Assessment of the impedance cardiogram recorded by an automated external defibrillator during clinical cardiac arrest*

Cromie, N; Allen, John D.; Navarro, Cesar; Turner, Colin; Anderson, John; Adgey, A. A. J.

doi:10.1097/ccm.0b013e3181c02ca1

Cited by 15 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…18,19,21 Combining ECG and ICC features improved the accuracy of the RLS based detector by over 7 points (see supplementary materials). The RLS filter we propose is a more efficient estimator of the ICC component than our previous LMS method, 22 and permits a reliable PEA/PR discrimination using shorter ECG/TI segments of duration below 5 s with minimal computational burden, around 50ms (see supplementary materials).…”

Section: Discussionmentioning

confidence: 99%

“…Cromie et al used a single feature computed from the spectrum of the processed TI as a clinical marker of circulatory collapse in animals 20 and humans. 21 In our previous work, we presented a reliable method to extract the impedance circulation component (ICC) using an adaptive processing scheme, and concluded that morphological features extracted from ICC showed great potential for PEA/PR discrimination. 22 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

et al. 2016

View full text Add to dashboard Cite

Aim To develop and evaluate a method to detect circulation in the presence of organized rhythms (ORs) during resuscitation using signals acquired by defibrillation pads. Methods Segments containing electrocardiogram (ECG) and thoracic impedance (TI) signals free of artifacts were used. The ECG corresponded to ORs classified as pulseless electrical acitivity (PEA) or pulse-generating rhythm (PR). A first dataset containing 1091 segments was split into training and test sets to develop and validate the circulation detector. The method processed ECG and TI to obtain the impedance circulation component (ICC). Morphological features were extracted from ECG and ICC, and combined into a classifier to discriminate between PEA and PR. The performance of the method was evaluated in terms of sensitivity (PR) and specificity (PEA). A second dataset (86 segments from different patients) was used to assess two application of the method: confirmation of arrest by recognizing absence of circulation during ORs and detection of return of spontaneous circulation (ROSC) during resuscitation. In both cases, time to confirmation of arrest/ROSC was determined. Results The method showed a sensitivity/specificity of 92.1%/90.3% and 92.2%/91.9% for training and test sets respectively. The method confirmed cardiac arrest with a specificity of 93.3% with a median delay of 0 s after the first OR annotation. ROSC was detected with a sensitivity of 94.4% with a median delay of 57 s from ROSC onset. Conclusion The method showed good performance, and can be reliably used to distinguish perfusing from non-perfusing ORs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

et al. 2016

View full text Add to dashboard Cite

show abstract

“…A detailed description of the features can be found in [7], [13], [14] and [15]. • TI features (30 from the TI and its $ − % coefficients and its combination with the ECG): the mean power of the TI and the mean cross-power between ECG and TI [16], the first-order derivative of the TI and the peak amplitude of the Fast Fourier Transform (FFT) of the first-order derivative of the TI [17], and the magnitudesquared coherence and the cross power spectral density of ECG and TI.…”

Section: Feature Extractionmentioning

confidence: 99%

Multimodal Biosignal Analysis Algorithm for the Classification of Cardiac Rhythms During Resuscitation

Lasa

Irusta

Eftestøl

et al. 2020

2020 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

Monitoring the heart rhythm during out-of-hospital cardiac arrest (OHCA) is important to improve treatment quality. OHCA rhythms fall into five categories: asystole (AS), pulseless electrical activity (PEA), pulse-generating rhythms (PR), ventricular fibrillation (VF) and ventricular tachycardia (VT). This paper introduces an algorithm to classify these OHCA rhythms using the ECG and the thorax impedance (TI) signals recorded by the defibrillation pads. The dataset consisted of 100 OHCA patient files from which 2833 4-s signal segments were extracted: 423 AS, 912 PE, 689 PR, 643 VF, and 166 VT. The Stationary Wavelet Transform (SWT) was used to obtain 95 features from the ECG and the TI. Random Forest classifiers were used, features were ranked during training using random forest importance, and models with increasing number of features were evaluated. The optimal classifier was obtained combining 50 ECG and TI features, with a median (80% confidence interval) average recall of 86.5%

show abstract

“…That same year, Cromie et al showed in an animal model that the Fast Fourier Transform (FFT) of the impedance cardiogram recorded through defibrillation pads was a potential clinical marker of cardiac arrest [14]. In a subsequent clinical study, they refined the method and evaluated its performance with in-hospital cardiac arrest and nonarrest patients [15]. They concluded that selective filtering of the impedance cardiogram was a powerful hemodynamic sensor of cardiac arrest, with a sensitivity (specificity) of 81% (97%) in the validation set.…”

Section: State Of the Artmentioning

confidence: 99%

Applications of the Transthoracic Impedance Signal during Resuscitation

González-Otero¹,

Gauna²,

Gutiérrez³

et al. 2018

Special Topics in Resuscitation

View full text Add to dashboard Cite

Defibrillators acquire both the ECG and the transthoracic impedance (TI) signal through defibrillation pads. TI represents the resistance of the thorax to current flow, and is measured by defibrillators to check that defibrillation pads are correctly attached to the chest of the patient. Additionally, some defibrillators use the TI measurement to adjust the energy of the defibrillation pulse. Changes in tissue composition due to redistribution and movement of fluids induce fluctuations in the TI. Blood flow during the cardiac cycle generates small fluctuations synchronized to each heartbeat. Respiration (or assisted ventilation) also causes changes in the TI. Additionally, during cardiopulmonary resuscitation (CPR), chest compressions cause a disturbance in the electrode-skin interface, inducing artifacts in the TI signal. These fluctuations may provide useful information regarding CPR quality, length of pauses in chest compressions (no flow time), presence of circulation, etc. This chapter explores the new applications of the transthoracic impedance signal acquired through defibrillation pads during resuscitative attempts.

show abstract

Assessment of the impedance cardiogram recorded by an automated external defibrillator during clinical cardiac arrest*

Abstract: The impedance cardiogram algorithm is a significant marker of circulatory collapse. Automated defibrillators with an integrated impedance cardiogram could improve emergency care by lay persons, enabling rapid and appropriate initiation of external cardiac massage.

Cited by 15 publications

References 17 publications

Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

Multimodal Biosignal Analysis Algorithm for the Classification of Cardiac Rhythms During Resuscitation

Applications of the Transthoracic Impedance Signal during Resuscitation

Contact Info

Product

Resources

About