact, models combining ECG and TI have been proposed to predict immediate rhythm transitions during OHCA [ 24 ] and to discriminate rhythms in OHCA [ 25 ], and in a preliminary study, a model combining an ECG and a TI feature showed promising results for the discrimination of faPEA and unPEA on a limited dataset [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

Urteaga

Aramendi

Elola

et al. 2021

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Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

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“…An 8-level SWT decomposition was used with a Daubechies-4 mother wavelet and soft thresholding. Detail coefficients

were used to reconstruct the denoised ECG, which corresponds to an analysis band of

, a typical band for ECG analysis in OHCA [ 23 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The time-varying Fourier coefficients,

and

, were estimated using a Kalman smoother [ 23 ]. The Kalman observation and state vectors are then [ 23 , 34 ]:

…”

Section: Methodsmentioning

confidence: 99%

“…The time-varying Fourier coefficients were assumed to be Gaussian processes with update equations [ 23 , 34 ]:

where

and

is a zero-mean Gaussian process with

the standard deviation. The update equations are thus:

where

and

is the identity matrix of dimension

.…”

Section: Methodsmentioning

confidence: 99%

“…The time-varying Fourier coefficients were assumed to be Gaussian processes with update equations [23,34]:…”

Section: Ti Processing and Icc Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

Urteaga

Aramendi

Elola

et al. 2021

Entropy

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Temporal Trends in End-Tidal Capnography and Outcomes in Out-of-Hospital Cardiac Arrest

Nassal,

Elola,

Aramendi

et al. 2024

JAMA Netw Open

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ImportanceWhile widely measured, the time-varying association between exhaled end-tidal carbon dioxide (EtCO2) and out-of-hospital cardiac arrest (OHCA) outcomes is unclear.ObjectiveTo evaluate temporal associations between EtCO2 and return of spontaneous circulation (ROSC) in the Pragmatic Airway Resuscitation Trial (PART).Design, Setting, and ParticipantsThis study was a secondary analysis of a cluster randomized trial performed at multicenter emergency medical services agencies from the Resuscitation Outcomes Consortium. PART enrolled 3004 adults (aged ≥18 years) with nontraumatic OHCA from December 1, 2015, to November 4, 2017. EtCO2 was available in 1172 cases for this analysis performed in June 2023.InterventionsPART evaluated the effect of laryngeal tube vs endotracheal intubation on 72-hour survival. Emergency medical services agencies collected continuous EtCO2 recordings using standard monitors, and this secondary analysis identified maximal EtCO2 values per ventilation and determined mean EtCO2 in 1-minute epochs using previously validated automated signal processing. All advanced airway cases with greater than 50% interpretable EtCO2 signal were included, and the slope of EtCO2 change over resuscitation was calculated.Main Outcomes and MeasuresThe primary outcome was ROSC determined by prehospital or emergency department palpable pulses. EtCO2 values were compared at discrete time points using Mann-Whitney test, and temporal trends in EtCO2 were compared using Cochran-Armitage test of trend. Multivariable logistic regression was performed, adjusting for Utstein criteria and EtCO2 slope.ResultsAmong 1113 patients included in the study, 694 (62.4%) were male; 285 (25.6%) were Black or African American, 592 (53.2%) were White, and 236 (21.2%) were another race; and the median (IQR) age was 64 (52-75) years. Cardiac arrest was most commonly unwitnessed (n = 579 [52.0%]), nonshockable (n = 941 [84.6%]), and nonpublic (n = 999 [89.8%]). There were 198 patients (17.8%) with ROSC and 915 (82.2%) without ROSC. Median EtCO2 values between ROSC and non-ROSC cases were significantly different at 10 minutes (39.8 [IQR, 27.1-56.4] mm Hg vs 26.1 [IQR, 14.9-39.0] mm Hg; P &lt; .001) and 5 minutes (43.0 [IQR, 28.1-55.8] mm Hg vs 25.0 [IQR, 13.3-37.4] mm Hg; P &lt; .001) prior to end of resuscitation. In ROSC cases, median EtCO2 increased from 30.5 (IQR, 22.4-54.2) mm HG to 43.0 (IQR, 28.1-55.8) mm Hg (P for trend &lt; .001). In non-ROSC cases, EtCO2 declined from 30.8 (IQR, 18.2-43.8) mm Hg to 22.5 (IQR, 12.8-35.4) mm Hg (P for trend &lt; .001). Using adjusted multivariable logistic regression with slope of EtCO2, the temporal change in EtCO2 was associated with ROSC (odds ratio, 1.45 [95% CI, 1.31-1.61]).Conclusions and RelevanceIn this secondary analysis of the PART trial, temporal increases in EtCO2 were associated with increased odds of ROSC. These results suggest value in leveraging continuous waveform capnography during OHCA resuscitation.Trial RegistrationClinicalTrials.gov Identifier: NCT02419573

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Invasive arterial blood pressure delineator for cardiopulmonary resuscitation patients during pauses of chest compressions

Urteaga,

Elola,

Aramendi

et al. 2024

Biomedical Signal Processing and Control

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Multimodal Algorithms for the Classification of Circulation States During Out-of-Hospital Cardiac Arrest

Cited by 17 publications

References 51 publications

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

Temporal Trends in End-Tidal Capnography and Outcomes in Out-of-Hospital Cardiac Arrest

Invasive arterial blood pressure delineator for cardiopulmonary resuscitation patients during pauses of chest compressions

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